1 citations · 3 across the 10 of their papers we have counts for
12 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…
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Eszter Varga-Umbrich, Zachary Weller-Davies, Paul Duckworth +3
Active learning for machine-learning interatomic potentials (MLIPs) must address several challenges to be practical: scaling to large candidate pools, leveraging energy-force super…
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Eszter Varga-Umbrich, Shikha Surana, Paul Duckworth +3
Training machine learning interatomic potentials (MLIPs) for reactive chemistry is often bottlenecked by the high cost of quantum chemical labels and the scarcity of transition sta…
Integral Formulas for Vector Signal Tensor Products
Valentin Heyraud, Zachary Weller-Davies, Jules Tilly
We derive integral formulas that simplify the Vector Signal Tensor Product recently introduced by Xie et al., which generalizes the Gaunt tensor product to anti-symmetric couplings…
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…
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…
Unified Framework for Matchgate Classical Shadows
Valentin Heyraud, Héloise Chomet, Jules Tilly
Estimating quantum fermionic properties is a computationally difficult yet crucial task for the study of electronic systems. Recent developments have begun to address this challeng…
Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit
Shuxiang Cao, Weixi Zhang, Jules Tilly +9
Qutrits, three-level quantum systems, have the advantage of potentially requiring fewer components than the typically used two-level qubits to construct equivalent quantum circuits…