Extracting Electrolyte Design from Interpretable Data-Driven Methods
- 2025
- ACS Spring 2025
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12 results for Max Giammona
Quantum Computer-Enhanced Surface Reaction Simulations for Battery Materials through Sample-Based Quantum Diagonalization and Local Embedding
- 2025
- APS Global Physics Summit 2025
Improving electrolyte performance for target cathode loading using an interpretable data-driven approach
- Vidushi Sharma
- Andy Tek
- et al.
- 2024
- Cell Reports Physical Science
KnowledgeHub: An end-to-end Tool for Assisted Scientific Discovery
- 2024
- IJCAI 2024
Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance
- 2023
- J. Chem. Inf. Model.
Understanding the role of mass transport and lithium interphase design in enabling conversion batteries
- 2023
- ACS Fall 2023
AI-directed discovery of high entropy electrolyte formulations for batteries using interhalogen cathodes and lithium metal anodes
- 2023
- ACS Fall 2023
Accelerated Electrolyte Discovery using Data Driven Approach
- 2023
- ACS Fall 2023
Going beyond High Throughput Screening towards AI in Accelerated Electrolyte Discovery
- 2022
- MRS Fall Meeting 2022
Transfer Learning Enabled Deep Learning Model for the Prediction of Battery Performance from Electrolyte Formulations
- 2022
- ECS Meeting 2022