Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
Name
acscentsci.0c00026.pdf
Description
Published version
Size
2.91 MB
Format
Adobe PDF
Checksum (MD5)
ece8a051395c325fa2eefd95582bee08
Author(s)
Janet, Jon Paul
Ramesh, Sahasrajit
Duan, Chenru
Kulik, Heather J.
Date Issued
March 11, 2020
Journal
ACS Central Science
Publisher
American Chemical Society (ACS)
Citation
Janet, Jon Paul, Ramesh, Sahasrajit, Duan, Chenru and Kulik, Heather J. 2020. "Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization." ACS Central Science, 6 (4).
Version
Final published version
Abstract
© 2020 American Chemical Society. The accelerated discovery of materials for real world applications requires the achievement of multiple design objectives. The multidimensional nature of the search necessitates exploration of multimillion compound libraries over which even density functional theory (DFT) screening is intractable. Machine learning (e.g., artificial neural network, ANN, or Gaussian process, GP) models for this task are limited by training data availability and predictive uncertainty quantification (UQ). We overcome such limitations by using efficient global optimization (EGO) with the multidimensional expected improvement (EI) criterion. EGO balances exploitation of a trained model with acquisition of new DFT data at the Pareto front, the region of chemical space that contains the optimal trade-off between multiple design criteria. We demonstrate this approach for the simultaneous optimization of redox potential and solubility in candidate M(II)/M(III) redox couples for redox flow batteries from a space of 2.8 M transition metal complexes designed for stability in practical redox flow battery (RFB) applications. We show that a multitask ANN with latent-distance-based UQ surpasses the generalization performance of a GP in this space. With this approach, ANN prediction and EI scoring of the full space are achieved in minutes. Starting from ca. 100 representative points, EGO improves both properties by over 3 standard deviations in only five generations. Analysis of lookahead errors confirms rapid ANN model improvement during the EGO process, achieving suitable accuracy for predictive design in the space of transition metal complexes. The ANN-driven EI approach achieves at least 500-fold acceleration over random search, identifying a Pareto-optimal design in around 5 weeks instead of 50 years.
Subjects
General Chemical Engineering
General Chemistry
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1021/acscentsci.0c00026
