Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Roadmap on Machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.</p

Roadmap on machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Super-heavy nuclei with Z = 118 and their mass and charge spectrum of fission fragments

The first results of the calculation of the mass and charge yields of fission fragments for over 60 isotopes which have Z = 118 are presented. The results were obtained from the condition of thermodynamic ordering of the ensemble of fission fragments. The role of neutrons shells with N = 82 or N = 126 and protons shells with Z = 50 in the realization of symmetric (or one-humped) and asymmetric (2- or 3-humped) shapes of the fission-fragment yields with the transition from neutron-proficient to neutron-deficient isotopes was investigated. The data of fragments yields had been analyzed under the conditions of a “cold” and “hot” fission. The calculations show the possibility to identify super-heavy nuclei with Z ≥ 118 produced synthetically by heavy-ion reaction on their mass/charge spectrum division

Roadmap on Machine learning in electronic structure

| openaire: EC/H2020/676580/EU//NoMaD | openaire: EC/H2020/951786/EU//NOMAD CoE Funding Information: This work received funding from the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 676580), and from the Deutsche Forschungsgemeinschaft (DFG), projects 414984028 (SFB 1404, FONDA) and 460197019 (NFDI consortium FAIRmat). Funding Information: This work was funded by the Austrian Science Fund (FWF) [J 4522-N] (JW), the Federal Ministry of Education and Research (BMBF) for the Berlin Center for Machine Learning/BIFOLD (01IS18037A) (KTS), and the UKRI Future Leaders Fellowship programme (MR/S016023/1) (RJM). MG works at the BASLEARN-TU Berlin/BASF Joint Lab for Machine Learning, co-financed by TU Berlin and BASF SE. Funding Information: Financial support by the Deutsche Forschungsgemeinschaft (DFG) through Project C1 of the collaborative research centre SFB/TR 103 ‘From Atoms to Turbine Blades—A Scientific Basis for a new Generation of Single-Crystal Superalloys’ is acknowledged. Funding Information: The work performed by OI was made possible by the Office of Naval Research (ONR) through support provided by the Energetic Materials Program (MURI Grant No. N00014-21-1-2476). This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the US Department of Energy (DOE) Office of Science. This research is part of the Frontera computing project at the Texas Advanced Computing Center. Frontera is made possible by the National Science Foundation award OAC-1818253. Funding Information: HJK acknowledges generous support by the Office of Naval Research under Grant Numbers N00014-17-1-2956, N00014-18-1-2434, and N00014-20-1-2150, DARPA Grant D18AP00039, the Department of Energy under Grant Numbers DE-SC0018096 and DE-SC0012702, the National Science Foundation under Grant Numbers CBET-1704266 and CBET-1846426, an AAAS Marion Milligan Mason Award, and a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. The author also thanks Adam H Steeves for providing a critical reading. Funding Information: PVB thanks Huozhi Zhou, Lav Varshney, and Yangfeng Ji for insightful discussions. Research was sponsored by the Defense Advanced Research Project Agency (DARPA) and The Army Research Office and was accomplished under Grant Number W911NF-20-1-0289. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DARPA, the Army Research Office, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes not with standing any copyright notation herein. Funding Information: This work was supported by KAKENHI Grant No. JP20J20845 from Japan Society for the Promotion of Science. Part of the calculation was performed at the Supercomputer System B and C at the Institute for Solid State Physics, the University of Tokyo. Funding Information: This work was performed as part of a user project at the Molecular Foundry, Lawrence Berkeley National Laboratory, supported by the Office of Science, Office of Basic Energy Sciences, of the U.S. Department of Energy under Contract No. DE-AC02–05CH11231. CB performed work at the NRC under the auspices of the AI4D Program. IT acknowledges NSERC Funding Information: KB acknowledges NSF Grant No. CHE 1856165 and BK acknowledges NSF Grant No. DGE 1633631. Funding Information: The authors thank Frisco Rose and Michael J Mehl for fruitful discussions, and acknowledge support by DOD-ONR (N00014-17-1-2090, N00014-17-1-2876) and by the National Science Foundation under DMREF Grant No. DMR-1921909. Funding Information: The authors would like to acknowledge support from the NCCR MARVEL, funded by the Swiss National Science Foundation (SNSF) (Grant Agreement ID 51NF40-182892). Funding Information: I acknowledge Jilles Vreeken, Angelo Ziletti, and Matthias Scheffler for insightful discussions. This work received funding from the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 676580 and No. 951786), the NOMAD laboratory CoE, and ERC:TEC1P (No. 740233). Funding Information: We acknowledge funding and support from the European Research Commission (Grant No. ERC CoG 772230), the Berlin Mathematics Research Center MATH+ (Project Nos. AA2-8, EF1-2, and AA2-22), and the German Ministry of Education and Research (Grant No. 01IS18037J, BIFOLD–BZML). Funding Information: We acknowledge Luca Ghiringhelli, Lucas Foppa, Claudia Draxl, Wray Buntine, and Daniel Schmidt for insightful discussions and, in particular, thank Luca Ghiringhelli and Lucas Foppa for critically reading the manuscript. This work received funding from the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 951786), the NOMAD CoE, and ERC: TEC1P (No. 740233) as well as from the Australian Research Council (DP210100045). Publisher Copyright: © 2022 The Author(s). Published by IOP Publishing Ltd.In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.Peer reviewe