UCSD NANO 266 Quantum Mechanical Modelling of Materials and Nanostructures is a graduate class that provides students with a highly practical introduction to the application of first principles quantum mechanical simulations to model, understand and predict the properties of materials and nano-structures. The syllabus includes: a brief introduction to quantum mechanics and the Hartree-Fock and density functional theory (DFT) formulations; practical simulation considerations such as convergence, selection of the appropriate functional and parameters; interpretation of the results from simulations, including the limits of accuracy of each method. Several lab sessions provide students with hands-on experience in the conduct of simulations. A key aspect of the course is in the use of programming to facilitate calculations and analysis.

1. High-throughput
Computational Materials
Design
Shyue Ping Ong

2. Eψ(r) = −
h 2
2m
∇2
ψ(r)+V(r)ψ(r)
Material Properties
First principles materials design
Basic laws of Physics
Density functional theory
(DFT) approximation

3. HT materials design is today a reality
Quantum
Espresso
Gaussian
VASP NwChem
Moore’s Law

4. Important properties for a Li-ion battery
cathode (and how to calculate them)
High
Voltage
< 4.5V
High
Capacity
High Li+
diffusivity
Good
Stability
Thermal
Safety
High energy density
(Voltage x Capacity)
Good cyclability
and power
Material must be
synthesizable
Charged cathode
does not evolve O2
easily
Li2
O
Fe2
O3
P2
O5
LiFeO2
Li
3
PO
4
Li5
FeO4
LiPO
3
Fe2
P4
O12
Fe(PO3
)3
Fe
2
P
2
O
7
FeP4
O11Li4
P2
O7
Fe3
(PO4
)2
LiFePO4
Capacity =
No. of Li transferred
Weight or vol.
0 0.2 0.4 0.6 0.8 1
0
50
100
150
200
250
Diffusion coordinate
Energy(meV)
LCO
NCO
NaCoO2
LiCoO2
If we can calculate relevant
properties for one material,
why not do it for all known
materials?
Voltage = −
E(LiCoO2 )− E(Li1−xCoO2 )− xE(Li)
xFe

5. High-throughput
materials design
framework
Known
compounds
New
compounds
permutation strategy
Database
Initial screening
(non-computational)
Computational
Screening
Candidate materials
Property
computation
Data mining
Discussion
compound flow
Heuristic
Information
knowledge flow
ICSD
Experimental evaluation
A. Jain, G. Hautier, C. Moore, S. P. Ong, C. Fischer, T. Mueller, K. Persson, G. Ceder. Computational Materials
Science, 2011, 50(8), 2295–2310.

6. Range of today’s
known materials
High-throughput screening of voltage and capacity
High voltage destroys electrolyte and is
associated with lack of safety.
High capacity
tends to be
associated
with instability
of structure
Prioritize compounds:
i) Stability
ii) Energy density,
iii) Thermal safety, …

7. Data-mined design map for the
phosphate chemistry
G. Hautier, A. Jain, S. P. Ong, B. Kang, C. Moore, R. Doe, G. Ceder. Chem. Mater., 2011, 23(15), 3495-3508.
Only 3 single redox
couples have the right
average voltage and
capacity to be
commercially
competitive!

8. Discovery – and confirmation – of
completely new classes for Li-ion cathodes
Chemistry Novelty Potential
energy density
improv. over
LiFePO4
Percent of
capacity already
achieved in the
lab
LiMnBO3 Compound known
(new electrochem.)
50% greater ~45%
Li9V3(P2O7)3(PO4)2 New
(never reported)
20% greater ~60%
Li3M(PO4)(CO3)
M=Fe, Mn, Co, ...
New
(never reported)
40% greater ~45%
G. Hautier, A. Jain, H. Chen, C. Moore, S. P. Ong, G. Ceder. Journal of Materials Chemistry, 2012, 21, 17147–
17153.
Sidorenkite
Na3Mn(PO4)(CO3)

9. High-throughput catalyst design
NANO266
9
Greeley, J.; Jaramillo, T. F.; Bonde, J.; Chorkendorff, I. B.;
Nørskov, J. K. Computational high-throughput screening of
electrocatalytic materials for hydrogen evolution., Nat. Mater.,
2006, 5, 909–13, doi:10.1038/nmat1752.
Greeley, J.; Nørskov, J. K. Combinatorial Density Functional Theory-Based
Screening of Surface Alloys for the Oxygen Reduction Reaction, J. Phys.
Chem. C, 2009, 113, 4932–4939, doi:10.1021/jp808945y.

