60 citations · 295 across the 19 of their papers we have counts for
19 papers
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…
A one-world interpretation of quantum mechanics
Isaac Layton, Jonathan Oppenheim, Zachary Weller-Davies
The measurement problem is the issue of explaining how the objective classical world emerges from a quantum one. Here we take a different approach. We assume that there is an objec…
Quantum gravity with dynamical wave-function collapse via a classical scalar field
Zachary Weller-Davies
In hybrid classical-quantum theories, the dynamics of the classical system induce the classicality of the quantum system, meaning that such models do not necessarily require a meas…