Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Bao Xin Xue"'
Autor:
Mohammad Atif Faiz Afzal, Mario Barbatti, Stefano Battaglia, Liqun Cao, Tucker Carrington, Rose K. Cersonsky, Bili Chen, Guanhua Chen, Bruno Cuevas-Zuviría, Leyuan Cui, Hongsheng Dai, Sandip De, Pavlo O. Dral, Ignacio Fdez. Galván, Owen Fresse-Colson, Gang Fu, Fuchun Ge, Johannes Hachmann, Mojtaba Haghighatlari, Yi-Fan Hou, Eugen Hruska, Manabu Ihara, Bin Jiang, Hong Jiang, Jun Jiang, Grier M. Jones, Alexei A. Kananenka, Julien Lam, Zhenggang Lan, Gaétan Laurens, Wei Liang, Roland Lindh, Fang Liu, Hong Liu, Zhi-Pan Liu, Sergei Manzhos, Philipp Marquetand, Jiawei Peng, Max Pinheiro Jr, Aatish Pradhan, Jan Řezáč, P.D.Varuna S. Pathirage, Cheng Shang, Aditya Sonpal, Peifeng Su, Huai-Yang Sun, Gauthier Tallec, Arif Ullah, Gaurav Vishwakarma, Konstantinos D. Vogiatzis, Jingchun Wang, Shuai Wang, Julia Westermayr, Jiang Wu, Xun Wu, Bao-Xin Xue, Jinzhe Zeng, Lina Zhang, Yaolong Zhang, Yi Zhao, Xiao Zheng, Xinxin Zhong, Tong Zhu, Yifei Zhu, Tetiana Zubatiuk
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5ed5888a7f4b03ffdc888de345270d64
https://doi.org/10.1016/b978-0-323-90049-2.09990-x
https://doi.org/10.1016/b978-0-323-90049-2.09990-x
Autor:
Pavlo O Dral, Fuchun Ge, Bao Xin Xue, Yi-Fan Hou, Max Pinheiro, Jianxing Huang, Mario Barbatti
Publikováno v:
Topics in Current Chemistry Collections ISBN: 9783031076572
Atomistic machine learning (AML) simulations are used in chemistry at an everincreasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faff46bd03bb4d5504c0220eed9c50af
https://doi.org/10.1007/978-3-031-07658-9_2
https://doi.org/10.1007/978-3-031-07658-9_2
Autor:
Jianxing Huang, Yi-Fan Hou, Bao-Xin Xue, Fuchun Ge, Max Pinheiro, Mario Barbatti, Pavlo O. Dral
Publikováno v:
Topics in current chemistry
Topics in current chemistry, Springer, 2021, 379, ⟨10.1007/s41061-021-00339-5⟩
Topics in Current Chemistry (Cham)
Topics in current chemistry, 2021, 379, ⟨10.1007/s41061-021-00339-5⟩
Topics in current chemistry, Springer, 2021, 379, ⟨10.1007/s41061-021-00339-5⟩
Topics in Current Chemistry (Cham)
Topics in current chemistry, 2021, 379, ⟨10.1007/s41061-021-00339-5⟩
Atomistic machine learning (AML) simulations are used in chemistry at an ever-increasing pace. A large number of AML models has been developed, but their implementations are scattered among different packages, each with its own conventions for input
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7458b487ac7fbd2c970bad69f360dd50
https://hal.archives-ouvertes.fr/hal-03256172/file/p157_dral_mlatom2_topcurrchem_2021.pdf
https://hal.archives-ouvertes.fr/hal-03256172/file/p157_dral_mlatom2_topcurrchem_2021.pdf
Publikováno v:
The Journal of Physical Chemistry A
Journal of Physical Chemistry A
Journal of Physical Chemistry A, 2020, ⟨10.1021/acs.jpca.0c05310⟩
The Journal of Physical Chemistry. a
Journal of Physical Chemistry A, American Chemical Society, 2020, ⟨10.1021/acs.jpca.0c05310⟩
Journal of Physical Chemistry A
Journal of Physical Chemistry A, 2020, ⟨10.1021/acs.jpca.0c05310⟩
The Journal of Physical Chemistry. a
Journal of Physical Chemistry A, American Chemical Society, 2020, ⟨10.1021/acs.jpca.0c05310⟩
International audience; We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f06b3367451b9daf36ac91fee16ffbb
Publikováno v:
Journal of Physical Chemistry A; 9/3/2020, Vol. 124 Issue 35, p7199-7210, 12p
Autor:
Pavlo O. Dral
Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by i
This volume presents the current status of software development in the field of computational and theoretical chemistry and gives an overview of the emerging trends. The challenges of maintaining the legacy codes and their adaptation to the rapidly g