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Semelhante a Hattrick-Simpers MRS Webinar on AI in Materials
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Hattrick-Simpers MRS Webinar on AI in Materials
- 1. Where Exactly Does One Actually Use AI in Materials Science? Brian DeCost, Zachary Trautt, Martin Green, Gilad Kusne, Jason Hattrick-Simpers NIST Gaithersburg Jason.Hattrick-Simpers@nist.gov @jae3goals Any mention of commercial products within this talk is for information only; it does not imply recommendation or endorsement by NIST.
- 2. Caveats • These slides were presented as a MRS webinar on Machine Learning, AI, and Data-Driven Materials Development and Design • They are intended as a quick-hit overview of the field of where people apply AI in materials science • The works presented are a mixture of my own work and the work of others. • Do not infer from the inclusion of a citation with et. al. that I participated in the work!! • Follow the citation and appropriately attribute the work • This is not an exhaustive review of the field and many great groups were not included, not as a slight on the magnitude of their contributions, but merely for the sake of brevity. • Use with caution
- 3. Context for This Part of the Webinar OEM • High level (non-application) breakthroughs • Revolutionizing society with broadly applicable tool sets Distributor • Adapting OEM breakthroughs to domain specific applications • Revolutionizing communities with disruptive new tools • Educate the larger materials community Consumer • Test driving new tools and use them to discover new materials/science • Make “minor” tool adaptations
- 4. Beware of the AI/ML Hyperbole! • Did Google guess my last name with just my picture???? • Clearly No!! • ML models are often interpolative and correlative but our INTERPRETATIONS can build in causation that doesn’t exist.
- 5. Who Is Your AI More Like? Judea Pearl arXiv:1801.04016v1 Modern AIs do not read, do not understand. They only disguise as if they do. -Noriko Arai “Can a robot pass a university entrance exam?” http://www.notablebiographies.com/Ni-Pe/Pauling-Linus.html https://www.nist.gov/content/nist-and-nobel/nobel-moment-dan-shechtman
- 6. DeCost, et. al., to be submitted
- 7. The Scientific Method Today Archival Data Knowledge Predictive Model Recommend Materials Synthesize Characterize Clean Process Collect Feed Extract Knowledge Collect Feed
- 8. Let’s Talk About Where We Use A.I. Hypothesis Generation Getting and Cleaning the Archival Data Predicting Material of Interest New Data Acquisition Synthesis Optimization Measurement Optimization Data Processing Pre- processing Feature Identification Knowledge Extraction Data Minimization Actionable Information Closing the Loop?
- 9. Hypothesis Generation • Most techniques require data to seed the search • Where is this data coming from • Curated archives (Landolt-Bornstein) • Online databases • Technical literature • Lab Notebooks • LIMS systems • All models need effective descriptors • Chemical • Structural • Do the ML models explicitly contain physics? • Extrapolative versus interpolative
- 10. Chemical Descriptors in a (Random) Forest Ward et al. npj Comp. Mater. (2016), 28. Experimental Data Machine Learning Algorithm Composition-based Representation 𝜎𝑟 < 1.1 Å MG Not MG 𝜇 𝑍 ΔΧ 𝜎 𝑇 𝑚 max 𝑟𝑐𝑜𝑣 𝑥 𝐻, 𝑥 𝐻𝑒, … 2 𝑮𝑭𝑨 = 𝒇(𝒙 𝑯, 𝒙 𝑯𝒆, … ) 24 Million Ternary Alloys 74520 potential MGs 5739 measurements 145 Attributes Random Forest
- 11. JARVIS-ML Websites: https://jarvis.nist.gov https://www.ctcms.nist.gov/jarvisml • In addition to chemical , structural descriptors are also very important • 1557 such chemo-structural descriptors (CFID) • 1557 descriptors for more than 25000 materials is a huge multi- dimensional space • Visualization with manifold-learning techniques, such as t-SNE • Data-science based conventional accuracy metrics (MAE, RMSE etc. ) are not enough from materials-science perspective • Integrating genetic algorithm with formation energy regression model to map phase space and reproduce reality with ML • We trained 12 regression and 2 classification models with gradient boosting decision tree • To enable others use our trained ML models, we made Flask-python app for ML predictions on-fly for any arbitrary material Publications: Phys. Rev. Materials 2, 08380 (2018) Nature:Scientific Data 4, 160125 (2017) Nature:Scientific Reports 7, 5179 (2017) Nature:Scientific Data 5, 180082 (2018) Phys. Rev. B 98, 014107 (2018) arXiv:1804.01024 (2018)
- 12. Bayesian Inference for Materials Design Gubernatis and Lookman, PRM (2018)
- 13. Machine Learning Isn’t Always Enough c/o Citrine Informatics Layer Physical Models with Machine Learning Models Use Engineering Constraints to Limit Search Space Hattrick-Simpers et. al. MSED (2018)
- 14. NREL Data Eco-System for Data Analytics Zakutayev et al. Scientific Data, 2018
- 15. Data Acquisition – Synthesis Optimization • A newly predicted material is not useful if I can’t make it • If I can make it and no one else can then my work isn’t impactful • Options are: • Learn from the literature what has worked before • Ensure that a framework exists for transferal of materials
- 16. Strangest Things – Dark Reactions Friedler, Schrier, Norquist et al. Nature, 2016, 533, 73. Dark Reactions refers to the many ‘failed’ reactions we attempt every day in the lab. Build an interpretable model Predict new materials!!
