Compositional descriptor-based recommender system accelerating the materials discovery

TL;DR

This paper introduces a descriptor-based recommender system that leverages compositional descriptors to efficiently identify chemically relevant inorganic compounds, accelerating materials discovery beyond traditional data-driven methods.

Contribution

It presents a novel recommender system utilizing compositional descriptors as prior knowledge to speed up the discovery of new inorganic compounds.

Findings

The recommender system effectively predicts stable inorganic compounds.

Validation shows improved discovery efficiency over existing methods.

Density functional theory confirms the stability of predicted compounds.

Abstract

Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds. A powerful machine-learning strategy is mandatory to discover new inorganic compounds from all chemical combinations. Herein we propose a descriptor-based recommender-system approach to estimate the relevance of chemical compositions where stable crystals can be formed [i.e., chemically relevant compositions (CRCs)]. As well as data-driven compositional similarity used in the literature, the use of compositional descriptors as a prior knowledge can accelerate the discovery of new compounds. We validate our recommender systems in two ways. Firstly, one database is used to construct a model, while another is used for the validation. Secondly, we estimate the phase stability for compounds at expected CRCs using density functional theory calculations.