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.
P.et al.MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.J
5 Pith papers cite this work, alongside 160 external citations. Polarity classification is still indexing.
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2026 5representative citing papers
SKMD adapts Stein variational gradient descent into molecular dynamics with asynchronous updates and global atomic descriptor kernels to acquire non-redundant training configurations while preserving the Boltzmann distribution, yielding higher MLIP accuracy with fewer samples than baselines.
A physics-plus-AI pipeline recovers crystal structures from powder X-ray diffraction on most of a hard benchmark set, but success drops sharply for low-symmetry cases.
QCOF ML potentials tuned on COF data outperform general MACE models for defective systems and reveal higher thermal defect sensitivity in CTF-1 versus COF-LZU1 with nearly invariant low-strain mechanics.
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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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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Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
SKMD adapts Stein variational gradient descent into molecular dynamics with asynchronous updates and global atomic descriptor kernels to acquire non-redundant training configurations while preserving the Boltzmann distribution, yielding higher MLIP accuracy with fewer samples than baselines.
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Ab-initio Crystal Structure Determination from Powder X-Ray Diffraction
A physics-plus-AI pipeline recovers crystal structures from powder X-ray diffraction on most of a hard benchmark set, but success drops sharply for low-symmetry cases.
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Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks
QCOF ML potentials tuned on COF data outperform general MACE models for defective systems and reveal higher thermal defect sensitivity in CTF-1 versus COF-LZU1 with nearly invariant low-strain mechanics.
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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.