1 citations · 1 across the 7 of their papers we have counts for
9 papers
E3J: An Efficient and Open-Source Backend for Euclidean Equivariant Operations on GPU and TPU
Olivier Peltre, Armand Picard, Adrien Pichard +6
We present e3j, a fast Euclid-equivariance backend for geometric deep learning applications with JAX bindings for GPU and TPU. Leveraging both optimized CUDA and Pallas kernels and…
ALF: An Active Learning Framework for Scientific Discovery
Shikha Surana, Alex Hawkins-Hooker, Olivia Gallup +3
Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongst the most promising ways to…
ChemReporter: A Framework for Curating and Exporting Large-Scale Chemical Datasets for MLIP Training
Marie Bluntzer, Jules Tilly, Christoph Brunken
Training set quality and diversity are key determinants of the reliability of machine learning interatomic potentials (MLIPs), yet using massive datasets in full is often impractic…
Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation
Christoph Brunken, Titouan Cormier, Lucien Walewski +15
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near ab initio accuracy at significantly reduced computational cost, but their broader adoption is…
MLIPAudit: A benchmarking tool for Machine Learned Interatomic Potentials
Leon Wehrhan, Lucien Walewski, Marie Bluntzer +4
Machine-learned interatomic potentials (MLIPs) promise to significantly advance atomistic simulations by delivering quantum-level accuracy for large molecular systems at a fraction…
Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems
Christoph Brunken, Olivier Peltre, Heloise Chomet +11
Machine Learning Interatomic Potentials (MLIP) are a novel in silico approach for molecular property prediction, creating an alternative to disrupt the accuracy/speed trade-off of…
Universally applicable and tunable graph-based coarse-graining for Machine learning force fields
Christoph Brunken, Sebastien Boyer, Mustafa Omar +7
Coarse-grained (CG) force field methods for molecular systems are a crucial tool to simulate large biological macromolecules and are therefore essential for characterisations of bi…
BoostMD: Accelerating molecular sampling by leveraging ML force field features from previous time-steps
Lars L. Schaaf, Ilyes Batatia, Christoph Brunken +2
Simulating atomic-scale processes, such as protein dynamics and catalytic reactions, is crucial for advancements in biology, chemistry, and materials science. Machine learning forc…
Protein binding affinity prediction under multiple substitutions applying eGNNs on Residue and Atomic graphs combined with Language model information: eGRAL
Arturo Fiorellini-Bernardis, Sebastien Boyer, Christoph Brunken +4
Protein-protein interactions (PPIs) play a crucial role in numerous biological processes. Developing methods that predict binding affinity changes under substitution mutations is f…