Most compositionally complex materials (CCMs, frequently referred to as high entropy alloys) are metastable and their attractive properties often belong to kinetically trapped states. However, pathways towards lower-free-energy phase states governing long-term stability, can remain hidden because diffusion-controlled a...
Yu-Jiao Li, Elaheh Akbarnejad, Quentin Bizot et al.· 2 citations
Foundation machine-learning interatomic potentials cover broad configurational and chemical spaces, but their reliability can vary across the atomic environments encountered during a simulation. Here we introduce the calibrated Mahalanobis (CALM) extrapolation grade $\gamma$, a piecewise differentiable per-atom quantit...
Yu. Lysogorskiy, A. Bochkarev, R. Drautz· 0 citations
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging...
Jörg Behler, Michele Ceriotti, Cecilia Clementi et al.· 0 citations
Phase stability in multicomponent solid solutions depends on configurational entropy beyond the ideal mixing limit, but capturing it together with vibrational entropy within the same atomistic framework remains challenging. Here, we extend non-equilibrium thermodynamic integration to composition-dependent transformatio...
Sarath Menon, M. Poul, T. Hickel et al.· 1 citation
Aromatic organic solutes in water exhibit a delicate balance between hydrophobic solvation and directional O-H$\cdots \pi$ hydrogen bonds, yet widely used force fields and state-of-the-art density functional approaches struggle to provide a consistent picture of these pivotal interactions. We introduce a data-efficient...
N. Stolte, H. Forbert, Yu. Lysogorskiy et al.· 0 citations
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