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Software for Atomistic Machine Learning

The application of machine-learning techniques to atomistic modeling of physics, chemistry and materials science is blooming, and machine learning is becoming an integral part of the toolbox of molecular simulations. As the conceptual framework underlying these techniques become mainstream, the software infrastructure used to apply machine learning to atomistic problems is also evolving, from experimental code to carefully designed, user-friendly, feature-rich and efficient libraries that implement state-of-the-art methods. This special issue welcomes contributions that present a snapshot of this implementation effort, discussing creative solutions of outstanding problems, demonstrating the efficiency and scaling of algorithms, and providing examples of applications to difficult modeling problems.

Software presented in this special issue will need to be "easily available" to academics. There are two aspects of availability that determine how easy it is. The first one is cost: alongside free (as in beer) software, academic versions of software costing a few hundred dollars we also classify as easily available. The second aspect is transparency. The process for obtaining the software needs to be public and not discriminate unduly: the code must be obtainable by all those who are willing to accept simple and conventional licensing terms, without any undue burden of collaboration or constraints on the intended use of the software.

Guest Editors: Gabor Csanyi, Matthias Rupp, and Emine Kucukbenli, with JCP Editors Michele Ceriotti, David Manolopoulos, Angelos Michaelides and David Sherrill

Special Collection Image
Thomas A. R. Purcell; Matthias Scheffler; Luca M. Ghiringhelli
Z. Schätzle; P. B. Szabó; M. Mezera; J. Hermann; F. Noé
Franco Pellegrini; Ruggero Lot; Yusuf Shaidu; Emine Küçükbenli
Evgeny Podryabinkin; Kamil Garifullin; Alexander Shapeev; Ivan Novikov
Filippo Bigi; Guillaume Fraux; Nicholas J. Browning; Michele Ceriotti
Martin Vondrák; Karsten Reuter; Johannes T. Margraf
Jinzhe Zeng; Duo Zhang; Denghui Lu; Pinghui Mo; Zeyu Li; Yixiao Chen; Marián Rynik; Li’ang Huang; Ziyao Li; Shaochen Shi; Yingze Wang; Haotian Ye; Ping Tuo; Jiabin Yang; Ye Ding; Yifan Li; Davide Tisi; Qiyu Zeng; Han Bao; Yu Xia; Jiameng Huang; Koki Muraoka; Yibo Wang; Junhan Chang; Fengbo Yuan; Sigbjørn Løland Bore; Chun Cai; Yinnian Lin; Bo Wang; Jiayan Xu; Jia-Xin Zhu; Chenxing Luo; Yuzhi Zhang; Rhys E. A. Goodall; Wenshuo Liang; Anurag Kumar Singh; Sikai Yao; Jingchao Zhang; Renata Wentzcovitch; Jiequn Han; Jie Liu; Weile Jia; Darrin M. York; Weinan E; Roberto Car; Linfeng Zhang; Han Wang
Tamás K. Stenczel; Zakariya El-Machachi; Guoda Liepuoniute; Joe D. Morrow; Albert P. Bartók; Matt I. J. Probert; Gábor Csányi; Volker L. Deringer
Stephen M. Goodlett; Justin M. Turney; Henry F. Schaefer, III
Kaisheng Song; Silvan Käser; Kai Töpfer; Luis Itza Vazquez-Salazar; Markus Meuwly
Andreas Møller Slavensky; Mads-Peter V. Christiansen; Bjørk Hammer
Daniel F. Thomas du Toit; Volker L. Deringer
Jicheng Guo; Vanessa Woo; David A. Andersson; Nathaniel Hoyt; Mark Williamson; Ian Foster; Chris Benmore; Nicholas E. Jackson; Ganesh Sivaraman
Luigi Bonati; Enrico Trizio; Andrea Rizzi; Michele Parrinello
Jarno Laakso; Lauri Himanen; Henrietta Homm; Eiaki V. Morooka; Marc O. J. Jäger; Milica Todorović; Patrick Rinke
Jon López-Zorrilla; Xabier M. Aretxabaleta; In Won Yeu; Iñigo Etxebarria; Hegoi Manzano; Nongnuch Artrith
Kristof T. Schütt; Stefaan S. P. Hessmann; Niklas W. A. Gebauer; Jonas Lederer; Michael Gastegger
Jan Kloppenburg; Livia B. Pártay; Hannes Jónsson; Miguel A. Caro
Paul L. Houston; Chen Qu; Qi Yu; Riccardo Conte; Apurba Nandi; Jeffrey K. Li; Joel M. Bowman
Nicholas J. Browning; Felix A. Faber; O. Anatole von Lilienfeld
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