Selecting compact training sets for machine-learned interatomic potentials requires deciding whether to preserve structural diversity or target configurations on which models disagree. The better choice can depend on how much data is retained, making a comparison at one training-set size insufficient. Here we link sele...
Foundation machine-learning interatomic potentials (MLIPs) are rapidly replacing density-functional theory (DFT) for modeling structure and nuclear dynamics, making their fidelity in strongly correlated systems an urgent question. We test three foundation potentials on the low-temperature order of three correlated, iso...
S. Acharya, D. Pashov, M. van Schilfgaarde et al.· 0 citations
It is argued that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods, and quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the...
Bruno Camino, C. R. Catlow, J. Buckeridge et al.· 0 citations
Foundation machine learning interatomic potentials (MLIPs) deliver near-ab-initio accuracy at a fraction of the computational cost, yet their promise for Metal-organic Frameworks (MOFs) remains largely unrealized as large unit cells make first-principles training data expensive to generate, fine-tuned models are scarce...
T. J. Inizan, Prathami Divakar Kamath, Alin M. Elena et al.· 0 citations
Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexible architectures, and low-energy lattice vibrations, metal-organic frameworks (MOFs) represent a rich platform for exploring NTE. However,...
Prathami Divakar Kamath, F. Tavani, A. Elena et al.· 1 citation
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