Talk given to the 1st multidisciplinary conference of Italian researchers in Czechia.
This is a public engagement talk about computational tools to investigate materials properties.
Computational materials design with high-throughput and machine learning methodsAnubhav Jain
Computational materials design with high-throughput and machine learning methods was presented. The presentation discussed (1) using density functional theory and high-throughput screening to rapidly generate data on many materials, (2) developing data mining approaches like matminer and matbench to extract useful information and connect to machine learning algorithms from the large volumes of data, and (3) concluded with a discussion of using these methods to accelerate materials innovation.
This document discusses semiconductor materials and devices. It begins by explaining electricity and electron bands in atoms. It then discusses the properties and atomic structures of conductors, insulators, and semiconductors. Semiconductors can be made to act as insulators or conductors through doping, which introduces impurity atoms. The document describes how n-type and p-type semiconductors are formed and their current flow. It concludes by explaining how a p-n junction diode is formed at the interface of p-type and n-type semiconductors and its current-voltage characteristics.
(This presentation is in .pptx format, and will display well when embedded improperly, such as on the SlideShare site. Please download at your discretion, and be sure to cite your source)
Review of the Hartree-Fock algorithm for the Self-Consistent Field solution of the electronic Schroedinger equation. This talk also serves to highlight some basic points in Quantum Mechanics and Computational Chemistry.
March 21st, 2012
This document presents research on developing a lead-free and environmentally-friendly substitute for PVDF polymer composites. It investigates using potassium sodium niobate (KNN) ceramic as a filler material in a polyacrylonitrile (PAN) polymer matrix composite. KNN is identified as a promising substitute due to its comparable piezoelectric and dielectric properties to lead-based ceramics, as well as being non-toxic. The document outlines the experimental process of fabricating PAN/KNN composites and evaluating their properties through various characterization techniques. Future work is proposed to further optimize the composite properties by controlling parameters like KNN particle size and volume fraction in the polymer matrix.
Methods available in WIEN2k for the treatment of exchange and correlation ef...ABDERRAHMANE REGGAD
This document summarizes methods available in the WIEN2k software for treating exchange and correlation effects beyond semilocal density functional theory. It discusses the semilocal generalized gradient approximation and meta-GGA functionals, the modified Becke-Johnson potential for improving band gaps, dispersion correction methods, and on-site corrections like DFT+U and hybrid functionals for strongly correlated materials. Input parameters and keywords for selecting these methods in the WIEN2k code are also outlined.
A perovskite solar cell is a type of solar cell which includes a perovskite structured compound, most commonly a hybrid organic-inorganic lead or tin halide-based material, as the light-harvesting active layer.
Density functional theory (DFT) is a computational quantum mechanics method used to investigate the electronic structure of many-body systems like molecules and solids. It functions by using functionals of the electron density rather than the many-body wavefunction. This makes calculations more efficient. DFT was developed based on the Hohenberg-Kohn theorems, which established that all ground state properties are uniquely determined by the electron density alone. This allowed modeling systems using functionals of the density rather than attempting to solve the complicated many-electron Schrodinger equation directly. DFT is now widely used in physics, chemistry, and materials science.
This document provides an overview of density functional theory (DFT). It discusses the history and development of DFT, including the Hohenberg-Kohn and Kohn-Sham theorems. The document outlines the fundamentals of DFT, including how it uses functionals of electron density rather than wavefunctions to simplify solving the many-body Schrodinger equation. It also describes the self-consistent approach in DFT calculations and provides examples of popular DFT software packages.
Computational materials design with high-throughput and machine learning methodsAnubhav Jain
Computational materials design with high-throughput and machine learning methods was presented. The presentation discussed (1) using density functional theory and high-throughput screening to rapidly generate data on many materials, (2) developing data mining approaches like matminer and matbench to extract useful information and connect to machine learning algorithms from the large volumes of data, and (3) concluded with a discussion of using these methods to accelerate materials innovation.
This document discusses semiconductor materials and devices. It begins by explaining electricity and electron bands in atoms. It then discusses the properties and atomic structures of conductors, insulators, and semiconductors. Semiconductors can be made to act as insulators or conductors through doping, which introduces impurity atoms. The document describes how n-type and p-type semiconductors are formed and their current flow. It concludes by explaining how a p-n junction diode is formed at the interface of p-type and n-type semiconductors and its current-voltage characteristics.
(This presentation is in .pptx format, and will display well when embedded improperly, such as on the SlideShare site. Please download at your discretion, and be sure to cite your source)
Review of the Hartree-Fock algorithm for the Self-Consistent Field solution of the electronic Schroedinger equation. This talk also serves to highlight some basic points in Quantum Mechanics and Computational Chemistry.
