Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Aditya Sonpal"'
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:
Mojtaba Haghighatlari, Ramachandran Subramanian, Bhargava Urala Kota, Doaa Altarawy, Srirangaraj Setlur, Aditya Sonpal, Johannes Hachmann, Gaurav Vishwakarma
Publikováno v:
WIREs Computational Molecular Science. 10
ChemML is an open machine learning and informatics program suite that is designed to support and advance the data-driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7a82bb74c7970977e6cabefb17072b58
https://doi.org/10.1002/wcms.1458
https://doi.org/10.1002/wcms.1458
This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, that is, statistical loss function metrics for the validation and benchmarking of data-derived models, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a29f7c278bb99b9981f058cd438892bb
Autor:
Yuling An, Luis M. Antunes, Vikram, Jose J. Plata, Anthony V. Powell, Keith T. Butler, Ricardo Grau-Crespo, Hadi Abroshan, Paul Winget, H. Shaun Kwak, Christopher T. Brown, Mathew D. Halls, Dalia Yablon, Ishita Chakraborty, Hillary Passino, Krishnan Iyer, Antonios Doufas, Maksim Shivokhin, John Thornton, Bede Pittenger, Cheng Qiu, Jinglei Yang, Alejandro E. Rodríguez-Sánchez, Aditya Sonpal, Mohammad Atif Faiz Afzal, Anand Chandrasekaran, Jon Paul Janet, Shinichi Ookawara, Tomoki Yasuda, Yosuke Matsuda, Shiro Yoshikawa, Hideyuki Matsumoto, Wei-Chih Chen, Da Yan, Cheng-Chien Chen, Keisuke Takahashi, Lauren Takahashi
Autor:
Mojtaba Haghighatlari, Andrew J. Schultz, Aditya Sonpal, Johannes Hachmann, Mohammad Atif Faiz Afzal
Publikováno v:
Chemical Science
Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.
The process of developing new compounds and materials is increasingly driven by computationa
The process of developing new compounds and materials is increasingly driven by computationa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d8f53b59021bce7387758ce31225308
https://doi.org/10.26434/chemrxiv.8217758.v2
https://doi.org/10.26434/chemrxiv.8217758.v2
Autor:
Mojtaba Haghighatlari, Gaurav Vishwakarma, Doaa Altarawy, Ramachandran Subramanian, Bhargava Urala Kota, Aditya Sonpal, Srirangaraj Setlur, Johannes Hachmann
ChemML is an open machine learning and informatics program suite that is designed to support and advance the data-driven research paradigm that is currently emerging in the chemical and materials domain. ChemML allows its users to perform various dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b092dab08e741427c66d031ef7960d1
https://doi.org/10.26434/chemrxiv.8323271.v1
https://doi.org/10.26434/chemrxiv.8323271.v1