4 Result(s)
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Article
Open AccessComplexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we contrast the performance of force,...
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Article
Open AccessBayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by map** both forces and uncertainties onto functions of low-dimensional features. ...
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Article
Open AccessOn-the-fly active learning of interpretable Bayesian force fields for atomistic rare events
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable error...
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Article
Improved chemical and electrochemical stability of perovskite oxides with less reducible cations at the surface
Segregation and phase separation of aliovalent dopants on perovskite oxide (ABO3) surfaces are detrimental to the performance of energy conversion systems such as solid oxide fuel/electrolysis cells and catalysts...
