首页|Swiss Federal Institute of Technology Lausanne (EPFL) Reports Findings in Machin e Learning (Solvation Free Energies from Machine Learning Molecular Dynamics)
Swiss Federal Institute of Technology Lausanne (EPFL) Reports Findings in Machin e Learning (Solvation Free Energies from Machine Learning Molecular Dynamics)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Machine Learning is the subject o f a report. According to news reporting from Lausanne, Switzerland, by NewsRx jo urnalists, research stated, “The present work proposes an extension to the appro ach of [Xi, C; et al. 6878] to calculate i on solvation free energies from first-principles (FP) molecular dynamics (MD) si mulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation.” The news correspondents obtained a quote from the research from the Swiss Federa l Institute of Technology Lausanne (EPFL), “Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus i mprove the convergence of statistical averages at a fraction of the original com putational cost, a machine-learned (ML) energy function is trained on FP energie s and forces and used in the MD simulations. The ML workflow and MD simulation t imes ( 200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV.”
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