首页|Findings on Machine Learning Detailed by Investigators at Sorbonne University (E fficient Machine Learning Approach for Accurate Freeenergy Profiles and Kinetic Rates)
Findings on Machine Learning Detailed by Investigators at Sorbonne University (E fficient Machine Learning Approach for Accurate Freeenergy Profiles and Kinetic Rates)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting from Paris, France, by News Rx journalists, research stated, "The computational exploration of reactive proc esses is challenging due to the requirement of thorough sampling across the free energy landscape using accurate ab initio methods." The news correspondents obtained a quote from the research from Sorbonne Univers ity, "To address these constraints, machine learning potentials are employed, ye t their training for this kind of problem is still a laborious and tedious task. In this study, we present an efficient approach to train these potentials by cl everly using a single batch of unbiased trajectories that avoid the pitfalls of trajectories artificially biased along a suboptimal collective variable."
ParisFranceEuropeCyborgsEmerging TechnologiesMachine LearningSorbonne University
NETL
NSTL
