Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
Unke, Stefan Chmiela, Michael Gastegger, Kristof T
4 Pith papers cite this work, alongside 321 external citations. Polarity classification is still indexing.
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Enerzyme framework trains electrostatics-aware NNPs on under 1,000 system-specific points to reproduce MTase reaction energetics and transition states for clusters up to 545 atoms.
Loss-guided adaptive scale refinement on NaCl aqueous system reduces overall force MAE from 399.65 to 381.23 by discovering intermediate scales from initial anchors.
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.
citing papers explorer
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Generative Pseudo-Force Fields for Molecular Generation
Proposes generative pseudo-force fields trained on quadratic pseudo-potentials from noisy equilibria as a time-step-agnostic diffusion variant for efficient molecular conformation generation with high validity on QM9.
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Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases
Enerzyme framework trains electrostatics-aware NNPs on under 1,000 system-specific points to reproduce MTase reaction energetics and transition states for clusters up to 545 atoms.
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Loss-Guided Adaptive Scale Refinement for Molecular Force Prediction
Loss-guided adaptive scale refinement on NaCl aqueous system reduces overall force MAE from 399.65 to 381.23 by discovering intermediate scales from initial anchors.
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Six Open Questions in Machine-Learned Interatomic Potential Foundation Models
This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.