Picture for Alexandre Tkatchenko

Alexandre Tkatchenko

Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling

Add code
Oct 10, 2024
Viaarxiv icon

Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective

Add code
Nov 21, 2022
Viaarxiv icon

Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations

Add code
May 17, 2022
Figure 1 for Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Figure 2 for Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Figure 3 for Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Figure 4 for Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Viaarxiv icon

BIGDML: Towards Exact Machine Learning Force Fields for Materials

Add code
Jun 08, 2021
Figure 1 for BIGDML: Towards Exact Machine Learning Force Fields for Materials
Figure 2 for BIGDML: Towards Exact Machine Learning Force Fields for Materials
Figure 3 for BIGDML: Towards Exact Machine Learning Force Fields for Materials
Figure 4 for BIGDML: Towards Exact Machine Learning Force Fields for Materials
Viaarxiv icon

Machine Learning Force Fields

Add code
Oct 14, 2020
Figure 1 for Machine Learning Force Fields
Figure 2 for Machine Learning Force Fields
Figure 3 for Machine Learning Force Fields
Figure 4 for Machine Learning Force Fields
Viaarxiv icon

Machine learning for molecular simulation

Add code
Nov 07, 2019
Figure 1 for Machine learning for molecular simulation
Figure 2 for Machine learning for molecular simulation
Figure 3 for Machine learning for molecular simulation
Figure 4 for Machine learning for molecular simulation
Viaarxiv icon

Learning representations of molecules and materials with atomistic neural networks

Add code
Dec 11, 2018
Figure 1 for Learning representations of molecules and materials with atomistic neural networks
Figure 2 for Learning representations of molecules and materials with atomistic neural networks
Figure 3 for Learning representations of molecules and materials with atomistic neural networks
Figure 4 for Learning representations of molecules and materials with atomistic neural networks
Viaarxiv icon

Quantum-chemical insights from interpretable atomistic neural networks

Add code
Jun 27, 2018
Figure 1 for Quantum-chemical insights from interpretable atomistic neural networks
Figure 2 for Quantum-chemical insights from interpretable atomistic neural networks
Figure 3 for Quantum-chemical insights from interpretable atomistic neural networks
Figure 4 for Quantum-chemical insights from interpretable atomistic neural networks
Viaarxiv icon

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Add code
Dec 19, 2017
Figure 1 for SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Figure 2 for SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Figure 3 for SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Figure 4 for SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Viaarxiv icon

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Add code
Sep 12, 2011
Figure 1 for Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Figure 2 for Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Figure 3 for Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Viaarxiv icon