Machine learning force fields
In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …
numerous advances previously out of reach due to the computational complexity of …
Polarizable force fields for biomolecular simulations: Recent advances and applications
Realistic modeling of biomolecular systems requires an accurate treatment of electrostatics,
including electronic polarization. Due to recent advances in physical models, simulation …
including electronic polarization. Due to recent advances in physical models, simulation …
Quantum machine learning for chemistry and physics
Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …
pertinent patterns within a given data set with the objective of subsequent generation of …
CHARMM at 45: Enhancements in accessibility, functionality, and speed
Since its inception nearly a half century ago, CHARMM has been playing a central role in
computational biochemistry and biophysics. Commensurate with the developments in …
computational biochemistry and biophysics. Commensurate with the developments in …
Universal QM/MM approaches for general nanoscale applications
Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address
chemical phenomena in complex molecular environments. Whereas this modeling approach …
chemical phenomena in complex molecular environments. Whereas this modeling approach …
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 …
QUBEKit: Automating the derivation of force field parameters from quantum mechanics
Modern molecular mechanics force fields are widely used for modeling the dynamics and
interactions of small organic molecules using libraries of transferable force field parameters …
interactions of small organic molecules using libraries of transferable force field parameters …
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 …
Streamlining and Optimizing Strategies of Electrostatic Parameterization
Accurate characterization of electrostatic interactions is crucial in molecular simulation.
Various methods and programs have been developed to obtain electrostatic parameters for …
Various methods and programs have been developed to obtain electrostatic parameters for …
Systematic improvement of empirical energy functions in the era of machine learning
The impact of targeted replacement of individual terms in empirical force fields is
quantitatively assessed for pure water, dichloromethane (CH 2 _2 Cl 2 _2), and solvated …
quantitatively assessed for pure water, dichloromethane (CH 2 _2 Cl 2 _2), and solvated …