Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

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Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Chattez avec Graph Search

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

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Solvation Free Energies from Machine Learning Molecular Dynamics

Live-cell imaging powered by computation

Stochastic pairwise preference convergence in Bayesian agents

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Optimality principles for the analysis of biological processes across scales: From enzyme kinetics to microbiome dynamics

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A Combination Technique for Optimal Control Problems Constrained by Random PDEs

Computational Studies on the Stability of Organic-Inorganic Hybrid Halide Lead Perovskites

Charge-Transfer States in Organic Nanowires

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Computational drug development for membrane protein targets

Hausdorff dimension of fermions on a random lattice

EncodingActs: Modeling, Representing and Transmitting Embodied Knowledge in Traditional Martial Arts

Surface segregation in high-entropy alloys from alchemical machine learning

Glenohumeral joint force prediction with deep learning