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2020 – today
- 2024
[i6]Daniel Schwalbe-Koda, Sébastien Hamel, Babak Sadigh, Fei Zhou, Vincenzo Lordi:
Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics. CoRR abs/2404.12367 (2024)- 2023
- [j1]Joshua A. Vita
, Daniel Schwalbe-Koda:
Data efficiency and extrapolation trends in neural network interatomic potentials. Mach. Learn. Sci. Technol. 4(3): 35031 (2023) - [i5]Daniel Schwalbe-Koda:
mkite: A distributed computing platform for high-throughput materials simulations. CoRR abs/2301.08841 (2023) - [i4]Joshua A. Vita, Daniel Schwalbe-Koda:
Data efficiency and extrapolation trends in neural network interatomic potentials. CoRR abs/2302.05823 (2023) - [i3]Daniel Schwalbe-Koda, Daniel E. Widdowson, Tuan Anh Pham, Vitaliy A. Kurlin:
Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances. CoRR abs/2307.10935 (2023) - 2021
- [i2]Daniel Schwalbe-Koda, Aik Rui Tan, Rafael Gómez-Bombarelli:
Adversarial Attacks on Uncertainty Enable Active Learning for Neural Network Potentials. CoRR abs/2101.11588 (2021)
2010 – 2019
- 2019
- [i1]Daniel Schwalbe-Koda, Rafael Gómez-Bombarelli:
Generative Models for Automatic Chemical Design. CoRR abs/1907.01632 (2019)
Coauthor Index
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