March 21st, 2012
This document presents research on developing a lead-free and environmentally-friendly substitute for PVDF polymer composites. It investigates using potassium sodium niobate (KNN) ceramic as a filler material in a polyacrylonitrile (PAN) polymer matrix composite. KNN is identified as a promising substitute due to its comparable piezoelectric and dielectric properties to lead-based ceramics, as well as being non-toxic. The document outlines the experimental process of fabricating PAN/KNN composites and evaluating their properties through various characterization techniques. Future work is proposed to further optimize the composite properties by controlling parameters like KNN particle size and volume fraction in the polymer matrix.
Methods available in WIEN2k for the treatment of exchange and correlation ef...ABDERRAHMANE REGGAD
This document summarizes methods available in the WIEN2k software for treating exchange and correlation effects beyond semilocal density functional theory. It discusses the semilocal generalized gradient approximation and meta-GGA functionals, the modified Becke-Johnson potential for improving band gaps, dispersion correction methods, and on-site corrections like DFT+U and hybrid functionals for strongly correlated materials. Input parameters and keywords for selecting these methods in the WIEN2k code are also outlined.
A perovskite solar cell is a type of solar cell which includes a perovskite structured compound, most commonly a hybrid organic-inorganic lead or tin halide-based material, as the light-harvesting active layer.
Density functional theory (DFT) is a computational quantum mechanics method used to investigate the electronic structure of many-body systems like molecules and solids. It functions by using functionals of the electron density rather than the many-body wavefunction. This makes calculations more efficient. DFT was developed based on the Hohenberg-Kohn theorems, which established that all ground state properties are uniquely determined by the electron density alone. This allowed modeling systems using functionals of the density rather than attempting to solve the complicated many-electron Schrodinger equation directly. DFT is now widely used in physics, chemistry, and materials science.
This document provides an overview of density functional theory (DFT). It discusses the history and development of DFT, including the Hohenberg-Kohn and Kohn-Sham theorems. The document outlines the fundamentals of DFT, including how it uses functionals of electron density rather than wavefunctions to simplify solving the many-body Schrodinger equation. It also describes the self-consistent approach in DFT calculations and provides examples of popular DFT software packages.
This presentation is the introduction to Density Functional Theory, an essential computational approach used by Physicist and Quantum Chemist to study Solid State matter.
The document discusses heterojunctions and p-n junctions. It defines a heterojunction as the interface between two dissimilar semiconductors with different band gaps. There are three types of heterojunctions based on band alignment: type I where bands straddle, type II where bands are staggered, and type III where there is a broken gap. A p-n heterojunction diode forms when a p-doped and n-doped semiconductor meet; electrons flow from the higher to lower Fermi level side and holes in the opposite direction.
(If visualization is slow, please try downloading the file.)
Part 2 of a tutorial given in the Brazilian Physical Society meeting, ENFMC. Abstract: Density-functional theory (DFT) was developed 50 years ago, connecting fundamental quantum methods from early days of quantum mechanics to our days of computer-powered science. Today DFT is the most widely used method in electronic structure calculations. It helps moving forward materials sciences from a single atom to nanoclusters and biomolecules, connecting solid-state, quantum chemistry, atomic and molecular physics, biophysics and beyond. In this tutorial, I will try to clarify this pathway under a historical view, presenting the DFT pillars and its building blocks, namely, the Hohenberg-Kohn theorem, the Kohn-Sham scheme, the local density approximation (LDA) and generalized gradient approximation (GGA). I would like to open the black box misconception of the method, and present a more pedagogical and solid perspective on DFT.
This document discusses thermoelectric materials. It provides background on thermoelectricity, which uses temperature differences to generate electricity or provide cooling. Thermoelectric efficiency is determined by a material's thermoelectric figure of merit (ZT), which depends on properties like the Seebeck coefficient, electrical conductivity, and thermal conductivity. The document notes challenges in developing organic thermoelectric materials and achieving high ZT values in both n-type and p-type materials. It proposes plans to create hybrid and composite thermoelectric materials for applications like refrigeration.
The document provides an introduction to the finite element method (FEM). It discusses how FEM can be used to obtain approximate solutions to boundary value problems in engineering. It outlines the general steps involved, including preprocessing (defining the model), solution/processing (computing unknown values), and postprocessing (analyzing results). Examples of FEM applications include structural analysis, fluid flow, heat transfer, and more. The key aspects of FEM include discretizing the domain into simple elements, choosing shape functions to approximate variations within each element, and assembling the element equations into a global system of equations to solve.
The document summarizes the thermoelectric effect, which is the direct conversion of temperature differences into electric voltage and vice versa. It was discovered in the 1820s by Thomas Seebeck and Jean Peltier. The effect occurs due to charge carrier diffusion and phonon drag in materials. Thermoelectric modules use pairs of P-type and N-type semiconductors to generate electricity from heat gradients or create cooling by using electricity. Some applications of thermoelectric generators include cooling computers, drink coolers, recharging devices, and powering space probes.
Some "accumulated wisdom" from several years of using the Vienna ab initio Simulation Package (VASP) code for computational modelling. Includes tips on convergence and parallelisation.
