High-throughput Computational Materials Design

Shyue Ping Ong

Eψ(r) = − h2

2m∇2ψ(r)+V (r)ψ(r)

Material Properties

First principles materials designBasic laws of Physics

Density functional theory (DFT) approximation

HT materials design is today a reality

Quantum Espresso

Gaussian

VASP NwChem

Moore’s Law

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

Li2O

Fe2O3

P2O5

LiFeO2

Li3PO4

Li5FeO4

LiPO3

Fe2P4O12

Fe(PO3)3

Fe2P2O7

FeP4O11Li4P2O7

Fe3(PO4)2LiFePO4

Capacity = No. of Li transferred Weight or vol.

0 0.2 0.4 0.6 0.8 10

50

100

150

200

250

Diffusion coordinate

Ener

gy (m

eV)

LCONCO

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

High-throughput materials design���framework

Known compounds

New compounds

permutation strategy Database

Initial screening (non-computational)

Computational Screening

Candidate materials

Propertycomputation

Data miningDiscussion

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.

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, …

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!

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)

High-throughput catalyst design

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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.

Other applications

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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.

High-throughput organics

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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.

HT brings its own set of challenges

1.  Error management 2.  Workflow management 3.  Data management

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“Random” errors are a major issue in high-throughput

November 10, 2014 MAVRL Workshop 2014

Approaches

Software wrappers around existing software DFT software to apply rule-based corrections on-the-fly

Significantly reduce error rates to below 1%

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Custodian Python Library

Examples

Computing properties frequently require multi-step calculations

structure

charge

Band structure

DOS

Optical

phonons

XAFS spectra

GW

Data management

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Modern database

Source: http://dataconomy.com/sql-vs-nosql-need-know/

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

“Information wants to be free and code wants to be wrong.”

– RSA Conference 2008

“Materials information and code wants to be free

and right.” – Unnamed developer, Materials

Project

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

As of Jul 21 2014q  Over 49,000 compounds,

and growingq  Diverse set of many

propertiesq 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

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

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

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

Materials Project DB

How do I access MP

data?

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

Materials Project DB

How do I access MP

data?

Option 2: Web Apps

Pros •  Intuitive and user-friendly •  Secure

Cons •  Significant loss in

flexibility and power

Web

App

s

Materials Project DB

How do I access MP

data?

Option 3: Web Apps built on RESTful API

Pros •  Intuitive and user-friendly •  Secure

Web

App

s

RE

STf

ul A

PI

•  Programmatic access for

developers and researchers

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.

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

Who implements REST APIs?

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

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

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"

Improved accessibility of

data

More developers of analyses and

apps

Increased data value

The Materials API +

= Powerful materials

analytics

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()

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()