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cond-mat.mtrl-sci2026
SLayerGen: a Crystal Generative Model for all Space and Layer Groups
Rees Chang, Andrew Novick, Ryan P Adams +1
Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as 2D superconductors, thin film…
cond-mat.mtrl-sci2025
Space Group Equivariant Crystal Diffusion
Rees Chang, Angela Pak, Alex Guerra +4
Accelerating inverse design of crystalline materials with generative models has significant implications for a range of technologies. Unlike other atomic systems, 3D crystals are i…
cond-mat.mtrl-sci2022★ 1 cited
Towards overcoming data scarcity in materials science: unifying models and datasets with a mixture of experts framework
Rees Chang, Yu-Xiong Wang, Elif Ertekin
While machine learning has emerged in recent years as a useful tool for rapid prediction of materials properties, generating sufficient data to reliably train models without overfi…