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Toward transferable empirical valence bonds: Making classical force fields reactive
Free accessAmerican Institute of Physics | 2024| -
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
American Chemical Society | 2024| -
Supervision in a changing world: Reflections from child psychotherapy
Taylor & Francis Verlag | 2023| -
Machine learning potentials with Iterative Boltzmann Inversion: training to experiment
Free accessArXiv | 2023| -
Molecular dynamics of high pressure tin phases: Empirical and machine learned interatomic potentials
American Institute of Physics | 2023| -
Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective
Free accessArXiv | 2022| -
Fault tolerance for power loss imminent circuitry failure
Free accessEuropean Patent Office | 2021| -
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE
Free accessAmerican Chemical Society | 2021| -
Modelling flexible protein–ligand binding in p38α MAP kinase using the QUBE force field
Royal Society of Chemistry | 2020| -
Nine Best Practices for Research Software Registries and Repositories: A Concise Guide
Free accessArXiv | 2020|
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