Results 31 to 40 of about 522,455 (311)
Heat flux for semilocal machine-learning potentials
Physical Review B, 2023 The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. Machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time ...
Langer, Marcel F. +4 more
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ML meets MLn: Machine learning in ligand promoted homogeneous catalysis
Artificial Intelligence Chemistry, 2023 The benefits of using machine learning approaches in the design, optimisation and understanding of homogeneous catalytic processes are being increasingly realised.
Jonathan D. Hirst +7 more
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A Survey of Machine Learning-Based System Performance Optimization Techniques
Applied Sciences, 2021 Recently, the machine learning research trend expands to the system performance optimization field, where it has still been proposed by researchers based on their intuitions and heuristics.
Hyejeong Choi, Sejin Park
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Improving Bayesian Network Structure Learning in the Presence of Measurement Error
, 2022 Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables.
Liu, Y, Constantinou, AC, Guo, Z
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Electrostatic Embedding of Machine Learning Potentials
Journal of Chemical Theory and Computation, 2022 This work presents a variant of an electrostatic embedding scheme that allows the embedding of arbitrary machine learned potentials trained on molecular systems in vacuo. The scheme is based on physically motivated models of electronic density and polarizability, resulting in a generic model without relying on an exhaustive training set.
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Hierarchical machine learning of potential energy surfaces [PDF]
The Journal of Chemical Physics, 2020 We present hierarchical machine learning (hML) of highly accurate potential energy surfaces (PESs). Our scheme is based on adding predictions of multiple Δ-machine learning models trained on energies and energy corrections calculated with a hierarchy of quantum chemical methods.
Dral, Pavlo O +3 more
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Ultra-fast interpretable machine-learning potentials
npj Computational Materials, 2023 AbstractAll-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic
Stephen R. Xie +2 more
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A New Physics-Inspired Discriminative Classifier [PDF]
AUT Journal of Electrical EngineeringConcepts and laws of physics have been a valuable source of inspiration for engineers to overcome human challenges and problems. Classification is an important example of such problems that play a major role in various fields of engineering sciences.
Mostafa Monemizadeh +3 more
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Machine-learning interatomic potential for W–Mo alloys [PDF]
Journal of Physics: Condensed Matter, 2021 Abstract In this work, we develop a machine-learning interatomic potential for W x Mo 1− x random alloys.
Giorgos Nikoulis +4 more
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National Science OpenNanoparticles, distinguished by their unique chemical and physical properties, have emerged as focal points within the realm of materials science. Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab ...
Gong Fu-Qiang, Xiong Ke, Cheng Jun
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