Fast and Accurate Prediction of Material Properties with Three-Body Tight-Binding Model for the Periodic Table Article Swipe

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YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2112.11585

Parameterized tight-binding models fit to first principles calculations can provide an efficient and accurate quantum mechanical method for predicting properties of molecules and solids. However, well-tested parameter sets are generally only available for a limited number of atom combinations, making routine use of this method difficult. Furthermore, most previous models consider only simple two-body interactions, which limits accuracy. To tackle these challenges, we develop a density functional theory database of nearly one million materials, which we use to fit a universal set of tight-binding parameters for 65 elements and their binary combinations. We include both two-body and three-body effective interaction terms in our model, plus self-consistent charge transfer, enabling our model to work for metallic, covalent, and ionic bonds with the same parameter set. To ensure predictive power, we adopt a learning framework where we repeatedly test the model on new low energy crystal structures and then add them to the fitting dataset, iterating until predictions improve. We distribute the materials database and tools developed in this work publicly.

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Theoretical Computer Science

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Parameterized complexity Tight binding Computer science Set (abstract data type) Binary number Periodic table Atom (system on chip) Statistical physics Work (physics) Quantum Simple (philosophy) Algorithm Theoretical computer science Electronic structure Mathematics Computational chemistry Physics Chemistry Thermodynamics Programming language Arithmetic Philosophy Epistemology Quantum mechanics Embedded system

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2112.11585
PDF: https://arxiv.org/pdf/2112.11585
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4226288497

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DOI: https://doi.org/10.48550/arxiv.2112.11585

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Title: Fast and Accurate Prediction of Material Properties with Three-Body Tight-Binding Model for the Periodic Table

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-12-22

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Authors: Kevin F. Garrity, Kamal Choudhary

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Landing page: https://arxiv.org/abs/2112.11585

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PDF URL: https://arxiv.org/pdf/2112.11585

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Concepts: Parameterized complexity, Tight binding, Computer science, Set (abstract data type), Binary number, Periodic table, Atom (system on chip), Statistical physics, Work (physics), Quantum, Simple (philosophy), Algorithm, Theoretical computer science, Electronic structure, Mathematics, Computational chemistry, Physics, Chemistry, Thermodynamics, Programming language, Arithmetic, Philosophy, Epistemology, Quantum mechanics, Embedded system

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abstract_inverted_index.materials,	73
abstract_inverted_index.mechanical	15
abstract_inverted_index.parameters	84
abstract_inverted_index.predicting	18
abstract_inverted_index.predictive	126
abstract_inverted_index.principles	6
abstract_inverted_index.properties	19
abstract_inverted_index.repeatedly	135
abstract_inverted_index.structures	144
abstract_inverted_index.three-body	97
abstract_inverted_index.challenges,	61
abstract_inverted_index.interaction	99
abstract_inverted_index.predictions	155
abstract_inverted_index.well-tested	25
abstract_inverted_index.Furthermore,	46
abstract_inverted_index.calculations	7
abstract_inverted_index.Parameterized	0
abstract_inverted_index.combinations,	38
abstract_inverted_index.combinations.	91
abstract_inverted_index.interactions,	54
abstract_inverted_index.tight-binding	1, 83
abstract_inverted_index.self-consistent	105
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	2
sustainable_development_goals[0].id	https://metadata.un.org/sdg/7
sustainable_development_goals[0].score	0.8199999928474426
sustainable_development_goals[0].display_name	Affordable and clean energy
citation_normalized_percentile.value	0.15825756
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False