4 papers
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…
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…
Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad
Jonathan Warrell, Francesco Alesiani, Cameron Smith +2
Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas.…