Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps



Pyzer-Knapp, Edward O, Chen, Linjiang, Day, Graeme M and Cooper, Andrew I
(2021) Accelerating computational discovery of porous solids through improved navigation of energy-structure-function maps. SCIENCE ADVANCES, 7 (33). eabi4763-.

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Abstract

While energy-structure-function (ESF) maps are a powerful new tool for in silico materials design, the cost of acquiring an ESF map for many properties is too high for routine integration into high-throughput virtual screening workflows. Here, we propose the next evolution of the ESF map. This uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost. We use this approach to obtain a two orders of magnitude speedup on an ESF study that focused on the discovery of molecular crystals for methane capture, saving more than 500,000 central processing unit hours from the original protocol. By accelerating the acquisition of insight from ESF maps, we pave the way for the use of these maps in automated ultrahigh-throughput screening pipelines by greatly reducing the opportunity risk associated with the choice of system to calculate.

Item Type: Article
Uncontrolled Keywords: 3404 Medicinal and Biomolecular Chemistry, 34 Chemical Sciences, Biotechnology
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 23 Dec 2021 15:15
Last Modified: 21 Jun 2024 10:29
DOI: 10.1126/sciadv.abi4763
Open Access URL: https://www.science.org/doi/10.1126/sciadv.abi4763
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URI: https://livrepository.liverpool.ac.uk/id/eprint/3145972