activity
20232026
most citedFast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations

4 citations · 4 across the 9 of their papers we have counts for

collaborators

12 papers

physics.comp-ph2026

BU-MBAR: A hybrid solution strategy for the MBAR equations

Fabio Müller, Francesco Alesiani, Henrik Christiansen

The multi-state Bennett acceptance ratio (MBAR) equations combine the data collected under different thermodynamic conditions in a statistically optimal way. Due to their practical…

physics.comp-ph2026

Hyperspatial Sampling: Circumventing Free-Energy Barriers via Replica Exchange with Extra Dimensions

Henrik Christiansen, Matheus Ferraz, Takashi Maruyama +1

Simulating systems with rugged free-energy landscapes remains a central challenge in computational physics and chemistry. We introduce hyperspatial replica exchange (HS-REX), an en…

cs.CL2026

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…

cs.LG2026

Logical Guidance for the Exact Composition of Diffusion Models

Francesco Alesiani, Jonathan Warrell, Tanja Bien +3

We propose LOGDIFF (Logical Guidance for the Exact Composition of Diffusion Models), a guidance framework for diffusion models that enables principled constrained generation with c…

math-ph2026

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…

cs.LG2025

Variational Kolmogorov-Arnold Network

Francesco Alesiani, Henrik Christiansen, Federico Errica

Kolmogorov-Arnold Networks (KANs) offer a theoretically grounded alternative to multi-layer perceptrons by representing multivariate functions as compositions of univariate basis f…