InvDesFlow-AL combines active learning with diffusion generative models to improve crystal structure prediction accuracy by 33% and identifies Li2AuH6 as a candidate BCS superconductor with 140 K transition temperature.
Inverse design of 3d molecular structures with conditional generative neural networks
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InvDesFlow-AL: active learning-based workflow for inverse design of functional materials
InvDesFlow-AL combines active learning with diffusion generative models to improve crystal structure prediction accuracy by 33% and identifies Li2AuH6 as a candidate BCS superconductor with 140 K transition temperature.