Atomic Forces
9 papers with code • 0 benchmarks • 0 datasets
Predicion of the atomic forces, generally calculated with a quantum mechanical code (e.g. at DFT theory).
Benchmarks
These leaderboards are used to track progress in Atomic Forces
Most implemented papers
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation.
Machine Learning of Accurate Energy-conserving Molecular Force Fields
stefanch/sGDML
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Science Advances 2017
Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories.
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
stefanch/sGDML
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Computer Physics Communications 2019
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
Neural Network Based in Silico Simulation of Combustion Reactions
Through further development, the algorithms in this study can be used to explore and discovery reaction mechanisms of many complex reaction systems, such as combustion, synthesis, and heterogeneous catalysis without any predefined reaction coordinates and elementary reaction steps.
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with antigen residues (epitope).
Transfer learning for chemically accurate interatomic neural network potentials
zaverkin_v/gmnn
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This work studies the capability of transfer learning, in particular discriminative fine-tuning, for efficiently generating chemically accurate interatomic neural network potentials on organic molecules from the MD17 and ANI data sets.
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials.
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT).
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.
