MDLab: AI frameworks for Carbon Capture and Battery MaterialsBruce ElmegreenHendrik Hamannet al.2025Frontiers in Environmental Science
Extracting Electrolyte Design from Interpretable Data-Driven MethodsVidushi SharmaMaxwell Giammonaet al.2025ACS Spring 2025
Quantum Computer-Enhanced Surface Reaction Simulations for Battery Materials through Sample-Based Quantum Diagonalization and Local EmbeddingMarco Antonio Guimaraes Auad BarrocaRodrigo Neumann Barros Ferreiraet al.2025APS Global Physics Summit 2025
Improving electrolyte performance for target cathode loading using an interpretable data-driven approachVidushi SharmaAndy Teket al.2024Cell Reports Physical Science
Surface reaction calculations with quantum computers for battery materialsMarco Antonio Guimaraes Auad BarrocaRodrigo Neumann Barros Ferreiraet al.2024ACS Fall 2024
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation ModelIndra Priyadarsini SVidushi Sharmaet al.2024ICML 2024
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation ModelIndra Priyadarsini SVidushi Sharmaet al.2024arXiv
Capturing Formulation Design of Battery Electrolytes with Chemical Large Language ModelEduardo Almeida SoaresVidushi Sharmaet al.2023NeurIPS 2023
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device PerformanceVidushi SharmaMaxwell Giammonaet al.2023J. Chem. Inf. Model.
Predictive Supremacy of Chemical Foundational Model for Battery ElectrolytesVidushi SharmaEduardo Almeida Soareset al.2023MRS Fall Meeting 2023