BoltzTraP is a software tool that uses linearized Boltzmann transport theory to calculate electronic transport properties from first-principles band structures. It can calculate properties like electrical conductivity, Seebeck coefficient, and electronic thermal conductivity. The document discusses applications of BoltzTraP to analyze transport properties of metals and thermoelectric materials. Key applications highlighted include analyzing anisotropy, resistivity temperature dependence, and optimizing the electronic structure of materials for high thermoelectric performance.
Organic electronics deals with conductive polymers and small molecules for carbon-based electronics. It includes laminar electronics like transparent and paper-based devices. Conductive organic materials can be polymers or small molecules and exhibit electrical conductivity between insulators and metals. Organic light-emitting diodes (OLEDs) have an organic semiconductor layer between electrodes that emits light in response to electric current. Organic electronics have advantages like lower cost fabrication compared to inorganic materials.
This document provides an overview of fuel cell technology, with a focus on high-temperature proton exchange membrane (HT-PEM) fuel cells. It introduces fuel cells as electrochemical devices that convert chemical energy directly into electricity. The history, advantages, types, applications and achievements/targets of fuel cell technology are summarized. HT-PEM fuel cells employ a thin solid polymer electrolyte and have potential, but material degradation remains a challenge that needs to be addressed for commercial viability. The document concludes that progress is being made to meet cost and durability targets needed for fuel cells to be competitive.
This Presentation "Energy band theory of solids" will help you to Clarify your doubts and Enrich your Knowledge. Kindly use this presentation as a Reference and utilize this presentation
This document provides an overview of electronic band structure and Bloch theory in solid state physics. It discusses the differences between the Sommerfeld and Bloch approaches to modeling electron behavior in periodic solids. Key points include:
- Bloch's treatment models electrons using band indices and crystal momentum rather than just momentum.
- Bloch states follow classical dynamics on average, with crystal momentum replacing ordinary momentum.
- The band structure determines allowed electron energies and velocities for a given crystal momentum.
- Bloch's theory accounts for periodic potentials within the crystal lattice, allowing for band gaps and a more accurate description of electron behavior in solids.
Heterojunctions are formed by combining two dissimilar semiconducting materials, such as aluminum-arsenic or gallium-phosphorus, which results in unequal band gaps compared to homojunctions. The document defines heterojunction band diagrams and discusses the electric field and electric potential that arise at the junction between dissimilar materials.
Under the supervision of Prof. Paulson Samuel, Raj Kapur Kumar presents research on green hydrogen generation through water electrolysis. The document discusses the types of hydrogen production, the benefits of green hydrogen, challenges in producing it affordably at scale, and modeling a proton exchange membrane electrolyzer. MATLAB simulations examine the electrolyzer's electrical characteristics and hydrogen production rates under varying conditions. The results further green hydrogen's viability as a renewable energy storage medium.
A feasible way towards safer, better-performing batteries?
Conventional Li-ion battery technologies, based on flammable liquid electrolytes, are continuously improving. However, faster progress towards greater safety, higher performance, and better cost reduction is desired. A next-generation battery technology like solid-state battery, which uses solid electrodes and solid electrolytes, could potentially satisfy these objectives.
More information on : https://www.i-micronews.com/batteries-energy-mgmt/product/solid-state-battery.html
This document describes a method for synthesizing nitrogen-doped porous graphitic carbon materials for use as electrodes in supercapacitors. The method involves polymerizing aniline in the presence of ferric chloride, followed by high-temperature treatment and chemical activation to produce a 3D porous structure. The novel carbon material demonstrated a high specific capacitance of 300 F/g as an electrode in aqueous electrolyte. Further characterization showed the material had a surface area of over 1200 m2/g after activation.
In this talk I will discuss different approximations in DFT: pseduo-potentials, exchange correlation functions.
The presentation can be downloaded here:
http://www.attaccalite.com/wp-content/uploads/2022/03/dft_approximations.odp
The document discusses the potential of a new solar cell material called perovskite. Perovskite solar cells can be produced at low cost using simple solution-based methods. Research suggests perovskite solar cells could eventually reach efficiencies over 20%, comparable to existing thin-film technologies. Perovskite absorbs light strongly and transports electrical charges well. Its properties may allow solar cells that convert over half of sunlight to electricity. Researchers are working to improve efficiency and stability through material modifications. Perovskite solar cells could eventually lead to much lower cost solar power compared to current technologies.
This document summarizes a presentation about using machine learning for computational chemistry. It discusses how machine learning and computational chemistry are deeply connected, with machine learning serving as a new tool for computational chemistry. The presentation outlines how machine learning can help accelerate drug discovery and materials design for applications in health and sustainability by generating new molecules and predicting chemical reactions.
