2.3k citations · 6.8k across the 20 of their papers we have counts for
12 papers · 1 filter
Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement
Zuriel Y. Yescas-Ramos, Andrés Álvarez-García, Huziel E. Sauceda
We present \textsc{dm-PhiSNet}, a physically constrained \textsc{PhiSNet}-based equivariant model that predicts one-electron reduced density matrices (1-RDMs) directly from molecul…
Delta-learned force fields for nonbonded interactions: Addressing the strength mismatch between covalent-nonbonded interaction for global models
Leonardo Cázares-Trejo, Marco Loreto-Silva, Huziel E. Sauceda
Noncovalent interactions--vdW dispersion, hydrogen/halogen bonding, ion-, and -stacking--govern structure, dynamics, and emergent phenomena in materials and molecular systems…
Accurate global machine learning force fields for molecules with hundreds of atoms
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke +4
Global machine learning force fields (MLFFs), that have the capacity to capture collective many-atom interactions in molecular systems, currently only scale up to a few dozen atoms…
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke, Stefan Chmiela, Michael Gastegger +3
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current ML-FFs typically ignore electroni…
Machine Learning Force Fields
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda +5
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 traditiona…
Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela +2
Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level methods, but at a much lower computational cos…
Dynamical Strengthening of Covalent and Non-Covalent Molecular Interactions by Nuclear Quantum Effects at Finite Temperature
Huziel E. Sauceda, Valentin Vassilev-Galindo, Stefan Chmiela +2
Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the inclusion of the zero point energy and its coupling with the anharmonicities in interatomic…
Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky +2
Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields…
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky +2
We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) app…
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller +1
Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. How…
SchNet - a deep learning architecture for molecules and materials
Kristof T. Schütt, Huziel E. Sauceda, Pieter-Jan Kindermans +2
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in ch…
Machine Learning of Accurate Energy-Conserving Molecular Force Fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E. Sauceda +3
Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach…