Jump to main content
Jump to site search


Benchmarking the acceleration of materials discovery by sequential learning

Author affiliations

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 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.

Graphical abstract: Benchmarking the acceleration of materials discovery by sequential learning

Back to tab navigation

Supplementary files

Article information


Submitted
27 Nov 2019
Accepted
27 Jan 2020
First published
29 Jan 2020

This article is Open Access
All publication charges for this article have been paid for by the Royal Society of Chemistry

Chem. Sci., 2020, Advance Article
Article type
Edge Article

Benchmarking the acceleration of materials discovery by sequential learning

B. Rohr, H. S. Stein, D. Guevarra, Y. Wang, J. A. Haber, M. Aykol, S. K. Suram and J. M. Gregoire, Chem. Sci., 2020, Advance Article , DOI: 10.1039/C9SC05999G

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. Material from this article can be used in other publications provided that the correct acknowledgement is given with the reproduced material.

Reproduced material should be attributed as follows:

  • For reproduction of material from NJC:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the Centre National de la Recherche Scientifique (CNRS) and the RSC.
  • For reproduction of material from PCCP:
    [Original citation] - Published by the PCCP Owner Societies.
  • For reproduction of material from PPS:
    [Original citation] - Published by The Royal Society of Chemistry (RSC) on behalf of the European Society for Photobiology, the European Photochemistry Association, and RSC.
  • For reproduction of material from all other RSC journals:
    [Original citation] - Published by The Royal Society of Chemistry.

Information about reproducing material from RSC articles with different licences is available on our Permission Requests page.


Social activity

Search articles by author

Spotlight

Advertisements