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

Last update: Dec 20, 2022

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Deep Learning PhaseSelect

Overview

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

8.11.2021 Andrij Vasylenko

Introduction

At the high level of consideration, the fundamental differences between the materials lie in the differences between the constituent chemical elements. Before the differences are detailed with the stoichiometric ratios and then atomic structures, the materials can be conceptualised at the level of their phase fields – the fields of all possible configurations of the selected chemical elements.

This code, PhaseSelect, classifies the materials at the level of sets of elements with respect to the likelihood to manifest a target functional property, while being synthetically accessible.

Please cite A.Vasylenko et al. 'Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties' arXiv:2202.01051, (2022)

Requirements

python-3.7

pip (version 19.0 or later)

OS:

Ubuntu (version 18.04 or later)

MacOS (Catalina 10.15.6 or later)

Dependencies:

TensorFlow-2.4.1

scikit-learn-0.24.0

numpy-1.19.4

pandas-1.1.4

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Releases(v.1.0.0)

v.1.0.0(Dec 20, 2022)
PhaseSelect focuses on quantitative assessment of materials at the early stages of materials discovery workflow, and assists selection of chemical elements to combine.

The package enables:

Learning elemental features from elemental co-occurrence in all previously studied materials

Classification of phase fields (sets of elements) w.r.t. high- and low- performing classes for properties of interest

Regression to predict a maximum value of the property of interest within a phase field.

Ranking phase fields w.r.t. chemical similarity as a metrics for synthetic accessibility within a phase field.

Source code(tar.gz)
Source code(zip)

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

Related tags

Overview

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

Introduction

Requirements

Dependencies:

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Owner

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