JA
JA
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machine learning
Machine learning phases of matter
Using machine learning techniques to detect phase transitions autonomously from readily accessible data.
JA in collaboration with Frank Schäfer, Niels Lörch, Flemming Holtorf, Christoph Bruder, Alan Edelman, Martin Žonda, and Axel Lode
2021 PRRes paper
Poster (2021 PRRes)
2022 PRX paper
Press release (2022 PRX)
Poster (2022 PRX)
Short talk (2022 PRX)
Long talk (2022 PRX)
2024 PRL paper
Press release UniBas (2024 PRL)
Press release MIT (2024 PRL)
Short talk (2024 PRL)
Long talk (2024 PRL)
2023 NeurIPS ML4Physical Sciences Workshop paper
Poster (2023 NeurIPS Workshop)
2023 arXiv preprint
Poster (2023 arXiv)
2024 arXiv preprint
Machine learning molecular collisions
Modelling changes in energy and state population distributions of molecular systems undergoing collisions using machine learning methods.
JA in collaboration with Debasish Koner, Juan Carlos San Vicente Veliz, Narendra Singh, Raymond Bemish, Silvan Käser, and Markus Meuwly
2020 JPCA paper
2022 JCP paper
2022 JPCA paper