Dynamic Homogenisation of randomly irregular viscoelastic metamaterialsUniversity of Glasgow
An analytical framework is developed for investigating the effect of viscoelasticity on irregular hexagonal lattices. At room temperature, many polymers are found to be near their glass temperature. Elastic moduli of honeycombs made of such materials are not constant, but changes in the time or frequency domain. Thus consideration of viscoelastic properties is essential for such honeycombs. Irregularity in lattice structures being inevitable from a practical point of view, analysis of the compound effect considering both irregularity and viscoelasticity is crucial for such structural forms. On the basis of a mechanics-based bottom-up approach, computationally efficient closed-form formulae are derived in the frequency domain. The spatially correlated structural and material attributes are obtained based on Karhunen-Lo\`{e}ve expansion, which is integrated with the developed analytical approach to quantify the viscoelastic effect for irregular lattices. Consideration of such spatially correlated behaviour can simulate the practical stochastic system more closely. Two Young's moduli and shear modulus are found to be dependent on the viscoelastic parameters, while the two in-plane Poisson's ratios are found to be independent of viscoelastic parameters. Results are presented in both deterministic and stochastic regime, wherein it is observed that the elastic moduli are significantly amplified in the frequency domain. The response bounds are quantified considering two different forms of irregularity, randomly inhomogeneous irregularity and randomly homogeneous irregularity. The computationally efficient analytical approach presented in this study can be quite attractive for practical purposes to analyse and design lattices with predominantly viscoelastic behaviour along with consideration of structural and material irregularity.
This presentation is the introduction to Density Functional Theory, an essential computational approach used by Physicist and Quantum Chemist to study Solid State matter.
The document discusses heterojunctions and p-n junctions. It defines a heterojunction as the interface between two dissimilar semiconductors with different band gaps. There are three types of heterojunctions based on band alignment: type I where bands straddle, type II where bands are staggered, and type III where there is a broken gap. A p-n heterojunction diode forms when a p-doped and n-doped semiconductor meet; electrons flow from the higher to lower Fermi level side and holes in the opposite direction.
(If visualization is slow, please try downloading the file.)
Part 2 of a tutorial given in the Brazilian Physical Society meeting, ENFMC. Abstract: Density-functional theory (DFT) was developed 50 years ago, connecting fundamental quantum methods from early days of quantum mechanics to our days of computer-powered science. Today DFT is the most widely used method in electronic structure calculations. It helps moving forward materials sciences from a single atom to nanoclusters and biomolecules, connecting solid-state, quantum chemistry, atomic and molecular physics, biophysics and beyond. In this tutorial, I will try to clarify this pathway under a historical view, presenting the DFT pillars and its building blocks, namely, the Hohenberg-Kohn theorem, the Kohn-Sham scheme, the local density approximation (LDA) and generalized gradient approximation (GGA). I would like to open the black box misconception of the method, and present a more pedagogical and solid perspective on DFT.
This document discusses thermoelectric materials. It provides background on thermoelectricity, which uses temperature differences to generate electricity or provide cooling. Thermoelectric efficiency is determined by a material's thermoelectric figure of merit (ZT), which depends on properties like the Seebeck coefficient, electrical conductivity, and thermal conductivity. The document notes challenges in developing organic thermoelectric materials and achieving high ZT values in both n-type and p-type materials. It proposes plans to create hybrid and composite thermoelectric materials for applications like refrigeration.
The document provides an introduction to the finite element method (FEM). It discusses how FEM can be used to obtain approximate solutions to boundary value problems in engineering. It outlines the general steps involved, including preprocessing (defining the model), solution/processing (computing unknown values), and postprocessing (analyzing results). Examples of FEM applications include structural analysis, fluid flow, heat transfer, and more. The key aspects of FEM include discretizing the domain into simple elements, choosing shape functions to approximate variations within each element, and assembling the element equations into a global system of equations to solve.
The document summarizes the thermoelectric effect, which is the direct conversion of temperature differences into electric voltage and vice versa. It was discovered in the 1820s by Thomas Seebeck and Jean Peltier. The effect occurs due to charge carrier diffusion and phonon drag in materials. Thermoelectric modules use pairs of P-type and N-type semiconductors to generate electricity from heat gradients or create cooling by using electricity. Some applications of thermoelectric generators include cooling computers, drink coolers, recharging devices, and powering space probes.
Some "accumulated wisdom" from several years of using the Vienna ab initio Simulation Package (VASP) code for computational modelling. Includes tips on convergence and parallelisation.
BoltzTraP is a software tool that uses linearized Boltzmann transport theory to calculate electronic transport properties from first-principles band structures. It can calculate properties like electrical conductivity, Seebeck coefficient, and electronic thermal conductivity. The document discusses applications of BoltzTraP to analyze transport properties of metals and thermoelectric materials. Key applications highlighted include analyzing anisotropy, resistivity temperature dependence, and optimizing the electronic structure of materials for high thermoelectric performance.
Organic electronics deals with conductive polymers and small molecules for carbon-based electronics. It includes laminar electronics like transparent and paper-based devices. Conductive organic materials can be polymers or small molecules and exhibit electrical conductivity between insulators and metals. Organic light-emitting diodes (OLEDs) have an organic semiconductor layer between electrodes that emits light in response to electric current. Organic electronics have advantages like lower cost fabrication compared to inorganic materials.
