Computing the formation free energies of metastable defects at finite temperature remains challenging due to anharmonicity, the multiplicity of basins and the frequent occurrence of migration events. Using the previously introduced constrained Bayesian Adaptive Biasing Force method (BABFc), we establish a practical the...
Clovis Lapointe, Anruo Zhong, M. Athènes et al.· 0 citations
The intersection of deep learning (DL) and materials electron microscopy (EM) has significantly expanded since the early 2010 s; however, the diversity of model architectures and use cases makes it hard to identify literature gaps and determine which models are well-established in this field of application. This work p...
Camilo A. F. Salvador, Clovis Lapointe, T. Bilyk et al.· Machine Learning: Science an...· 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
This work introduces a framework for propagating MLIP uncertainty to the averaged committor probability, a kinetic observable that enables reaction-rate calculations and demonstrates that path-space reweighting provides an efficient route for propagating MLIP uncertainty to rare event kinetics.
Léonard Moracchini, Thomas Pigeon, Morgane Menz et al.· 0 citations
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