10. Other applications
NANO266
10
Topological insulators
Hautier, G.; Miglio, A.; Ceder, G.; Rignanese, G.-M.; Gonze,
X. Identification and design principles of low hole effective
mass p-type transparent conducting oxides., Nat. Commun.,
2013, 4, 2292, doi:10.1038/ncomms3292.
Transparent conducting oxides
Yang, K.; Setyawan, W.; Wang, S.; Buongiorno Nardelli, M.; Curtarolo, S. A
search model for topological insulators with high-throughput robustness
descriptors, Nat. Mater., 2012, 11, 614–619, doi:10.1038/nmat3332.

11. High-throughput organics
NANO266
11
Hachmann, J.; Olivares-Amaya, R.; Jinich, A.; Appleton, A. L.; Blood-Forsythe, M. a.; Seress, L. R.; Román-Salgado, C.; Trepte, K.; Atahan-
Evrenk, S.; Er, S.; Shrestha, S.; Mondal, R.; Sokolov, A.; Bao, Z.; Aspuru-Guzik, A. Lead candidates for high-performance organic photovoltaics
from high-throughput quantum chemistry – the Harvard Clean Energy Project, Energy Environ. Sci., 2014, 7, 698, doi:10.1039/c3ee42756k.
Cheng, L.; Assary, R. S.; Qu, X.; Jain, A.; Ong, S. P.; Rajput, N. N.; Persson, K.;
Curtiss, L. A. Accelerating Electrolyte Discovery for Energy Storage with High-
Throughput Screening, J. Phys. Chem. Lett., 2015, 6, 283–291, doi:10.1021/
jz502319n.

12. HT brings its own set of challenges
1. Error management
2. Workflow management
3. Data management
NANO266
12

13. “Random” errors are a major issue in high-
throughput
November 10, 2014 MAVRL Workshop 2014

14. Approaches
Software wrappers around existing software DFT software to apply
rule-based corrections on-the-fly
Significantly reduce error rates to below 1%
NANO266
14
Custodian Python Library
Examples

15. Computing properties frequently require multi-
step calculations
structure
charge
Band
structure
DOS
Optical
phonons
XAFS
spectra
GW

16. Data management
NANO266
16
Modern database
Source: http://dataconomy.com/sql-vs-nosql-need-know/

17. “Information wants to be free.”
– Steward Brand, 1960s

18. “Information wants to be free and
code wants to be wrong.”
– RSA Conference 2008

19. “Materials information and code
wants to be free
and right.”
– Unnamed developer, Materials
Project

20. The Materials Project is an open
science project to make the computed
properties of all known inorganic
materials publicly available to all
researchers to accelerate materials
innovation.
June 2011: Materials Genome Initiative
which aims to “fund computational tools,
software, new methods for material
characterization, and the development of
open standards and databases that will make
the process of discovery and development
of advanced materials faster, less
expensive, and more predictable”
https://www.materialsproject.org

21. As of Jul 21 2014
q Over 49,000 compounds,
and growing
q Diverse set of many
properties
q Structural (lattice
parameters, atomic
positions, etc.),
q Energetic (formation
energies, phase stability,
etc.)
q Electronic structure (DOS,
Bandstructures)
q Suite of Web Apps for
materials analysis

