Generative design of inorganic materials

TL;DR

This paper discusses a generative design framework for inorganic materials that integrates multi-modal learning, property databases, and high-throughput experiments to enable data-driven inverse design of functional materials.

Contribution

It introduces a unified generative design framework combining AI models, data integration, and experimental validation for inorganic materials discovery.

Findings

Proposes a closed-loop machine learning framework for materials design.

Highlights the integration of multi-modal data with generative models.

Argues for domain-specific workflows to advance inverse design.

Abstract

Materials discovery is fundamental to advance next-generation technologies as well as for sustainable and circular economy. Beyond computational screening, generative models are efficient at finding materials with desired properties, via multi-modal learning using multiscale data. This perspective examines the landscape of generative design for inorganic materials and discusses the integration of multi-modal learning with high-throughput experimental validation. We contextualize these challenges through the lens of a generative design framework as a unified approach to address the data-driven inverse design of functional materials. The central idea of the framework is constructed around a foundation AI model for inorganic materials interlinked deeply with various property databases and high-throughput experiments via a machine learning driven closed loop, which enables the framework to solve key challenges in functional materials. We argue that domain-specific implementations of such integrated workflows represent a promising pathway toward the unresolved challenge of data-driven inverse design for atom-engineered inorganic functional materials.