- 17. Natural Language Processing for Synthesis
- 18. Optimizing Deposition Profiles (II) J. K. Bunn, et. al., I&EC, 55 (2016)
- 19. Playing FAIR with Data To be Findable: • (meta)data are assigned a globally unique and persistent identifier • data are described with rich metadata • metadata clearly and explicitly include the identifier of the data it describes • (meta)data are registered or indexed in a searchable resource To be Accessible: • (meta)data are retrievable by their identifier using a standardized communications protocol – the protocol is open, free, and universally implementable – the protocol allows for an authentication and authorization procedure, where necessary • metadata are accessible, even when the data are no longer available To be Interoperable: • (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation. • (meta)data use vocabularies that follow FAIR principles • (meta)data include qualified references to other (meta)data To be Reusable: • meta(data) are richly described with a plurality of accurate and relevant attributes – (meta)data are released with a clear and accessible data usage license – (meta)data are associated with detailed provenance – (meta)data meet domain-relevant community standards Wilkinson, Mark D., et al. "The FAIR Guiding Principles for scientific data management and stewardship." Scientific data 3 (2016). DOI: 10.1038/sdata.2016.18
- 20. Data Processing – Data Cleaning/Feature Identification • Raw data are noisy, filled with artifacts, and often contain little information per pixel • AI methods can help • Denoise data • Remove spurious artifacts • Perform (preliminary) analysis • Identify Features
- 21. Unsupervised De-Noising of Atomic Images Vasudevan, R.K Applied Physics Letters, 106(9), p.091601, 2015, Belianinov, A., Advanced Structural and Chemical Imaging, 1(1), p.6., 2015
- 22. Data Processing Q (1/A) Signal to Noise in a Pattern High Quality Data Noisier Data Crystallinity Experimental Mishap Beam blocked by sample holder Sparse Optimal Coverage Density Fang Ren, ACS Comb. Sci. - DOI: 10.1021/acscombsci.7b00015
- 23. Feature Identification with MOnsters
- 24. Knowledge Extraction • For HTE groups this has been the single most important step for the past 15 years • 20,000 diffraction spectra across 14 ternaries and various annealing profiles • How does one simplify multi-dimensional data (either via visualization or outright information extraction)? • How can AI be used to optimize experimental measurement time? • How can AI make redundant tasks easier so humans can focus on high-level tasks? • Pretty big rift between supervised and unsupervised data analysis communities
- 25. Rapid IV Acquisition by Bayesian Inference
- 26. DeCost et al accepted for publication in Microscopy & Microanalysis (arXiv preprint arXiv:1805.08693 (2018)) Representation learning Classifier Deep learning: jointly learn features and predictive model DeCost Acta Mater. 2017 DOI: 10.1016/j.actamat.2017.05.014 microstructure --> processing segmentation and quantification Deep Learning for Microstructure Data
- 27. Data Processing – Knowledge Extraction Pipeline Data Acquisition Data Processing and Conditioning Phase Attribution Convert to - and integrate Calculate Similarity Measure Hattrick-Simpers, et. al. APL Materials 4 (2016)
- 28. Supervised Knowledge Extraction J. K. Bunn, et. al., JMR, 30 (2015) Human Analysis AutoPhase Analysis Expert Analysis One-by-one Attribution AutoPhase Attribution Sample Data Selection Training Data Selection General Feature Analysis
- 29. Unsupervised Knowledge Extraction - NMF 2 Angle (Deg.)37 48 Intensity(Arb.Unit.) De-convolved XRD Spectra Phase Diagram Fe Fe40Pd60 Fe40Ga60 C. J. Long, et al. 2009. Rev. Sci. Instrum. 80, 103902 Fe46Pd26Ga28 FCC Fe 22% BCC Fe 41% FCC FePd 31% Phase mixture for each spot as a pie chart! Stanev, et al. 2018. npj Computational Materials
- 30. Why not perform the analysis during the experiment? Kusne, et al. Scientific Reports 4, 6367 (2014)
- 31. Iterative ML – High-throughput Experimental Studies Discover New Metallic Glasses 100x Faster Are “deep” eutectics necessary for growing thin film Metallic Glasses? Ren, et al. Science Advances Vol 4 No. 4 (2018)
- 32. Closing the Loop
