Machine learning force fields (MLFFs) have gained popularity in recent years as they provide a cost-effective alternative to ab initio molecular dynamics (MD) simulations. Despite a small error on the test set, MLFFs inherently suffer from generalizati...
Machine learning for molecular simulation. Ann. Rev. Phys. Chem. 71, 361–390 (2020). Article Google Scholar Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142–10186 (2021). Article Google Scholar Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals...
Molecular Simulation Meets Machine LearningAuthor(s): Richard J. Sadus 1 Publication date (Electronic): December 19 2023 Journal: Journal of Chemical & Engineering Data Publisher: American Chemical Society (ACS) Read this article at ScienceOpenPublisher...
In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-th...
We developed a simulation methodology on the basis of machine learning techniques for simulation of pharmaceutical solubility in a supercritical solvent, i.e., CO2 with the perspective of nanodrug production. The X variables considered in this simulation work included pressure and temperature of the ...
Molecular dynamics Theory and computation This article is cited by An interpretable DeePMD-kit performance model for emerging supercomputers Xiangyu Meng Xun Wang Weile Jia CCF Transactions on High Performance Computing(2025) Machine Learning Guided Insights into the Effects of Nb/Ta and Ti/Ta Ratios ...
Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since extended defects—governing plasticity and crack nucleation...
We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in ...
Using a Machine Learning (ML), Deep Learning (DL), and High Throughput (HT) computing techniques can provide an efficient robust data processing platform for the prediction and discovery of new materials. ML techniques involve ...
Machine learning for the discovery of molecular recognition based on single-walled carbon nanotube corona-phases Xun Gong, Nicholas Renegar, Retsef Levi & Michael S. Strano npj Computational Materials volume 8, Article number: 135 (2022) Cite this article 3766 Accesses 1 Altmetric Metrics details...