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Title: Benchmarking the acceleration of materials discovery by sequential learning

Abstract

Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any “good” material, discovery of all “good” materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 comparedmore » to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery.« less

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [3]
  1. Accelerated Materials Design and Discovery, Toyota Research Institute, Los Altos, USA
  2. Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA
  3. Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, USA, Division of Engineering and Applied Science
Publication Date:
Research Org.:
California Institute of Technology (CalTech), Pasadena, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1598712
Alternate Identifier(s):
OSTI ID: 1801646
Grant/Contract Number:  
SC0004993; SC0020383
Resource Type:
Published Article
Journal Name:
Chemical Science
Additional Journal Information:
Journal Name: Chemical Science Journal Volume: 11 Journal Issue: 10; Journal ID: ISSN 2041-6520
Publisher:
Royal Society of Chemistry (RSC)
Country of Publication:
United Kingdom
Language:
English
Subject:
36 MATERIALS SCIENCE

Citation Formats

Rohr, Brian, Stein, Helge S., Guevarra, Dan, Wang, Yu, Haber, Joel A., Aykol, Muratahan, Suram, Santosh K., and Gregoire, John M. Benchmarking the acceleration of materials discovery by sequential learning. United Kingdom: N. p., 2020. Web. doi:10.1039/C9SC05999G.
Rohr, Brian, Stein, Helge S., Guevarra, Dan, Wang, Yu, Haber, Joel A., Aykol, Muratahan, Suram, Santosh K., & Gregoire, John M. Benchmarking the acceleration of materials discovery by sequential learning. United Kingdom. https://doi.org/10.1039/C9SC05999G
Rohr, Brian, Stein, Helge S., Guevarra, Dan, Wang, Yu, Haber, Joel A., Aykol, Muratahan, Suram, Santosh K., and Gregoire, John M. Wed . "Benchmarking the acceleration of materials discovery by sequential learning". United Kingdom. https://doi.org/10.1039/C9SC05999G.
@article{osti_1598712,
title = {Benchmarking the acceleration of materials discovery by sequential learning},
author = {Rohr, Brian and Stein, Helge S. and Guevarra, Dan and Wang, Yu and Haber, Joel A. and Aykol, Muratahan and Suram, Santosh K. and Gregoire, John M.},
abstractNote = {Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any “good” material, discovery of all “good” materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery.},
doi = {10.1039/C9SC05999G},
journal = {Chemical Science},
number = 10,
volume = 11,
place = {United Kingdom},
year = {2020},
month = {3}
}

Journal Article:
Free Publicly Available Full Text
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https://doi.org/10.1039/C9SC05999G

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