Adaptive MFML algorithm saturates accuracy at low fidelities before escalating, cutting data costs up to 30x vs single-fidelity and 5x vs standard MFML on coupled cluster and excitation energies.
TorchANI: A free and open source PyTorch-based deep learning implementation of t he ANI neural network potentials
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Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.
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Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
Adaptive MFML algorithm saturates accuracy at low fidelities before escalating, cutting data costs up to 30x vs single-fidelity and 5x vs standard MFML on coupled cluster and excitation energies.
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Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Bayesian E(3)-equivariant MLPs with joint energy-force NLL loss achieve competitive accuracy while enabling uncertainty-guided active learning, OOD detection, and calibration.