102 citations · 281 across the 11 of their papers we have counts for
11 papers
FEAT: Free energy Estimators with Adaptive Transport
Jiajun He, Yuanqi Du, Francisco Vargas +4
We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages lea…
Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design -- A Perspective
Yuzhi Xu, Haowei Ni, Qinhui Gao +10
Computational molecular design -- the endeavor to design molecules, with various missions, aided by machine learning and molecular dynamics approaches, has been widely applied to c…
On the design space between molecular mechanics and machine learning force fields
Yuanqing Wang, Kenichiro Takaba, Michael S. Chen +14
A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully e…
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez +22
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use…
Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
Kenichiro Takaba, Iván Pulido, Pavan Kumar Behara +11
The development of reliable and extensible molecular mechanics (MM) force fields -- fast, empirical models characterizing the potential energy surface of molecular systems -- is in…
EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment
Yuanqing Wang, Iván Pulido, Kenichiro Takaba +4
Atomic partial charges are crucial parameters in molecular dynamics (MD) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby the potential…
Spatial Attention Kinetic Networks with E(n)-Equivariance
Yuanqing Wang, John D. Chodera
Neural networks that are equivariant to rotations, translations, reflections, and permutations on n-dimensional geometric space have shown promise in physical modeling for tasks su…
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
Peter Eastman, Pavan Kumar Behara, David L. Dotson +9
Machine learning potentials are an important tool for molecular simulation, but their development is held back by a shortage of high quality datasets to train them on. We describe…
Stochastic Aggregation in Graph Neural Networks
Yuanqing Wang, Theofanis Karaletsos
Graph neural networks (GNNs) manifest pathologies including over-smoothing and limited discriminating power as a result of suboptimally expressive aggregating mechanisms. We herein…
End-to-End Differentiable Molecular Mechanics Force Field Construction
Yuanqing Wang, Josh Fass, Benjamin Kaminow +6
Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of appli…
Graph Nets for Partial Charge Prediction
Yuanqing Wang, Josh Fass, Chaya D. Stern +2
Atomic partial charges are crucial parameters for Molecular Dynamics (MD) simulations, molecular mechanics calculations, and virtual screening, as they determine the electrostatic…