In recent years, machine learning-based potentials (MLPs) have emerged as powerful surrogates for existing modelling techniques, representing complex potential energy surfaces (PES) with an accuracy comparable to QM methods but at a significantly lower computational cost9,10.
Jul 19, 2024
Jul 2, 2024 · Machine learning is capable of effectively predicting the potential energies of molecules in the presence of high-quality data sets.
Jul 17, 2024 · The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material ...
7 days ago · In this work, we propose a hybrid framework of machine learning potentials that is capable of simulating metal/electrolyte interfaces by unifying the ...
2 days ago · Machine-learning interatomic potential (MLIP) utilizes ML techniques to reproduce the potential energy surfaces of atomistic systems by training on a ...
2 days ago · We apply the environment-adaptive machine learning potentials to predict observable properties for Ta element and InP compound, and compare them with density ...
Jul 11, 2024 · Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the ...
Jul 9, 2024 · Machine learning is a subfield of artificial intelligence focused on developing computers that learn similarly to humans. Through the use of algorithms that ...
Jul 2, 2024 · We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic ...
Transition State Searching Accelerated by Deep Learning ...
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2 days ago · In this study, we conducted a performance comparison of deep potential for molecular dynamics (DeePMD), recursively embedded atom neural network (REANN), and ...