22. New integrated web interface
Materials Explorer: Search for materials by
formula, elements or properties
Battery Explorer: Search for battery materials
by voltage, capacity and other properties
Crystal Toolkit: Design new materials from
existing materials
Structure Predictor: Predict novel structures
Phase Diagram App: Generate compositional
and grand canonical phase diagrams
Pourbaix Diagram App: Generate Pourbaix
diagrams
Reaction Calculator: Balance reactions and
calculate their enthalpies

23. The Materials Project Open Software Stack
HT electronic structure calculations introduces
unique requirements
• Materials analysis – Python Materials Genomics
• Error checking and recovery – Custodian
• Scientific Workflows - Fireworks

24. Sustainable software development
Open-source
• Managed via
• More eyes = robustness
• Contributions from all over the world
Benevolent dictators
• Unified vision
• Quality control
Clear documentation
• Prevent code rot
• More users
Continuous integration and testing
• Ensure code is always working

25. Materials
Project DB
How do I
access MP
data?

26. Materials
Project DB
How do I
access MP
data?
Option 1:Direct access
Most flexible and powerful, but
• User needs to know db language
• Security is an issue
• Fragile – if db tech or schema
changes, user’s analysis breaks

27. Materials
Project DB
How do I
access MP
data?
Option 2:WebApps
Pros
• Intuitive and user-friendly
• Secure
Cons
• Significant loss in
flexibility and power
WebApps

28. Materials
Project DB
How do I
access MP
data?
Option 3:WebApps built on
RESTfulAPI
Pros
• Intuitive and user-friendly
• Secure
WebApps
RESTfulAPI
• Programmatic access for
developers and researchers

29. The Materials API
An open platform for accessing Materials
Project data based on REpresentational
State Transfer (REST) principles.
Flexible and scalable to cater to large
number of users, with different access
privileges.
Simple to use and code agnostic.

30. A REST API maps a URL to a
resource.
Example:
GET https://api.dropbox.com/1/account/info
Returns information about a user’s account.
Methods: GET, POST, PUT, DELETE, etc.
Response: Usually JSON or XML or both

31. Who implements REST
APIs?

33. https://www.materialsproject.org/rest/v1/materials/Fe2O3/vasp/energy
Preamble
Identifier, typically a
formula (Fe2O3), id
(1234) or chemical
system (Li-Fe-O)
Data type
(vasp, exp,
etc.)
Property
Request
type

34. Secure access
An individual API key provides secure
access with defined privileges.
All https requests must supply API key
as either a “x-api-key” header or a GET/
POST “API_KEY” parameter.
API key available at
https://www.materialsproject.org/
dashboard

35. Sample output (JSON)
Intuitive response
format
Machine-readable
(JSON parsers
available for most
programming
languages)
Metadata provides
provenance for
tracking
{
}
created_at: 2014-07-18T11:23:25.415382,
valid_response: true,
version: {
},
-
pymatgen: 2.9.9,
db: 2014.04.18,
rest: 1.0
response: [
],
-
{
},
-
energy: -67.16532048,
material_id: mp-24972
{
},
-
energy: -132.33035197,
material_id: mp-542309
{…},+
{…},+
{…},+
{…},+
{…},+
{…},+
{…},+
{…}+
copyright: Materials Project, 2012

36. Improved
accessibility of
data
More
developers of
analyses and
apps
Increased
data value

37. The Materials API
+
=
Powerful materials
analytics

38. Generating any phase diagram
with 5 lines of code
a = MPRester(YOUR_API_KEY)
entries = a.get_entries_in_chemsys([‘Li’, ‘Sn’, ‘S’])
pd = PhaseDiagram(entries)
plotter = PDPlotter(pd)
plotter.show()

39. Verifying a new structure
(Li4SnS4) with 1 calculation 9
lines of code
drone = VaspToComputedEntryDrone()
queen = BorgQueen(drone, rootpath=.”)
entries = queen.get_data()
a = MPRester(YOUR_API_KEY)
mp_entries = a.get_entries_in_chemsys([‘Li’, ‘Sn’, ‘S’])
entries.extend(mp_entries)
pd = PhaseDiagram(entries)
plotter = PDPlotter(pd)
plotter.show()