[HTML][HTML] Toward empirical force fields that match experimental observables
Biomolecular force fields have been traditionally derived based on a mixture of reference
quantum chemistry data and experimental information obtained on small fragments …
quantum chemistry data and experimental information obtained on small fragments …
[HTML][HTML] Empirical optimization of molecular simulation force fields by Bayesian inference
J Köfinger, G Hummer - The European Physical Journal B, 2021 - Springer
The demands on the accuracy of force fields for classical molecular dynamics simulations
are steadily growing as larger and more complex systems are studied over longer times …
are steadily growing as larger and more complex systems are studied over longer times …
Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
Machine learning encompasses tools and algorithms that are now becoming popular in
almost all scientific and technological fields. This is true for molecular dynamics as well …
almost all scientific and technological fields. This is true for molecular dynamics as well …
Accurate machine learned quantum-mechanical force fields for biomolecular simulations
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological
processes. Accurate MD simulations require computationally demanding quantum …
processes. Accurate MD simulations require computationally demanding quantum …
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …
[HTML][HTML] Using the maximum entropy principle to combine simulations and solution experiments
Molecular dynamics (MD) simulations allow the investigation of the structural dynamics of
biomolecular systems with unrivaled time and space resolution. However, in order to …
biomolecular systems with unrivaled time and space resolution. However, in order to …
Machine learning force fields: Recent advances and remaining challenges
I Poltavsky, A Tkatchenko - The journal of physical chemistry …, 2021 - ACS Publications
In chemistry and physics, machine learning (ML) methods promise transformative impacts by
advancing modeling and improving our understanding of complex molecules and materials …
advancing modeling and improving our understanding of complex molecules and materials …
A hierarchical Bayesian framework for force field selection in molecular dynamics simulations
S Wu, P Angelikopoulos… - … of the Royal …, 2016 - royalsocietypublishing.org
We present a hierarchical Bayesian framework for the selection of force fields in molecular
dynamics (MD) simulations. The framework associates the variability of the optimal …
dynamics (MD) simulations. The framework associates the variability of the optimal …
Open force field evaluator: An automated, efficient, and scalable framework for the estimation of physical properties from molecular simulation
Developing accurate classical force field representations of molecules is key to realizing the
full potential of molecular simulations, both as a powerful route to gaining fundamental …
full potential of molecular simulations, both as a powerful route to gaining fundamental …
Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning
The training set of atomic configurations is key to the performance of any Machine Learning
Force Field (MLFF) and, as such, the training set selection determines the applicability of the …
Force Field (MLFF) and, as such, the training set selection determines the applicability of the …