ML4Science Seminar: Alexandre Tkatchenko (University of Luxembourg)
Machine Learning Quantum Chemical Space
Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding (quantum) molecules and materials? Aiming towards a unified machine learning (ML) model of quantum interactions, I will discuss the potential and challenges for using ML techniques in chemistry and physics. ML methods can not only accurately estimate molecular properties of large datasets, but they can also lead to new insights into chemical similarity, aromaticity, reactivity, and molecular dynamics. While the potential of machine learning for revealing insights into molecules and materials is high, I will conclude my talk by discussing the many remaining challenges.
References:
[1] O. A. von Lilienfeld, K. R. Müller, and A. Tkatchenko, Exploring Chemical Compound Space with Quantum-Based Machine Learning. Nat. Rev. Chem., in press; https://arxiv.org/abs/1911.10084.
[2] K.T. Schütt, F. Arbabzadah, S. Chmiela, K.R. Müller, and A. Tkatchenko, Quantum-chemical insights from deep tensor neural networks. Nature Commun. 8, 13890 (2017).
[3] S. Chmiela, A. Tkatchenko, H.E. Sauceda, I. Poltavsky, K.T. Schütt, and K.-R. Müller, Machine Learning of Accurate Energy-Conserving Molecular Force Fields. Science Adv. 3, 1603015 (2017).
[4] S. Chmiela, H. E. Sauceda, K. R. Mueller, and A. Tkatchenko, Towards exact molecular dynamics simulations with machine-learned force fields. Nature Commun. 9, 3887 (2018).
Видео ML4Science Seminar: Alexandre Tkatchenko (University of Luxembourg) канала ML4Science
Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding (quantum) molecules and materials? Aiming towards a unified machine learning (ML) model of quantum interactions, I will discuss the potential and challenges for using ML techniques in chemistry and physics. ML methods can not only accurately estimate molecular properties of large datasets, but they can also lead to new insights into chemical similarity, aromaticity, reactivity, and molecular dynamics. While the potential of machine learning for revealing insights into molecules and materials is high, I will conclude my talk by discussing the many remaining challenges.
References:
[1] O. A. von Lilienfeld, K. R. Müller, and A. Tkatchenko, Exploring Chemical Compound Space with Quantum-Based Machine Learning. Nat. Rev. Chem., in press; https://arxiv.org/abs/1911.10084.
[2] K.T. Schütt, F. Arbabzadah, S. Chmiela, K.R. Müller, and A. Tkatchenko, Quantum-chemical insights from deep tensor neural networks. Nature Commun. 8, 13890 (2017).
[3] S. Chmiela, A. Tkatchenko, H.E. Sauceda, I. Poltavsky, K.T. Schütt, and K.-R. Müller, Machine Learning of Accurate Energy-Conserving Molecular Force Fields. Science Adv. 3, 1603015 (2017).
[4] S. Chmiela, H. E. Sauceda, K. R. Mueller, and A. Tkatchenko, Towards exact molecular dynamics simulations with machine-learned force fields. Nature Commun. 9, 3887 (2018).
Видео ML4Science Seminar: Alexandre Tkatchenko (University of Luxembourg) канала ML4Science
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
