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Found 13 papers, 8 papers with code
From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields
Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations.
Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
Kernel machines have sustained continuous progress in the field of quantum chemistry.
Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields
Reconstructing force fields (FFs) from atomistic simulation data is a challenge since accurate data can be highly expensive.
BIGDML: Towards Exact Machine Learning Force Fields for Materials
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof.
Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems
Attention mechanisms are developing into a viable alternative to convolutional layers as elementary building block of NNs.
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields.
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 traditional electronic-structure methods.
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective coarse-grained model.
Local Function Complexity for Active Learning via Mixture of Gaussian Processes
Inhomogeneities in real-world data, e. g., due to changes in the observation noise level or variations in the structural complexity of the source function, pose a unique set of challenges for statistical inference.
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
The analysis of sGDML molecular dynamics trajectories yields new qualitative insights into dynamics and spectroscopy of small molecules close to spectroscopic accuracy.
Chemical Physics Computational Physics Data Analysis, Statistics and Probability
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model.
Computational Physics
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science.
Chemical Physics
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
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