Generative design of inorganic materials
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.
Cite
@article{arxiv.2604.14082,
title = {Generative design of inorganic materials},
author = {Jose Recatala-Gomez and Haiwen Dai and Zhu Ruiming and Nikita Kazeev and Nong Wei and Gang Wu and Maciej Koperski and Tan Teck Leong and Andrey Ustyuzhanin and Gerbrand Ceder and Kostya Novoselov and Kedar Hippalgaonkar},
journal= {arXiv preprint arXiv:2604.14082},
year = {2026}
}