Enabling deeper learning on big data for materials informatics applications

Jha, Dipendra; Gupta, Vishu; Ward, Logan; Yang, Zijiang; Wolverton, Christopher; Foster, Ian; Liao, Wei-keng; Choudhary, Alok; Agrawal, Ankit

doi:10.1038/s41598-021-83193-1

Title: Enabling deeper learning on big data for materials informatics applications

Abstract

Abstract The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) bettermore »« less

Authors:: Jha, Dipendra; Gupta, Vishu; Ward, Logan; Yang, Zijiang; Wolverton, Christopher; Foster, Ian; Liao, Wei-keng; Choudhary, Alok; Agrawal, Ankit

Publication Date:: Fri Feb 19 00:00:00 EST 2021

Research Org.:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1766588

Alternate Identifier(s):: OSTI ID: 1823644

Grant/Contract Number:: SC0014330, DE-SC0019358; AC02-06CH11357

Resource Type:: Published Article

Journal Name:: Scientific Reports

Additional Journal Information:: Journal Name: Scientific Reports Journal Volume: 11 Journal Issue: 1; Journal ID: ISSN 2045-2322

Publisher:: Nature Publishing Group

Country of Publication:: United Kingdom

Language:: English

Subject:: 36 MATERIALS SCIENCE; Computational methods; Materials science

Citation Formats


                    Jha, Dipendra, Gupta, Vishu, Ward, Logan, Yang, Zijiang, Wolverton, Christopher, Foster, Ian, Liao, Wei-keng, Choudhary, Alok, and Agrawal, Ankit. Enabling deeper learning on big data for materials informatics applications.  United Kingdom: N. p., 2021. 
Web.  doi:10.1038/s41598-021-83193-1.

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                    Jha, Dipendra, Gupta, Vishu, Ward, Logan, Yang, Zijiang, Wolverton, Christopher, Foster, Ian, Liao, Wei-keng, Choudhary, Alok, & Agrawal, Ankit. Enabling deeper learning on big data for materials informatics applications.  United Kingdom.  https://doi.org/10.1038/s41598-021-83193-1

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                    Jha, Dipendra, Gupta, Vishu, Ward, Logan, Yang, Zijiang, Wolverton, Christopher, Foster, Ian, Liao, Wei-keng, Choudhary, Alok, and Agrawal, Ankit. Fri .  
"Enabling deeper learning on big data for materials informatics applications".  United Kingdom.  https://doi.org/10.1038/s41598-021-83193-1.

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@article{osti_1766588,

  title        = {Enabling deeper learning on big data for materials informatics applications},

  author       = {Jha, Dipendra and Gupta, Vishu and Ward, Logan and Yang, Zijiang and Wolverton, Christopher and Foster, Ian and Liao, Wei-keng and Choudhary, Alok and Agrawal, Ankit},

  abstractNote = {Abstract The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.},

  doi          = {10.1038/s41598-021-83193-1},

  journal      = {Scientific Reports},

  number       = 1,

  volume       = 11,

  place        = {United Kingdom},

  year         = {Fri Feb 19 00:00:00 EST 2021},

  month        = {Fri Feb 19 00:00:00 EST 2021}

}

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Title: Enabling deeper learning on big data for materials informatics applications

Abstract

Citation Formats

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science journal, April 2016

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition journal, December 2018

Learning atoms for materials discovery journal, June 2018

Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks journal, October 2018

Densely Connected Convolutional Networks conference, July 2017

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies journal, December 2015

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A predictive machine learning approach for microstructure optimization and materials design journal, June 2015

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

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations journal, July 2017

Materials Informatics: The Materials “Gene” and Big Data journal, July 2015

Microsoft COCO: Common Objects in Context book, January 2014

Matminer: An open source toolkit for materials data mining journal, September 2018

Going deeper with convolutions conference, June 2015

Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape journal, August 2018

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

Deep materials informatics: Applications of deep learning in materials science journal, June 2019

The high-throughput highway to computational materials design journal, February 2013

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning journal, November 2019

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) journal, September 2013

Machine learning of molecular electronic properties in chemical compound space journal, September 2013

ImageNet: A large-scale hierarchical image database conference, June 2009

Deep Residual Learning for Image Recognition conference, June 2016

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

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning journal, February 2017

Machine learning in materials informatics: recent applications and prospects journal, December 2017

Materials science with large-scale data and informatics: Unlocking new opportunities journal, May 2016

Adaptive machine learning framework to accelerate ab initio molecular dynamics journal, December 2014

Predicting materials properties without crystal structure: deep representation learning from stoichiometry journal, December 2020

Predicting the Band Gaps of Inorganic Solids by Machine Learning journal, March 2018

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

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals journal, April 2019

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

IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery conference, July 2019

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High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory journal, July 2017

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties journal, April 2018

Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery journal, September 2015

An improved residual LSTM architecture for acoustic modeling conference, July 2017

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

Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery journal, June 2020

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
journal, April 2016

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
journal, December 2018

Learning atoms for materials discovery
journal, June 2018

Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks
journal, October 2018

Densely Connected Convolutional Networks
conference, July 2017

The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
journal, December 2015

Accelerated search for materials with targeted properties by adaptive design
journal, April 2016

A predictive machine learning approach for microstructure optimization and materials design
journal, June 2015

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

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations
journal, July 2017

Materials Informatics: The Materials “Gene” and Big Data
journal, July 2015

Microsoft COCO: Common Objects in Context
book, January 2014

Matminer: An open source toolkit for materials data mining
journal, September 2018

Going deeper with convolutions
conference, June 2015

Machine learning with force-field-inspired descriptors for materials: Fast screening and mapping energy landscape
journal, August 2018

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

Deep materials informatics: Applications of deep learning in materials science
journal, June 2019

The high-throughput highway to computational materials design
journal, February 2013

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
journal, November 2019

Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
journal, September 2013

Machine learning of molecular electronic properties in chemical compound space
journal, September 2013

ImageNet: A large-scale hierarchical image database
conference, June 2009

Deep Residual Learning for Image Recognition
conference, June 2016

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

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
journal, February 2017

Machine learning in materials informatics: recent applications and prospects
journal, December 2017

Materials science with large-scale data and informatics: Unlocking new opportunities
journal, May 2016

Adaptive machine learning framework to accelerate ab initio molecular dynamics
journal, December 2014

Predicting materials properties without crystal structure: deep representation learning from stoichiometry
journal, December 2020

Predicting the Band Gaps of Inorganic Solids by Machine Learning
journal, March 2018

Machine Learning Energies of 2 Million Elpasolite $(A B C_{2} D_{6})$ Crystals
journal, September 2016

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
journal, April 2019

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

IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
conference, July 2019

Atomistic calculations and materials informatics: A review
journal, June 2017

A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016

The Materials Data Facility: Data Services to Advance Materials Science Research
journal, July 2016

Informatics Infrastructure for the Materials Genome Initiative
journal, July 2016

High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory
journal, July 2017

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
journal, April 2018

Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
journal, September 2015

An improved residual LSTM architecture for acoustic modeling
conference, July 2017

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

Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery
journal, June 2020