4 papers
Fine-tuning MLIP foundation models: strategies for accuracy and transferability
Tamás Lajos Tompa, Eszter Varga-Umbrich, Ilyes Batatia +3
Adapting machine-learned interatomic potential (MLIP) foundation models to specialised tasks through fine-tuning is an increasingly important practice, yet systematic guidance on w…
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…