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Title: Accelerating materials science with high-throughput computations and machine learning

Abstract

With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. In this report we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.

Authors:
ORCiD logo [1]
  1. University of California San Diego, La Jolla, CA (United States). Materials Virtual Laboratory
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California San Diego, La Jolla, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF); US Department of the Navy, Office of Naval Research (ONR)
OSTI Identifier:
1528949
Alternate Identifier(s):
OSTI ID: 1529421
Grant/Contract Number:  
SC0012583; SC0012118; 1436976; N00014-15-1-0030; 1411192; N00014-16-1-2621; 1640899
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Computational Materials Science
Additional Journal Information:
Journal Volume: 161; Journal Issue: C; Journal ID: ISSN 0927-0256
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; machine learning; high-throughput; materials discovery; materials design; multi-scale models

Citation Formats

Ong, Shyue Ping. Accelerating materials science with high-throughput computations and machine learning. United States: N. p., 2019. Web. doi:10.1016/j.commatsci.2019.01.013.
Ong, Shyue Ping. Accelerating materials science with high-throughput computations and machine learning. United States. https://doi.org/10.1016/j.commatsci.2019.01.013
Ong, Shyue Ping. 2019. "Accelerating materials science with high-throughput computations and machine learning". United States. https://doi.org/10.1016/j.commatsci.2019.01.013. https://www.osti.gov/servlets/purl/1528949.
@article{osti_1528949,
title = {Accelerating materials science with high-throughput computations and machine learning},
author = {Ong, Shyue Ping},
abstractNote = {With unprecedented amounts of materials data generated from experiments as well as high-throughput density functional theory calculations, machine learning techniques has the potential to greatly accelerate materials discovery and design. In this report we review our efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.},
doi = {10.1016/j.commatsci.2019.01.013},
url = {https://www.osti.gov/biblio/1528949}, journal = {Computational Materials Science},
issn = {0927-0256},
number = C,
volume = 161,
place = {United States},
year = {Fri Feb 01 00:00:00 EST 2019},
month = {Fri Feb 01 00:00:00 EST 2019}
}

Journal Article:

Citation Metrics:
Cited by: 51 works
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Works referenced in this record:

Self-Consistent Equations Including Exchange and Correlation Effects
journal, November 1965


Inhomogeneous Electron Gas
journal, November 1964


Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
journal, February 2013


FireWorks: a dynamic workflow system designed for high-throughput applications: FireWorks: A Dynamic Workflow System Designed for High-Throughput Applications
journal, May 2015


Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows
journal, November 2017


Phosphates as Lithium-Ion Battery Cathodes: An Evaluation Based on High-Throughput ab Initio Calculations
journal, August 2011


Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations
journal, January 2011


Computational high-throughput screening of electrocatalytic materials for hydrogen evolution
journal, October 2006


A search model for topological insulators with high-throughput robustness descriptors
journal, May 2012


The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid
journal, August 2011


Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
journal, July 2013


AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
journal, June 2012


Mastering the game of Go without human knowledge
journal, October 2017


In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images
journal, April 2018


Generalized Gradient Approximation Made Simple
journal, October 1996


Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness
journal, January 2017


van der Waals Interactions in Layered Lithium Cobalt Oxides
journal, August 2015


Elastic properties of bulk and low-dimensional materials using van der Waals density functional
journal, July 2018


Assessment of van der Waals inclusive density functional theory methods for layered electroactive materials
journal, January 2017


Accurate first-principles structures and energies of diversely bonded systems from an efficient density functional
journal, June 2016


Surface energies of elemental crystals
journal, September 2016


High-throughput computational X-ray absorption spectroscopy
journal, July 2018


Automated generation and ensemble-learned matching of X-ray absorption spectra
journal, March 2018


Elastic Properties of Alkali Superionic Conductor Electrolytes from First Principles Calculations
journal, November 2015


Aqueous Stability of Alkali Superionic Conductors from First-Principles Calculations
journal, April 2016


Progress and prospective of solid-state lithium batteries
journal, February 2013


Inorganic solid Li ion conductors: An overview
journal, June 2009


A lithium superionic conductor
journal, July 2011


Superionic glass-ceramic electrolytes for room-temperature rechargeable sodium batteries
journal, January 2012


Design principles for solid-state lithium superionic conductors
journal, August 2015


Rational Composition Optimization of the Lithium-Rich Li 3 OCl 1– x Br x Anti-Perovskite Superionic Conductors
journal, May 2015


Role of Na + Interstitials and Dopants in Enhancing the Na + Conductivity of the Cubic Na 3 PS 4 Superionic Conductor
journal, December 2015


