Cargando…
Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systems
Electronic structure methods offer in principle accurate predictions of molecular properties, however, their applicability is limited by computational costs. Empirical methods are cheaper, but come with inherent approximations and are dependent on the quality and quantity of training data. The rise...
Autores principales: | Thürlemann, Moritz, Riniker, Sereina |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10646964/ https://www.ncbi.nlm.nih.gov/pubmed/38020395 http://dx.doi.org/10.1039/d3sc04317g |
Ejemplares similares
-
Regularized by Physics: Graph Neural Network Parametrized
Potentials for the Description of Intermolecular Interactions
por: Thürlemann, Moritz, et al.
Publicado: (2023) -
Similarity maps - a visualization strategy for molecular fingerprints and machine-learning methods
por: Riniker, Sereina, et al.
Publicado: (2013) -
Force-field functor theory: classical force-fields which reproduce equilibrium quantum distributions
por: Babbush, Ryan, et al.
Publicado: (2013) -
Enhanced sampling without borders: on global biasing functions and how to reweight them
por: Kamenik, Anna S., et al.
Publicado: (2021) -
Machine learning of accurate energy-conserving molecular force fields
por: Chmiela, Stefan, et al.
Publicado: (2017)