Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics

TL;DR

This paper presents a simple, interpretable machine learning model that predicts the electronic band gap of crystalline materials solely from their chemical composition, addressing the property’s atypical statistical distribution.

Contribution

The authors introduce a novel, composition-based model for band gap prediction that incorporates chemical heuristics and models the property’s unique distribution, with a single parameter per element.

Findings

Model achieves accurate band gap predictions from composition alone.

Provides chemical interpretability linking elements to band gap values.

Addresses the atypical statistical distribution of band gaps.

Abstract

In solid-state materials science, substantial efforts have been devoted to the calculation and modeling of the electronic band gap. While a wide range of ab initio methods and machine learning algorithms have been created that can predict this quantity, the development of new computational approaches for studying the band gap remains an active area of research. Here we introduce a simple machine learning model for predicting the band gap using only the chemical composition of the crystalline material. To motivate the form of the model, we first analyze the empirical distribution of the band gap, which sheds new light on its atypical statistics. Specifically, our analysis enables us to frame band gap prediction as a task of modeling a mixed random variable, and we design our model accordingly. Our model formulation incorporates thematic ideas from chemical heuristic models for other material properties in a manner that is suited towards the band gap modeling task. The model has exactly one parameter corresponding to each element, which is fit using data. To predict the band gap for a given material, the model computes a weighted average of the parameters associated with its constituent elements and then takes the maximum of this quantity and zero. The model provides heuristic chemical interpretability by intuitively capturing the associations between the band gap and individual chemical elements.