- 33. “In the next 5 years, AI-driven, autonomous materials research is going to fundamentally change how we do materials science.” -Jim Warren, Technical Program Director for Materials Genomics, NIST
- 34. Autonomous Research is Already Here AFRL - ARES Hein - UBC NIST - UMD
- 35. Autonomous REsearch Systems (ARES)
- 36. 000 Jason Hein Autonomous robots for experimental chemistry Alán Aspuru-Guzik Artificial intelligence to predict materials performance Ada: An Autonomous Discovery Accelerator Automated Synthesis Automated Characterization Screen computationally (AI + simulation) 36 Curtis Berlinguette Clean energy materials & device fabrication
- 37. Gilad’s Automated Experimentation Platform Fe Fe0.4Pd0.6 Fe0.4Ga0.6 Kusne, et. al., to be submitted
- 38. Active clustering for autonomous XRD phase mapping Think carefully about modeling to remove researcher degrees of freedom DeCost, et. al., to be submitted
- 39. Conclusions • AI & ML are already prevalent in the design of new materials, materials synthesis, data capture/cleaning and knowledge extraction • Neither AI nor ML are a panacea that will replace human intuition and creativity, they are enablers • In some cases an order of magnitude increase in materials exploration/discovery is possible • Maybe a fairer metric of AI’s influence will be on the rate of hypothesis generation and (in)validation • AI needs FAIR data including negative results to be effective • Not part of the solution = consigned to obscurity • Full materials research autonomy (for specific problems) has already been demonstrated
- 40. Demonstrations and Talks by (confirmed speakers): • Theory • Computational Approaches • Experimental Approaches Andrew Millis (Columbia) Antoine Georges (CCQ) Karin Rabe (Rutgers) Bootcamp: Machine Learning for Materials Research & Workshop: Machine Learning Quantum Materials • Dates: July 30 – Aug 3, 2018 • Location: IBBR (Gaithersburg, Maryland) MLMR Introduces researchers from industry, national labs, and academia to machine learning theory and tools for rapid data analysis. https://nanocenter.umd.edu/events/mlmr/ Bootcamp Three days of lectures and hands-on exercises covering a range of data analysis topics from data pre-processing through advanced machine learning analysis techniques. Example topics include: • Identifying important features in complex/high dimensional data • Visualizing high dimensional data to facilitate user analysis. • Identifying the fabrication ‘descriptors’ that best predict variance in functional properties. • Quantifying similarities between materials using complex/high dimensional data The hands-on exercises will demonstrate practical use of machine learning tools on real materials data (scalar values, spectra, micrographs, etc. Sasha Balatsky (LANL) Roger Melko (Waterloo) Shoucheng Zhang (Stanford) Stefano Curtarolo (Duke) Gus Hart (BYU) Ichiro Takeuchi (UMD) Sergei Kalinin (ORNL) Benji Maruyama (AFRL) Jiun-Haw Chu (Univ. Washington) Giuseppe Carleo (Flatiron) Miles Soudenmire (Flatiron)
- 41. Acknowledgements USC Travis Williams SLAC Dr. Apurva Mehta Dr. Fang Ren Dr. Suchismita Northwestern Prof. Wolverton Dr. Logan Ward UNSW Prof. Kevin Laws NIST Dr. Martin Green Dr. Zachary Trautt Dr. Gilad Kusne Dr. Brian DeCost Mr. Ryan Smith (REU) NREL Dr. Andriy Zakutayev CSM Prof. Packard Dr. Schoeppner
- 42. Acknowledgements for Providing Slides of Their Work • Kamal Choudhary and Francesca Tavazza (JARVIS/NIST) • Alex Belianinov (PCA – STEM or STM/ORNL) • Turab Lookman (Piezoelectric Bayesian slide/LANL) • Benji Murayama (AFRL – ARES) • Jason Hein (UBC – ADA) • Rama Vasaduvan (Bayesian Local Imaging/ORNL) • John Perkins (NREL – LIMS/NREL) • Joshua Schrier (Interpretable models /Haverford College) • Brian DeCost (GP phase diagram control and CNN microstructure / NIST with Liz Holm of CMU) • Gilad Kusne (Automated phase mapping & NMF / NIST with Ichiro Takeuchi of UMD) • Apurva Mehta (Automated Synchrotron Data minimization/SLAC – SSRL) • Chris Wolverton and Logan Ware (Magpie predicting metallic glasses/Northwestern)
- 43. Unsupervised Knowledge Extraction Hattrick-Simpers APL Materials 4 (2016)
- 44. Optimizing Deposition Profiles J. K. Bunn, et. al., I&EC, 55 (2016)
Notas do Editor
- Emphasize use of ML in my work but rampant skepticism.