This document provides an overview of fuel cell technology, with a focus on high-temperature proton exchange membrane (HT-PEM) fuel cells. It introduces fuel cells as electrochemical devices that convert chemical energy directly into electricity. The history, advantages, types, applications and achievements/targets of fuel cell technology are summarized. HT-PEM fuel cells employ a thin solid polymer electrolyte and have potential, but material degradation remains a challenge that needs to be addressed for commercial viability. The document concludes that progress is being made to meet cost and durability targets needed for fuel cells to be competitive.
This Presentation "Energy band theory of solids" will help you to Clarify your doubts and Enrich your Knowledge. Kindly use this presentation as a Reference and utilize this presentation
This document provides an overview of electronic band structure and Bloch theory in solid state physics. It discusses the differences between the Sommerfeld and Bloch approaches to modeling electron behavior in periodic solids. Key points include:
- Bloch's treatment models electrons using band indices and crystal momentum rather than just momentum.
- Bloch states follow classical dynamics on average, with crystal momentum replacing ordinary momentum.
- The band structure determines allowed electron energies and velocities for a given crystal momentum.
- Bloch's theory accounts for periodic potentials within the crystal lattice, allowing for band gaps and a more accurate description of electron behavior in solids.
Heterojunctions are formed by combining two dissimilar semiconducting materials, such as aluminum-arsenic or gallium-phosphorus, which results in unequal band gaps compared to homojunctions. The document defines heterojunction band diagrams and discusses the electric field and electric potential that arise at the junction between dissimilar materials.
Under the supervision of Prof. Paulson Samuel, Raj Kapur Kumar presents research on green hydrogen generation through water electrolysis. The document discusses the types of hydrogen production, the benefits of green hydrogen, challenges in producing it affordably at scale, and modeling a proton exchange membrane electrolyzer. MATLAB simulations examine the electrolyzer's electrical characteristics and hydrogen production rates under varying conditions. The results further green hydrogen's viability as a renewable energy storage medium.
A feasible way towards safer, better-performing batteries?
Conventional Li-ion battery technologies, based on flammable liquid electrolytes, are continuously improving. However, faster progress towards greater safety, higher performance, and better cost reduction is desired. A next-generation battery technology like solid-state battery, which uses solid electrodes and solid electrolytes, could potentially satisfy these objectives.
More information on : https://www.i-micronews.com/batteries-energy-mgmt/product/solid-state-battery.html
This document describes a method for synthesizing nitrogen-doped porous graphitic carbon materials for use as electrodes in supercapacitors. The method involves polymerizing aniline in the presence of ferric chloride, followed by high-temperature treatment and chemical activation to produce a 3D porous structure. The novel carbon material demonstrated a high specific capacitance of 300 F/g as an electrode in aqueous electrolyte. Further characterization showed the material had a surface area of over 1200 m2/g after activation.
In this talk I will discuss different approximations in DFT: pseduo-potentials, exchange correlation functions.
The presentation can be downloaded here:
http://www.attaccalite.com/wp-content/uploads/2022/03/dft_approximations.odp
The document discusses the potential of a new solar cell material called perovskite. Perovskite solar cells can be produced at low cost using simple solution-based methods. Research suggests perovskite solar cells could eventually reach efficiencies over 20%, comparable to existing thin-film technologies. Perovskite absorbs light strongly and transports electrical charges well. Its properties may allow solar cells that convert over half of sunlight to electricity. Researchers are working to improve efficiency and stability through material modifications. Perovskite solar cells could eventually lead to much lower cost solar power compared to current technologies.
This document summarizes a presentation about using machine learning for computational chemistry. It discusses how machine learning and computational chemistry are deeply connected, with machine learning serving as a new tool for computational chemistry. The presentation outlines how machine learning can help accelerate drug discovery and materials design for applications in health and sustainability by generating new molecules and predicting chemical reactions.
Dynamic Homogenisation of randomly irregular viscoelastic metamaterialsUniversity of Glasgow
An analytical framework is developed for investigating the effect of viscoelasticity on irregular hexagonal lattices. At room temperature, many polymers are found to be near their glass temperature. Elastic moduli of honeycombs made of such materials are not constant, but changes in the time or frequency domain. Thus consideration of viscoelastic properties is essential for such honeycombs. Irregularity in lattice structures being inevitable from a practical point of view, analysis of the compound effect considering both irregularity and viscoelasticity is crucial for such structural forms. On the basis of a mechanics-based bottom-up approach, computationally efficient closed-form formulae are derived in the frequency domain. The spatially correlated structural and material attributes are obtained based on Karhunen-Lo\`{e}ve expansion, which is integrated with the developed analytical approach to quantify the viscoelastic effect for irregular lattices. Consideration of such spatially correlated behaviour can simulate the practical stochastic system more closely. Two Young's moduli and shear modulus are found to be dependent on the viscoelastic parameters, while the two in-plane Poisson's ratios are found to be independent of viscoelastic parameters. Results are presented in both deterministic and stochastic regime, wherein it is observed that the elastic moduli are significantly amplified in the frequency domain. The response bounds are quantified considering two different forms of irregularity, randomly inhomogeneous irregularity and randomly homogeneous irregularity. The computationally efficient analytical approach presented in this study can be quite attractive for practical purposes to analyse and design lattices with predominantly viscoelastic behaviour along with consideration of structural and material irregularity.
