31 citations · 40 across the 5 of their papers we have counts for
5 papers
Structured force reformulation of many-body dispersion: towards effective atom--atom decomposition and surrogate modeling
Zhaoxiang Shen, Raúl I. Sosa, Stéphane P. A. Bordas +2
We present a structured force reformulation of the many-body dispersion (MBD) model that enables a physically consistent decomposition of forces into pairwise components. By introd…
Machine learning surrogate models of many-body dispersion interactions in polymer melts
Zhaoxiang Shen, Raúl I. Sosa, Jakub Lengiewicz +2
Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. H…
Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
Saurabh Deshpande, Hussein Rappel, Mark Hobbs +2
Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is st…
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease
Elisa Gómez de Lope, Saurabh Deshpande, Ramón Viñas Torné +3
Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical statistical and machine learning methods. Graph neur…
Quantum-informed simulations for mechanics of materials: DFTB+MBD framework
Zhaoxiang Shen, Raúl I. Sosa, Stéphane P. A. Bordas +2
The macroscopic behaviors of materials are determined by interactions that occur at multiple lengths and time scales. Depending on the application, describing, predicting, and unde…