Generalized convex hull construction for materials discovery

TL;DR

This paper introduces a generalized convex hull method that uses data-driven principal coordinates to identify potentially synthesizable materials, reducing bias and uncertainty in materials discovery.

Contribution

It presents a novel convex hull framework based on principal coordinates, addressing bias and uncertainty in traditional methods for materials discovery.

Findings

Identifies candidate structures with high synthesis probability

Reduces redundant structures in the candidate set

Addresses bias and uncertainty in materials databases

Abstract

High-throughput computational materials searches generate large databases of locally-stable structures. Conventionally, the needle-in-a-haystack search for the few experimentally-synthesizable compounds is performed using a convex hull construction, which identifies structures stabilized by manipulation of a particular thermodynamic constraint (for example pressure or composition) chosen based on prior experimental evidence or intuition. To address the biased nature of this procedure we introduce a generalized convex hull framework. Convex hulls are constructed on data-driven principal coordinates, which represent the full structural diversity of the database. Their coupling to experimentally-realizable constraints hints at the conditions that are most likely to stabilize a given configuration. The probabilistic nature of our framework also addresses the uncertainty stemming from the use of approximate models during database construction, and eliminates redundant structures. The remaining small set of candidates that have a high probability of being synthesizable provide a much needed starting point for the determination of viable synthetic pathways.