51 citations · 56 across the 12 of their papers we have counts for
13 papers
Logical Guidance of Stochastic Interpolants
Gerrit Gerhartz, Francesco Alesiani, Jonathan Warrell +3
Guidance and composition of generative models have applications in visual design, chemistry, and biology. Logical guidance composes conditional scores exactly for diffusion models.…
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…
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…
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…
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…
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…
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…
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…
Geometric Kolmogorov-Arnold Superposition Theorem
Francesco Alesiani, Takashi Maruyama, Henrik Christiansen +1
The Kolmogorov-Arnold Theorem (KAT), or more generally, the Kolmogorov Superposition Theorem (KST), establishes that any non-linear multivariate function can be exactly represented…
Adaptive Width Neural Networks
Federico Errica, Henrik Christiansen, Viktor Zaverkin +2
For almost 70 years, researchers have typically selected the width of neural networks' layers either manually or through automated hyperparameter tuning methods such as grid search…
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
Viktor Zaverkin, Francesco Alesiani, Takashi Maruyama +5
The ability to perform fast and accurate atomistic simulations is crucial for advancing the chemical sciences. By learning from high-quality data, machine-learned interatomic poten…
Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching
Federico Errica, Henrik Christiansen, Viktor Zaverkin +3
Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is…
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Viktor Zaverkin, David Holzmüller, Henrik Christiansen +5
Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses bias…