Machine learning potentials can exceed training accuracy, researchers propose

A new perspective paper argues that machine learning potentials (MLPs) can surpass the accuracy of the methods used to train them. The authors propose that by generalizing across different quantum-chemical methods, MLPs can achieve higher accuracy than their training data, even without relying on experimental observables. This approach could lead to more accurate universal MLPs and reshape the construction and evaluation of training datasets for chemical simulations. AI

IMPACT Proposes a new methodology for training MLPs that could enhance accuracy in chemical simulations.

RANK_REASON The cluster contains a research paper published on arXiv discussing novel methods for machine learning potentials. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• Chemical simulations of prebiotic molecules: interstellar ethanimine isomers
• DagsHub
• density functional theory
• Gotit.pub
• Hugging Face
• Iterative Boltzmann Inversion
• Quantum-chemical methods
• ScienceCast
• Wavefunction-based theories

• paper
• model release

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