MANDREL: Modular Reinforcement Learning Pipelines for Material Discovery
- Clyde Fare
- George K. Holt
- et al.
- 2024
- AAAI 2024
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4 results for Clyde Fare
A multi-fidelity machine learning approach to high throughput materials screening
- Clyde Fare
- Peter Fenner
- et al.
- 2022
- npj Computational Materials
Quantum-centric supercomputing for materials science: A perspective on challenges and future directions
- Yuri Alexeev
- Maximilian Amsler
- et al.
- 2024
- Future Generation Computer Systems
History Matching Reservoir Models With Many Objective Bayesian Optimization
- Steven Samoil
- Clyde Fare
- et al.
- 2024
- Appl. AI Lett.