- Maybe back off and say that these are just examples of where we use AI and that it is not exhaustive and that there are few studies that ONLY do 1. often things are linked in a workflow.
- Stocihiometic attributes capture the fraction but not type of elements present. Elemental property attributes of atomic row, mendeev number, atomic weight, total # of unfilled states, etc. with both weighted averages, max, min, range average deviation and mode Calence orbital occupation attributes Ionic compound attributes… ALFOWLIB and MaterialsProject
- "dark reactions" are failed...*recorded* (in lab notebooks), but *not published* (in open literature) The intepretable model gives us *hypotheses* about the underlying chemistry of crystal formation, in terms of physical attributes and reactions conditions. We've actually gone into the laboratory to test them, and 2.5 of the three hypotheses are correct... Not just predict new materials, but predict the recipes to synthesize the new materials. Better than "human experts", even on a problem where the humans think they have a good strategy.
- The idea start with an arbitrary sized image, cut it into a bunch of arbitrary squares, chump them out as linear vectors. A lot of repeatable units in an image, you want to chunk out how the similar images are as a function of position. Window sizing can be automated by using FFT to get a square the size of the primary peak. ABSOLUTELY requires periodicity.
- Here they are actually potting the calculated polarization for each of the spots (often the property of interest). Disorder analysis from switching traces. To investigate the variability of the switching current profiles, we performed K-means analysis with 12 clusters, restricting the data to the switching current traces in the positive voltage window. The result of the K-means analysis is shown in the cluster label map in a, and the mean response of each cluster in b. Note that clusters with <20 members are not plotted on this graph. To obtain a more quantitative estimate on the disorder, a Gaussian fitting procedure was applied to each switching current profile.
- Top: Lots of recent (since 2012) breakthroughs in computer vision credited to deep learning -- interesting applications in analyzing microscopy data as well. Key point: It's not 'automate the technicians and engineers jobs out of existence', it's 'automate the boring stuff so the technicians and engineers can be more efficient, work with better information, and ask more interesting/impactful questions of the microstructure.' Deep learning: build high-level, complex features out of lower-level features. For image data, this is accomplished by stacking many layers of learnable convolution filters that do feature extraction. 2. Deep learning in 6 words: jointly learn features and predictive model CNN cartoon (top): visualization of the activations in a convolutional neural network (CNN) looking at an ultrahigh carbon steel micrograph. The layers of the network track with different types of features Levels 1 & 2 respond to edges, Level 3 to carbide particles and the network, and so on. This particular CNN is designed for image segmentation -- it uses features from all five levels to classify individual pixels according proeutectoid carbide (cyan), ferrite (denuded zone, blue), ferrite with particles (yellow), and widmanstatten carbide (green). Bottom left: Deep learning can be used to relate image data to processing or properties metadata. We should prepare interesting samples, automate the collection of many images, and have ML models flag interesting features for us to review! figure: t-SNE visualization of CNN features for ~1000 ultrahigh carbon steel micrographs. Colormap indicates annealing temperature and relative marker size indicates annealing time for this dataset. (inset: colors show primary microconstituents. purple: network carbide, blue: ferrite w/ particles, yellow: pearlite). Similar annealing condition -> similar microstructure :: similar microstructure -> similar CNN features Bottom right: Deep learning can also be used to automate/augment/scale up the kind of microstructure analyses we can do. e.g. more powerful segmentation models --> broader scope of fast quantification of interesting microstructure features. Automate the image acquisition and you can get good statistics for things that are currently just subjective. For instance, imagine having a CNN automatically produce the denuded zone width distribution. This would be very challenging ORNL group doing atomistic defect analysis and tracking in the TEM Dane Morgan's group identifying and segmenting individual dislocation loops in TEM images
- Mean shift theory…
- Love this slide, but perhaps you can merge it with the next one to show the LOOP?
- If you’re interested in machine learning, we have an annual bootcamp at University of Maryland that teaches a wide variety of these techniques. You’ll learn things like how to identify important features in your data, how to visualize complex or high dimensional data, and how to identify descriptors. Each morning there are lectures and the afternoons are hands on activities applying machine learning to real materials data. And Ichiro Takeuchi, me and some collaborators have also organized an annual bootcamp. At this bootcamp we teach an introduction to machine learning, the most common techniques. Half of each day is also hands on training where you learn how to write code to analyze real data. Some examples of stuff we teach. How to …