Optical materials discovery and design with federated databases and machine learning

TL;DR

This paper demonstrates a machine learning-driven approach to discover optical materials by screening large federated databases of crystal structures, combining active learning and high-throughput calculations to identify promising candidates.

Contribution

It introduces a framework integrating federated databases, automated calculations, and machine learning for targeted optical materials discovery, enabling continuous updates and assessments.

Findings

Identification of promising high-refractive-index materials

Effective use of MODNet neural network for property prediction

Framework supports periodic re-evaluation with expanding datasets

Abstract

Combinatorial and guided screening of materials space with density-functional theory and related approaches has provided a wealth of hypothetical inorganic materials, which are increasingly tabulated in open databases. The OPTIMADE API is a standardised format for representing crystal structures, their measured and computed properties, and the methods for querying and filtering them from remote resources. Currently, the OPTIMADE federation spans over 20 data providers, rendering over 30 million structures accessible in this way, many of which are novel and have only recently been suggested by machine learning-based approaches. In this work, we outline our approach to non-exhaustively screen this dynamic trove of structures for the next-generation of optical materials. By applying MODNet, a neural network-based model for property prediction, within a combined active learning and high-throughput computation framework, we isolate particular structures and chemistries that should be most fruitful for further theoretical calculations and for experimental study as high-refractive-index materials. By making explicit use of automated calculations, federated dataset curation and machine learning, and by releasing these publicly, the workflows presented here can be periodically re-assessed as new databases implement OPTIMADE, and new hypothetical materials are suggested.