Read original ↗
paperarXivTrust 82 · PrimaryPublished 7d agoLive · 2d ago

Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applications, such as vibrational analysis and transition state search, rely heavily on the PES Hessian. Yet, standard MLIPs tend to be trained on energy and forces alone, leaving Hessian information largely unexploited. Meanwhile, existing methods that explicitly incorporate the Hessian into training objectives require architectural modifications and introduce significant computational and memory overheads due to higher-order backpropagation. To add

Lineage graph

Paper → model → repo connections mined from source citations (Tier-1 exact match).

Why these links exist

Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.

Covers

newsVibrational power spectra as a tool to benchmark universal machine-learning interatomic potentials for molecular systems: the OMOL-1k-MD data set - NaturenewsBridging three-dimensional molecular structures and artificial intelligence with a conformation description languagenewsReshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1

Implements (incoming)

repoamitness/learningrepoDataTalksClub/machine-learning-zoomcamprepostefan-jansen/machine-learning-for-tradingrepoEthicalML/awesome-production-machine-learningreporasbt/python-machine-learning-book

Covers (incoming)

newsDeep-learning model maps ion binding sites in protein structures - Wiley Analytical SciencenewsMachine learning boosts first-principles study of perovskite photovoltaic properties - Bioengineer.org

authored (incoming)

personBumju KwakpersonJeonghee Jo

Related across the graph

repostefan-jansen/machine-learning-for-tradingrepoamitness/learningreporasbt/python-machine-learning-booknewsMachine learning boosts first-principles study of perovskite photovoltaic properties - Bioengineer.orgnewsReshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1newsVibrational power spectra as a tool to benchmark universal machine-learning interatomic potentials for molecular systems: the OMOL-1k-MD data set - NaturenewsDeep-learning model maps ion binding sites in protein structures - Wiley Analytical SciencepersonBumju KwaknewsBridging three-dimensional molecular structures and artificial intelligence with a conformation description languagerepoDataTalksClub/machine-learning-zoomcamppersonJeonghee JorepoEthicalML/awesome-production-machine-learning

Topics

cs.LG