Discovering advanced materials for energy applications (with high-throughput ...Anubhav Jain
This document summarizes a talk on discovering advanced materials for energy applications using high-throughput computing and mining the scientific literature. It discusses how materials discovery and optimization typically take decades due to the vast number of possible atomic configurations. Density functional theory provides a way to computationally screen millions of potential materials by automating calculations on supercomputers. Examples are given of new battery cathode and thermoelectric materials that have been discovered through high-throughput density functional theory calculations and later experimentally confirmed.
History of nanoscience, Nanomaterial Dimensions, why small is good, surface area to volume ratio, top down and bottom up technique and physical and chemical synthesis technique and future application.
History and Applications of Finite Element Analysis
Theory of Elasticity
Finite Element Equation of Bar element
Finite Element Equation of Truss element
Finite Element Equation of Beam element
Tutorial related to
Bar element
Beam element
Finite element simulation using ANSYS 15.0
Bar element
Truss element
Beam element
Machine Learning in Materials Science and Chemistry, USPTO, Nathan C. FreyNathan Frey, PhD
Machine learning and artificial intelligence have transformed our online experience, and for an increasing number of individuals, these fields are fundamentally changing the way we work. In this talk, I will discuss how machine learning is used in the physical sciences, particularly materials science and chemistry, and what transformative impacts we have seen or might expect to see in the future. This discussion will focus on the unique challenges (and opportunities) faced by materials and chemistry researchers applying machine learning in their work. I will present a brief introduction to machine learning for physical scientists and give examples related to synthesis, property prediction and engineering, and artificial intelligence that “reads” research articles. These examples will introduce some of the most prevalent and useful open-source software tools that drive modern machine learning applications. Two significant themes will be emphasized throughout: the careful evaluation of machine learning results and the central importance of data quality and quantity. Finally, I will provide some mundane, “human learned” speculation about the future of machine learning in physical science and recommended resources for further study.
This document summarizes the work of Working Group II on Probabilistic Numerics from the SAMSI QMC Transition Workshop. The working group aims to develop probabilistic numerical methods that provide a richer probabilistic quantification of numerical error in outputs, allowing for better statistical inference. Members of the working group have published several papers on topics like Bayesian probabilistic numerical methods for solving differential equations and performing integral approximations, and applying these methods to problems in mathematical epidemiology and industrial process monitoring. The group has also organized workshops and reading groups to discuss the development of probabilistic numerical methods.
Available methods for predicting materials synthesizability using computation...Anubhav Jain
This document summarizes a talk about computational and machine learning approaches for predicting materials synthesizability. It discusses how machine learning algorithms are generating millions of potential stable compound predictions, far more than can be experimentally tested. It also examines ways to better prioritize candidate materials for synthesis, such as by assessing their likelihood of dynamical stability and calculating their finite-temperature Gibbs free energies more efficiently using machine-learned interatomic force constants. Finally, it describes efforts to integrate literature knowledge using natural language processing to further guide experimental exploration and reduce the number of experiments needed to synthesize predicted materials.
Introduction to computation material science.
The presentation source can be downloaded here:
http://www.attaccalite.com/wp-content/uploads/2022/11/CompMatScience.odp
Accelerating Science with Generative Adversarial NetworksMichela Paganini
Presentation at NERSC Data Day 2017 at Lawrence Berkeley National Laboratory on the potential of Generative Adversarial Networks to speed up scientific simulation and empower scientists and researchers.
This thesis examines the ab-initio calculation of spin-dependent transport properties in disordered materials. It presents a theoretical method to systematically calculate macroscopic transport quantities from first principles. The thesis considers vibrational excitations within perturbation theory and includes anharmonic terms using the quasi-harmonic approximation. It also presents a theory to introduce temperature corrections to the calculation of magnetic exchange interactions. Results are presented for a set of materials of interest for spintronics and spin-caloritronics applications.
Asynchronous futures: Digital technologies at the time of the AnthropoceneAlexandre Monnin
1) The document discusses the future of digital technologies and their relationship to physical resources and sustainability in the context of the Anthropocene.
2) It notes that while Moore's Law has led to exponential growth in computing power, this has come at tremendous resource and energy costs that may not be sustainable long-term as technologies approach physical limits.
