We are delighted to announce that the Physical Chemistry Chemical Physics (PCCP) themed issue Insightful Machine Learning for Physical Chemistry is now online and free to access until mid-December 2023.
Machine learning has become an increasingly powerful tool for providing insights into applications such as the design of materials based on soft and hard matter and for improving the accuracy of ground- and excited-state simulations.
Guest Edited by Isaac Tamblyn, Pavlo O. Dral, Olexandr Isayev and Aurora Clark, this collection reviews contributions from various fields with a focus on design principles for new materials, learning many-body correlations, multi-scale physical chemistry, and uncovering phenomena for excited matter.
Read the full issue online
Themed collection on Insightful Machine Learning for Physical Chemistry
Aurora E. Clark, Pavlo O. Dral, Isaac Tamblyn and Olexandr Isayev
Phys. Chem. Chem. Phys., 2023, 25, 22563-22564. DOI: 10.1039/D3CP90129G
Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality
Sergei Manzhos, Shunsaku Tsuda and Manabu Ihara
Phys. Chem. Chem. Phys., 2023, 25, 1546-1555. DOI: 10.1039/D2CP04155C
Transfer learning for chemically accurate interatomic neural network potentials
Viktor Zaverkin, David Holzmüller, Luca Bonfirraro and Johannes Kästner
Phys. Chem. Chem. Phys., 2023, 25, 5383-5396. DOI: 10.1039/D2CP05793J
The principal component analysis of the ring deformation in the nonadiabatic surface hopping dynamics
Yifei Zhu, Jiawei Peng, Xu Kang, Chao Xu and Zhenggang Lan
Phys. Chem. Chem. Phys., 2022, 24, 24362-24382. DOI: 10.1039/D2CP03323B
Solvent selection for polymers enabled by generalized chemical fingerprinting and machine learning
Joseph Kern, Shruti Venkatram, Manali Banerjee, Blair Brettmann and Rampi Ramprasad
Phys. Chem. Chem. Phys., 2022, 24, 26547-26555. DOI: 10.1039/D2CP03735A
We hope you enjoy reading the articles. Please get in touch if you have any questions about this themed collection or PCCP.