107 citations · 417 across the 22 of their papers we have counts for
23 papers
FlashCart: Fast Cartesian Tensor Products for Equivariant Interatomic Potentials
Viktor Zaverkin, Payman Goodarzi, Sergey V. Sukhomlinov +4
Machine-learned interatomic potentials extend atomistic simulations beyond the length- and timescales accessible to electronic-structure methods. However, the computational cost of…
On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists
Seungone Kim, Dongkeun Yoon, Kiril Gashteovski +55
With the advancement of AI capabilities, AI reviewers are beginning to be deployed in scientific peer review, yet their capability and credibility remain in question: many scientis…
A Leibniz rule of distributional pairing and hyperforce sum rule
Takashi Maruyama, Tatsuki Seto, Viktor Zaverkin +1
We reformulate and generalize the equilibrium hyperforce sum rule, a generalization of the Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy, by employing the Schwartz space an…
Performance of universal machine-learned potentials with explicit long-range interactions in biomolecular simulations
Viktor Zaverkin, Matheus Ferraz, Francesco Alesiani +1
Universal machine-learned potentials promise transferable accuracy across compositional and vibrational degrees of freedom, yet their application to biomolecular simulations remain…
Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations
Henrik Christiansen, Takashi Maruyama, Federico Errica +3
We present an end-to-end differentiable molecular simulation framework (DIMOS) for molecular dynamics and Monte Carlo simulations. DIMOS easily integrates machine-learning-based in…
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca +9
While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and rele…