Two papers by our Senior Fellow, Ryosuke Jinnouchi, and his collaborators at the University of Vienna and VASP Software, GmbH, were cited in a document introducing the achievements of John J. Hopfield and Geoffrey E. Hinton, recipients of the 2024 Nobel Prize in Physics*1. The machine learning pioneered by these scientists, which uses artificial neural networks (ANNs), has revolutionized various fields, including physics. Jinnouchi and his colleagues applied machine learning to simulate the thermodynamic properties and phase transitions of water. Their work was introduced as an application demonstrating the extensive impact that machine learning, including ANNs, has had on science research.
Toyota Central R&D Labs, Inc. honors the achievements of Professors Hopfield and Hinton, and remains committed to advancing science and technology.
*1: Royal Swedish Academy of Sciences. "Scientific Background to the Nobel Prize in Physics 2024" https://www.nobelprize.org/uploads/2024/09/advanced-physicsprize2024.pdf, (accessed October 9, 2024)
[Bibliographic Information for the Cited Papers]
Title: Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference
Authors: Jinnouchi, R., Lahnsteiner, J., Karsai, F., Kresse, G., Bokdam, M.
Journal Name: Physical Review Letters
Published: June 7, 2019
https://doi.org/10.1103/PhysRevLett.122.225701
Title: Comparing Machine Learning Potentials for Water: Kernel-based Regression and Behler–Parrinello Neural Networks
Authors: Montero de Hijes, P., Dellago, C., Jinnouchi, R., Schmiedmayer, B., Kresse, G.
Journal Name: The Journal of Chemical Physics
Published: March 20, 2024
https://doi.org/10.1063/5.0197105