Structure of an Ultrathin Oxide on Pt3Sn(111) Solved by Machine Learning Enhanced Global Optimization
Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory‐based search...
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| Veröffentlicht in: | Angewandte Chemie International Edition Jg. 61; H. 25 |
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| Sprache: | Englisch |
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Weinheim
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20.06.2022
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| Ausgabe: | International ed. in English |
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| Abstract | Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory‐based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure—the (4×4) surface oxide on Pt3Sn(111)—based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation.
Machine learning techniques can be implemented to accelerate surface structure determination based on density functional theory. The application of such an algorithm is demonstrated here for a surface oxide on Pt3Sn(111) which had eluded determination by experimental methods. |
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| AbstractList | Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory‐based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure—the (4×4) surface oxide on Pt3Sn(111)—based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation.
Machine learning techniques can be implemented to accelerate surface structure determination based on density functional theory. The application of such an algorithm is demonstrated here for a surface oxide on Pt3Sn(111) which had eluded determination by experimental methods. Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory-based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure—the (4×4) surface oxide on Pt3Sn(111)—based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation. Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory‐based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure—the (4×4) surface oxide on Pt3Sn(111)—based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation. Machine learning techniques can be implemented to accelerate surface structure determination based on density functional theory. The application of such an algorithm is demonstrated here for a surface oxide on Pt3Sn(111) which had eluded determination by experimental methods. |
| Author | Diebold, Ulrike Setvín, Martin Lundgren, Edvin Sokolović, Igor Shipilin, Mikhail Merte, Lindsay R. Hammer, Bjørk Bisbo, Malthe Kjær Hagman, Benjamin Schmid, Michael |
| AuthorAffiliation | 4 Department of Surface and Plasma Science Faculty of Mathematics and Physics Charles University 180 00 Prague 8 Czech Republic 1 Materials Science and Applied Mathematics Malmö University 20506 Malmö Sweden 3 Institute of Applied Physics TU Wien 1040 Vienna Austria 2 Center for Interstellar Catalysis Department of Physics and Astronomy Aarhus University 8000 Aarhus Denmark 5 Div. of Synchrotron Radiation Research Lund University 22100 Lund Sweden |
| AuthorAffiliation_xml | – name: 4 Department of Surface and Plasma Science Faculty of Mathematics and Physics Charles University 180 00 Prague 8 Czech Republic – name: 3 Institute of Applied Physics TU Wien 1040 Vienna Austria – name: 1 Materials Science and Applied Mathematics Malmö University 20506 Malmö Sweden – name: 5 Div. of Synchrotron Radiation Research Lund University 22100 Lund Sweden – name: 2 Center for Interstellar Catalysis Department of Physics and Astronomy Aarhus University 8000 Aarhus Denmark |
| Author_xml | – sequence: 1 givenname: Lindsay R. orcidid: 0000-0002-3213-4199 surname: Merte fullname: Merte, Lindsay R. email: lindsay.merte@mau.se organization: Malmö University – sequence: 2 givenname: Malthe Kjær orcidid: 0000-0002-2175-1028 surname: Bisbo fullname: Bisbo, Malthe Kjær organization: Aarhus University – sequence: 3 givenname: Igor orcidid: 0000-0003-1357-396X surname: Sokolović fullname: Sokolović, Igor organization: TU Wien – sequence: 4 givenname: Martin orcidid: 0000-0002-1210-7740 surname: Setvín fullname: Setvín, Martin organization: Charles University – sequence: 5 givenname: Benjamin surname: Hagman fullname: Hagman, Benjamin organization: Lund University – sequence: 6 givenname: Mikhail orcidid: 0000-0003-1623-1578 surname: Shipilin fullname: Shipilin, Mikhail organization: Lund University – sequence: 7 givenname: Michael orcidid: 0000-0003-3373-9357 surname: Schmid fullname: Schmid, Michael organization: TU Wien – sequence: 8 givenname: Ulrike orcidid: 0000-0003-0319-5256 surname: Diebold fullname: Diebold, Ulrike organization: TU Wien – sequence: 9 givenname: Edvin orcidid: 0000-0002-3692-6142 surname: Lundgren fullname: Lundgren, Edvin organization: Lund University – sequence: 10 givenname: Bjørk orcidid: 0000-0002-7849-6347 surname: Hammer fullname: Hammer, Bjørk email: hammer@phys.au.dk organization: Aarhus University |
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| CorporateAuthor | Synkrotronljusfysik Lunds universitet Naturvetenskapliga fakulteten Profile areas and other strong research environments Fysiska institutionen Faculty of Science Lund University Synchrotron Radiation Research Department of Physics Strategiska forskningsområden (SFO) Strategic research areas (SRA) NanoLund: Centre for Nanoscience Profilområden och andra starka forskningsmiljöer |
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| SubjectTerms | Algorithms Atomic structure Chemical Sciences Combinatorial analysis Complexity Computer applications Density Functional Calculations Evolutionary algorithms Global optimization Intermetallic compounds Kemi Learning algorithms Machine Learning Natural Sciences Naturvetenskap Search algorithms Solid surfaces Structural models Structure Elucidation Surface Chemistry Surface structure Teoretisk kemi (Här ingår: Beräkningskemi) Theoretical Chemistry (including Computational Chemistry) |
| Title | Structure of an Ultrathin Oxide on Pt3Sn(111) Solved by Machine Learning Enhanced Global Optimization |
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