3) The document questions where research may lead in the future and considers more sustainable alternatives like biomimetics, new architectures, and alternative materials if current trajectories prove unsustainable in light of physical and resource constraints.
The interplay between data-driven and theory-driven methods for chemical scie...Ichigaku Takigawa
The 1st International Symposium on Human InformatiX
X-Dimensional Human Informatics and Biology
ATR, Kyoto, February 27-28, 2020
https://human-informatix.atr.jp
The Algorithms of Life - Scientific Computing for Systems Biologyinside-BigData.com
In this deck from ISC 2019, Ivo Sbalzarini from TU Dresden presents: The Algorithms of Life - Scientific Computing for Systems Biology. In his talk, Sbalzarini mainly discussed the rapidly growing importance and influence in the life sciences for scientific high-performance computing.
"Scientific high-performance computing is of rapidly growing importance and influence in the life sciences. Thanks to the increasing knowledge about the molecular foundations of life, recent advances in biomedical data science, and the availability of predictive biophysical theories that can be numerically simulated, mechanistic understanding of the emergence of life comes within reach. Computing is playing a pivotal and catalytic role in this scientific revolution, both as a tool of investigation and hypothesis testing, but also as a school of thought and systems model. This is because a developing tissue, embryo, or organ can itself be seen as a massively parallel distributed computing system that collectively self-organizes to bring about behavior we call life. In any multicellular organism, every cell constantly takes decisions about growth, division, and migration based on local information, with cells communicating with each other via chemical, mechanical, and electrical signals across length scales from nanometers to meters. Each cell can therefore be understood as a mechano-chemical processing element in a complexly interconnected million- or billion-core computing system. Mechanistically understanding and reprogramming this system is a grand challenge. While the “hardware” (proteins, lipids, etc.) and the “source code” (genetic code) are increasingly known, we known virtually nothing about the algorithms that this code implements on this hardware. Our vision is to contribute to this challenge by developing computational methods and software systems for high-performance data analysis, inference, and numerical simulation of computer models of biological tissues, incorporating the known biochemistry and biophysics in 3D-space and time, in order to understand biological processes on an algorithmic basis. This ranges from real-time approaches to biomedical image analysis, to novel simulation languages for parallel high-performance computing, to virtual reality and machine learning for 3D microscopy and numerical simulations of coupled biochemical-biomechanical models. The cooperative, interdisciplinary effort to develop and advance our understanding of life using computational approaches not only places high-performance computing center stage, but also provides stimulating impulses for the future development of this field."
Watch the video: https://wp.me/p3RLHQ-kBB
Learn more: https://www.isc-hpc.com/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Moshe Talesnik, Towards a ubiquitous good NST education Brussels, Belgium
The document discusses nanotechnology education programs for secondary schools. It analyzes 12 exemplary programs from different European countries based on parameters like whether they are compulsory or optional courses, integrate nanotechnology into other subjects or are standalone, involve virtual or in-person teaching, industry/academic partnerships, and hands-on versus theoretical focus. The analysis finds that while programs vary in their approaches, most involve independent nanotechnology subjects, hands-on teaching with industry/academic support, and aim to engage students and the broader community. The document concludes that to fully realize the potential of nanotechnology education, schools need programs that are both comprehensive and innovative.
The Face of Nanomaterials: Insightful Classification Using Deep Learning - An...PyData
Artificial intelligence is emerging as a new paradigm in materials science. This talk describes how physical intuition and (insightful) machine learning can solve the complicated task of structure recognition in materials at the nanoscale.
Combining density functional theory calculations, supercomputing, and data-dr...Anubhav Jain
The document summarizes how computational materials science using density functional theory (DFT) calculations, supercomputing, and data-driven methods can help design new materials faster than traditional experimental approaches. It describes how high-throughput DFT calculations are run on supercomputers to screen large numbers of potential materials. The results are compiled in open databases like the Materials Project to be shared and reused by researchers. While computational limitations remain, combining computation and data is helping accelerate the discovery of new materials with improved properties for applications like batteries, thermoelectrics, and carbon capture.
Nature-inspired Solutions for Engineering: A Transformative Methodology for I...KTN
Nature- Inspired Engineering (NIE) is the application of fundamental scientific mechanisms, underpinning desirable properties observed in nature (e.g., resilience, scalability, efficiency), to inform the design of advanced technological solutions. As illustrated by the many applications, from energy technology, catalysis and reactor engineering, to functional materials for the built environment, electronic or optical devices, biomedical and healthcare engineering, NIE has the opportunity to inform transformative solutions to tackle some of our most pressing challenges, as well as to be a pathway to innovation.