Room-Temperature All-solid-state Rechargeable Sodium-ion Batteries with a Cl-doped Na3PS4 Superionic Conductor
journal, September 2016


Insights into the Performance Limits of the Li 7 P 3 S 11 Superionic Conductor: A Combined First-Principles and Experimental Study
journal, March 2016


Data-Driven First-Principles Methods for the Study and Design of Alkali Superionic Conductors
journal, September 2016


Experimental and Computational Evaluation of a Sodium-Rich Anti-Perovskite for Solid State Electrolytes
journal, January 2016


Superionic Conductivity in Lithium-Rich Anti-Perovskites
journal, August 2012


Ultimate Limits to Intercalation Reactions for Lithium Batteries
journal, October 2014


Can Multielectron Intercalation Reactions Be the Basis of Next Generation Batteries?
journal, January 2018


Effect of Structure on the Fe[sup 3+]∕Fe[sup 2+] Redox Couple in Iron Phosphates
journal, January 1997


Thermodynamics, Kinetics and Structural Evolution of ε-LiVOPO 4 over Multiple Lithium Intercalation
journal, February 2016


Comparison of the polymorphs of VOPO 4 as multi-electron cathodes for rechargeable alkali-ion batteries
journal, January 2017


Uniform second Li ion intercalation in solid state ϵ-LiVOPO4
journal, August 2016


KVOPO 4 : A New High Capacity Multielectron Na-Ion Battery Cathode
journal, May 2018


A revolution in lighting
journal, April 2015


The inorganic crystal structure data base
journal, May 1983


Electronic Structure Descriptor for the Discovery of Narrow-Band Red-Emitting Phosphors
journal, May 2016


Elucidating Structure–Composition–Property Relationships of the β-SiAlON:Eu 2+ Phosphor
journal, November 2016


Understanding the links between composition, polyhedral distortion, and luminescence properties in green-emitting β-Si6−zAlzOzN8−z:Eu2+ phosphors
journal, January 2017


Thermal properties of graphite, molybdenum and tantalum to their destruction temperatures
journal, August 1960


Nanostructured high-strength molybdenum alloys with unprecedented tensile ductility
journal, January 2013


Toughening of brittle materials by grain boundary engineering
journal, December 2004


Bismuth-induced embrittlement of copper grain boundaries
journal, August 2004


Molybdenum Silicide Based Materials and Their Properties
journal, June 1999


Pesting of the high-temperature intermetallic MoSi2
journal, December 1993


Mo-Si-B Alloys for Ultrahigh-Temperature Structural Applications
journal, June 2012


Improvement in the ductility of molybdenum alloys due to grain boundary segregation
journal, February 2002


Effect of Zr, B and C additions on the ductility of molybdenum
journal, April 2002


Ductilization of Mo–Si solid solutions manufactured by powder metallurgy
journal, August 2009


Role of Zr in strengthening MoSi2 from density functional theory calculations
journal, February 2018


Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
journal, March 2015


Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals
journal, September 2018


Accurate force field for molybdenum by machine learning large materials data
journal, September 2017


On representing chemical environments
journal, May 2013


The equilibrium diagram of the system molybdenum-nickel
journal, September 1964


Calculating phase diagrams using PANDAT and panengine
journal, December 2003


Formation enthalpies by mixing GGA and GGA + U calculations
journal, July 2011


Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning
journal, May 2017


Combinatorial screening for new materials in unconstrained composition space with machine learning
journal, March 2014


Machine Learning Energies of 2 Million Elpasolite ( A B C 2 D 6 ) Crystals
journal, September 2016


Universal fragment descriptors for predicting properties of inorganic crystals
journal, June 2017


Predicting the thermodynamic stability of perovskite oxides using machine learning models
journal, July 2018


Deep learning
journal, May 2015


Deep neural networks for accurate predictions of crystal stability
journal, September 2018


Oxidation energies of transition metal oxides within the GGA + U framework
journal, May 2006


Theoretical approaches to x-ray absorption fine structure
journal, July 2000


Ab initio theory and calculations of X-ray spectra
journal, July 2009


Band-structure calculations for the 3 d transition metal oxides in G W
journal, February 2013


Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms
journal, May 2018


Efficient implementation of core-excitation Bethe–Salpeter equation calculations
journal, December 2015


Bethe-Salpeter equation calculations of core excitation spectra
journal, March 2011


Works referencing / citing this record:

Machine learning and big scientific data
journal, January 2020

  • Hey, Tony; Butler, Keith; Jackson, Sam
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 378, Issue 2166
  • https://doi.org/10.1098/rsta.2019.0054