Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensemble averages. We introduce the thermodynamic interatomic potential (TIP), which extends an interatomic potential from its static energy to a...
Juno Nam, Bo-Wen Deng, Xiaochen Du et al.· 0 citations
Five frontier LLMs in a Bayesian Optimization setting are benchmarked against published statistical baselines on seven combinatorial datasets spanning protein engineering, reaction optimization, molecular design, peptide self-assembly, and catalysis, indicating that priors and data must be decoupled to achieve effectiv...
Mattias Akke, Soojung Yang, Jurǵis Ruža et al.· 0 citations
Predicting mobile-ion self-diffusivity $D^*$ from molecular dynamics (MD) simulations is essential for identifying promising solid-state electrolytes, but directly simulating ion diffusion is computationally expensive, particularly with high-accuracy machine learning interatomic potentials (MLIPs). Diffusion is a slow,...
Gavin Winter, Juno Nam, Rafael Gómez-Bombarelli· 0 citations
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