6 papers
Predicting Atomistic Transitions with Transformers
Henry Tischler, Wenting Li, Qi Tang +2
Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation technique…
Characterizing Atomistic Transitions Using Cross-scale Graph-pooled Chebyshev Signatures
Rostyslav Hnatyshyn, Danny Perez
Large-scale atomistic simulations can produce extreme volumes of information in the form of long trajectories. Reliably and automatically extracting key information from such datas…
Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions
Anton Bochkarev, Yury Lysogorskiy, Aparna Subramanyam +2
Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an…
Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
Danny Perez, Aparna P. A. Subramanyam, Ivan Maliyov +1
The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now p…
Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials
Aparna P. A. Subramanyam, Danny Perez
In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. Ho…
Parameter uncertainties for imperfect surrogate models in the low-noise regime
Thomas D Swinburne, Danny Perez
Bayesian regression determines model parameters by minimizing the expected loss, an upper bound to the true generalization error. However, the loss ignores misspecification, where…