Atomistic Simulations for Reactions and Spectroscopy in the Era of Machine Learning -- Quo Vadis?
Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas- and in the condensed phase. Together with recently developed and currently pursued efforts in integrating and combining this with machine learning techniques provides a unique opportunity to bring such dynamics simulations closer to reality. This perspective delineates the present status of the field from efforts of others in the field and some of your own work and discusses open questions and future prospects.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
On Simulating Thin-Film Processes at the Atomic Scale Using Machine Learned Force Fields
Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms of the processes but also to extract quantitative metrics on the events and reactions taking place at the gas-…
Coupled reaction and diffusion governing interface evolution in solid-state batteries
Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid state batteries. However, challenges per…
Active LearningModel-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights f…
Active LearningUncertainty QuantificationRepresenting local protein environments with atomistic foundation models
The local structure of a protein strongly impacts its function and interactions with other molecules. Therefore, a concise, informative representation of a local protein environment is essential for modeling and designin…
Accelerating a hybrid continuum-atomistic fluidic model with on-the-fly machine learning
We present a hybrid continuum-atomistic scheme which combines molecular dynamics (MD) simulations with on-the-fly machine learning techniques for the accurate and efficient prediction of multiscale fluidic systems. By us…
Bayesian InferenceBIG-bench Machine Learning