The webcast recording is now available. Click here to watch it: https://www.youtube.com/watch?v=gPyTb_-qhgo
Find out more about the Nature Inspired Solutions special interest group at https://ktn-uk.co.uk/interests/nature-inspired-solutions
Join the Nature Inspired Solutions LinkedIn group at https://www.linkedin.com/groups/13701855/
Semelhante a Computational methods applied to materials modeling (20)
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
EWOCS-I: The catalog of X-ray sources in Westerlund 1 from the Extended Weste...Sérgio Sacani
Context. With a mass exceeding several 104 M⊙ and a rich and dense population of massive stars, supermassive young star clusters
represent the most massive star-forming environment that is dominated by the feedback from massive stars and gravitational interactions
among stars.
Aims. In this paper we present the Extended Westerlund 1 and 2 Open Clusters Survey (EWOCS) project, which aims to investigate
the influence of the starburst environment on the formation of stars and planets, and on the evolution of both low and high mass stars.
The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
Methods. The project is based primarily on recent observations conducted with the Chandra and JWST observatories. Specifically,
the Chandra survey of Westerlund 1 consists of 36 new ACIS-I observations, nearly co-pointed, for a total exposure time of 1 Msec.
Additionally, we included 8 archival Chandra/ACIS-S observations. This paper presents the resulting catalog of X-ray sources within
and around Westerlund 1. Sources were detected by combining various existing methods, and photon extraction and source validation
were carried out using the ACIS-Extract software.
Results. The EWOCS X-ray catalog comprises 5963 validated sources out of the 9420 initially provided to ACIS-Extract, reaching a
photon flux threshold of approximately 2 × 10−8 photons cm−2
s
−1
. The X-ray sources exhibit a highly concentrated spatial distribution,
with 1075 sources located within the central 1 arcmin. We have successfully detected X-ray emissions from 126 out of the 166 known
massive stars of the cluster, and we have collected over 71 000 photons from the magnetar CXO J164710.20-455217.
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.
Computational methods applied to materials modeling
1. Computational methods applied to materials
modeling
Dr. Federico Brivio - Federico.Brivio@natur.cuni.cz
1st
multidisciplinary conference of Italian researchers in Czechia
- June 19, 2019
Raffaello Sanzio - La scuola di Atene
6. 5
Everyone’s Materials Science
Material Science(tists) is an extreme schizophrenic field that aspire to
do everything!
Commercial products
Fundamental development
Materials itself
New applications
|
7. 6
Everyone’s Materials Science
Material Science(tists) is an extreme schizophrenic field that aspire to
do everything!
Commercial products
Fundamental development
Materials itself
New applications
|
8. 7
Material Simulation
Computers are nowadays at the base of most research!
Solve theoretical equation to
predict Material Properties
(often) Cheaper, safer,
cleaner, faster, ...
|
10. 9
Material Design
Stage I (Past):
Reproduction of specific cases
Few "home-brew" software
Stage II (Today):
Prediction of Properties of materials
Accessible/commercial software
Stage III (Future):
Prediction of Materials with specific
properties
Network, cloud, data-mining
REAL material design
|
11. 10
Material Design
Stage I (Past):
Reproduction of specific cases
Few "home-brew" software
Stage II (Today):
Prediction of Properties of materials
Accessible/commercial software
Stage III (Future):
Prediction of Materials with specific
properties
Network, cloud, data-mining
REAL material design
|
14. 13
Quantum Mechanic
Most of the properties of Materials depends on electrons, i.e. their
energy.
Instead of small little balls we need to consider waves(functions), this
is the equation:
Each electron
Electrons are waves
Electrons interact (?!)
we have a global final wavefunction!
|
15. 14
Quantum Mechanic
Most of the properties of Materials depends on electrons, i.e. their
energy.
Instead of small little balls we need to consider waves(functions), this
is the equation:
Each electron
Electrons are waves
Electrons interact (?!)
we have a global final wavefunction!
|
16. 15
Wavefunctions are toooo-large
The previous equation is VERY DIFFICULT!
Material with N
electron
3N variable (xyz)
Electron has also
a spin! 6N
variables!
we need to find a
final
wavefunction!
|
17. 16
Physicist are lazy! - DFT is born
The multivariable problem is substituted by analyzing a mean case.
The (charge) electron Density n!
Φspin,N(x, y, z) → n(x, y, z)
Study large system (today 100s atoms)
Implementation of different models with the same basics!
Check with experimental DATA!
|
30. 29
Acknowledgement
Thank you for your attention! I also want to thanks:
- Prof. Nachtigall and the whole research group
- EU - European structural and investing funds and the MSMT
This Presentation is powered by LATEX- Beamer Class
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32. 31
Images
Images sources if not specified
Slide 5 - Modified from xkcd.com
Slide 6 - Taken from: https:
//www.azom.com/article.aspx?ArticleID=15337
Slide 8 - Taken from: www.top500.org
Slide 10 - Pokemon are a trademark of Nintendo
Slide 12 - Modified from:
http://www.mm.ethz.ch/research_multiscale.html
Slide 13 - IBM Almaden Research Center
Slide 15 - Taken from: https://docplayer.ru/
57424226-Nauchnaya-vizualizaciya-v-fizike-kondensirov
html
Slide 24 - Elliott Wave International
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