Influence of spin-orbit coupling on chemical bonding

Guļāns, Andris; Draxl, Claudia

doi:10.48550/arxiv.2204.02751

Cited by 1 publication

(1 citation statement)

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“…Staying within the realm of computational ab initio results, the major content of the NOMAD Laboratory, ground-state calculations-in particular the energetics of materials-are likely to be controlled first. Here, first examples of data assessment [188] and error estimates [189,190] are underway. Excited states, in turn, are a true challenge.…”

Section: Current and Future Challengesmentioning

confidence: 99%

Roadmap on Machine learning in electronic structure

et al. 2022

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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.

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Section: Current and Future Challengesmentioning

confidence: 99%