Brett M. Savoie
Materials Science · Purdue University West Lafayette
Publications
265
Citations
6,444
Est. group size
~44
Recurring co-author estimate
Active years
15
Publishing since 2012
Brett M. Savoie's research uses computational chemistry and machine learning to understand and predict chemical reactivity, with applications spanning battery electrolytes, polymer recycling, organic electronics, and prebiotic chemistry. A major focus is developing automated tools and algorithms (such as reaction network exploration, machine-learned interatomic potentials, and self-driving laboratory workflows) to systematically map out chemical reaction spaces and predict molecular behavior faster than traditional trial-and-error methods. This work bridges materials discovery, physical chemistry, and artificial intelligence.
Publication output has grown substantially over the past decade, rising from single digits annually before 2020 to a peak of over 70 in 2023, and has remained at an elevated, though somewhat variable, level of 18-32 papers per year since.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Enhancing Polystyrene Circularity via Functionalized Weak Linkages
Macromolecules · 2026
- Fluoride Electrolyte Discovery via Reactivity Guided Genetic Algorithms
The Journal of Physical Chemistry B · 2026
- Kinetics overcome thermodynamics in primitive analogs of the reverse tricarboxylic acid cycle
Chemical Science · 2026
- Side-Chain Grafting Frequency Controls Trap Formation and Polaron Mobility in Organic Mixed Conductors
ChemRxiv · 2026
- Anion-Driven Solvent Degradation Networks in High-Concentration Battery Electrolytes
ChemRxiv · 2026
- Escaping Vibrational Purgatory: Hybrid kMC/MD Algorithms for Atomistic Simulations of Slow Reaction Chemistry
Journal of Chemical Theory and Computation · 2026
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms
Chemical Reviews · 2026
- OPENING THE LOOP: COMPOSABLE WORKFLOWS FOR SELF-DRIVING LABORATORIES VIA MULTI-AGENT REINFORCEMENT LEARNING
ChemRxiv · 2026
- Systematic Expansion of Chemical Reaction Space from Graph-Based Model Reactions
Figshare · 2026
- Systematic Expansion of Chemical Reaction Space from Graph-Based Model Reactions
Figshare · 2026
- Systematic Benchmarking Dataset Generation for Graphical Model Reaction Templates
Figshare · 2026
- Electron Alchemy with Machine-Learned Interatomic Potentials: Case Studies of Local Charge in Bond Dissociation Curves
Journal of Chemical Theory and Computation · 2026
- Systematic Expansion of Chemical Reaction Space from Graph-Based Model Reactions
ChemRxiv · 2026
- mol_prop_imputation-1.0.0
Zenodo (CERN European Organization for Nuclear Research) · 2026
- mol_prop_imputation-1.0.0
Zenodo (CERN European Organization for Nuclear Research) · 2026
- ChemRxiv×41
- The Cambridge Structural Database×39
- Figshare×15
- Bulletin of the American Physical Society×13
- Journal of the American Chemical Society×8
- Qiyuan Zhao
Materials Science · Purdue University West Lafayette
- Robert J. Appleton
Materials Science · Purdue University West Lafayette
- Veerupaksh Singla
Materials Science · Purdue University West Lafayette
- Frazier N. Baker
Materials Science · The Ohio State University
- Mustafa Kurban
Materials Science · Indiana University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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