Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

Vasylenko, Andrij ORCID: 0000-0002-6933-0628, Antypov, Dmytro ORCID: 0000-0003-1893-7785, Gusev, Vladimir, Gaultois, Michael ORCID: 0000-0003-2172-2507, Dyer, Matthew ORCID: 0000-0002-4923-3003 and Rosseinsky, Matthew ORCID: 0000-0002-1910-2483 (2022) Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties. [Preprint]

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Official URL: http://dx.doi.org/10.21203/rs.3.rs-1334648/v1

Abstract

At the high level, the fundamental differences between materials originate from the unique nature of the constituent chemical elements. Before specific differences emerge according to the precise ratios of elements (composition) in a given crystal structure (phase), the material can be represented by its phase field defined simply as the set of the constituent chemical elements. Classification of the materials at the level of their phase fields can accelerate materials discovery by selecting the elemental combinations that are likely to produce desirable functional properties in synthetically accessible materials. Here, we demonstrate that classification of the materials’ phase field with respect to the maximum expected value of a target functional property can be combined with the ranking of the materials’ synthetic accessibility. This end-to-end machine learning approach (PhaseSelect) first derives the atomic characteristics from the compositional environments in all computationally and experimentally explored materials, and then employs these characteristics to classify the phase field by their merit. PhaseSelect can quantify the materials’ potential at the level of the periodic table, which we demonstrate with significant accuracy for three avenues of materials’ applications: high-temperature superconducting, high-temperature magnetic and targetted energy band gap materials.

Item Type:	Preprint
Uncontrolled Keywords:	cond-mat.mtrl-sci, cond-mat.mtrl-sci, cond-mat.supr-con, cs.LG
Divisions:	Faculty of Science and Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	07 Feb 2022 09:58
Last Modified:	05 Feb 2023 05:03
DOI:	10.21203/rs.3.rs-1334648/v1
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3148386