An Invertible Crystallographic Representation for General Inverse Design of Inorganic Crystals with Targeted Properties

Traditionally, the discovery of new solid-state materials with user-defined properties is driven either by human intuition, heuristic chemical rules, and/or density functional theory (DFT). However, these methods have limitations, either in accessibly (domain expertise), accuracy, and/or throughput [1]. Consequently, the material search space remains underexplored, given order 105 reported compounds compared to order 1010 theorized ternary compounds [2]. To accelerate the exploration of new solid-state materials, a framework capable of inverse design for materials with user-defined properties is needed. Herein, we present a generalized framework for inverse design of crystals with user-defined properties, which include both ground-state and excited-state properties (e.g., thermoelectric power factor) using sparsely labelled training data. The key enabler of this inverse-design framework is a general and invertible crystallographic representation that encodes the crystallographic information into the representations in both real space and reciprocal space. The trained surrogate model achieves similar property prediction accuracy and precision as DFT calculations within seconds. Using the developed inverse-design framework, we design 79 new crystals with user-targeted formation energies, 17 crystals with targeted bandgap, and 27 crystals for potential thermoelectric applications. The compositions of those designed materials are unique and cannot be found in the training or test sets. We validate our predictions using first-principle calculations. ­­Toward bridging the gap between simulation and experiment, we demonstrate a naive synthesizability metric — predicting the existence of an ICSD record — and show this methodology can, in principle, include stability and/or synthesizability as a target metric, once consensus metrics are agreed upon by the field.