Elemental Reactivity Maps for Materials Discovery

TL;DR

This paper presents a machine learning approach to predict highly reactive elemental combinations for inorganic materials discovery, utilizing reactivity maps to identify promising new compounds like Co4Ge3.19Al0.81 and Co2Al1.24Ge1.76.

Contribution

It introduces a method to construct reliable negative datasets for reactivity prediction and visualizes elemental reactivity maps to guide materials discovery.

Findings

Predicted high reactivity in the Co_Al_Ge system.

Synthesized two novel ternary compounds based on predictions.

Demonstrated effectiveness of reactivity maps in materials discovery.

Abstract

When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to predict elemental combinations with high reactivity for materials discovery. The essential issue for such prediction is the uncertainty of whether the unreported combinations are non-reactive or not just investigated, though the reactive combinations can be easily collected as positive datasets from the materials databases. To construct the negative datasets, we developed a process to select reliable non-reactive combinations by evaluating the similarity between unreported and reactive combinations. The machine learning models were trained by both datasets and the prediction results were visualized by two-dimensional heatmaps: elemental reactivity maps to identify elemental combinations with high reactivity but no reported stable compounds. The maps predicted high reactivity (i.e., synthesizability) for the Co_Al_Ge ternary system, and two novel ternary compounds were synthesized: Co4Ge3.19Al0.81 and Co2Al1.24Ge1.76.