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Hartl B, Mihalkovič M, Šamaj L, Mazars M, Trizac E, Kahl G. Ordered ground state configurations of the asymmetric Wigner bilayer system-Revisited with unsupervised learning. J Chem Phys 2023; 159:204112. [PMID: 38018755 DOI: 10.1063/5.0166822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/01/2023] [Indexed: 11/30/2023] Open
Abstract
We have reanalyzed the rich plethora of ground state configurations of the asymmetric Wigner bilayer system that we had recently published in a related diagram of states [Antlanger et al., Phys. Rev. Lett. 117, 118002 (2016)], comprising roughly 60 000 state points in the phase space spanned by the distance between the plates and the charge asymmetry parameter of the system. In contrast to this preceding contribution where the classification of the emerging structures was carried out "by hand," we have used for the present contribution machine learning concepts, notably based on a principal component analysis and a k-means clustering approach: using a 30-dimensional feature vector for each emerging structure (containing relevant information, such as the composition of the configuration as well as the most relevant order parameters), we were able to reanalyze these ground state configurations in a considerably more systematic and comprehensive manner than we could possibly do in the previously published classification scheme. Indeed, we were now able to identify new structures in previously unclassified regions of the parameter space and could considerably refine the previous classification scheme, thereby identifying a rich wealth of new emerging ground state configurations. Thorough consistency checks confirm the validity of the newly defined diagram of states.
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Affiliation(s)
- Benedikt Hartl
- Institute for Theoretical Physics and Center for Computational Materials Science (CMS), TU Wien, Vienna, Austria
- Allen Discovery Center, Tufts University, Medford, Massachusetts 02155, USA
| | - Marek Mihalkovič
- Institute of Physics, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Ladislav Šamaj
- Institute of Physics, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Martial Mazars
- Université Paris-Saclay, Université Paris-Saclay, CNRS, LPTMS, Orsay, France
| | - Emmanuel Trizac
- Université Paris-Saclay, Université Paris-Saclay, CNRS, LPTMS, Orsay, France
- ENS de Lyon, 46 allée d'Italie, 69364 Lyon, France
| | - Gerhard Kahl
- Institute for Theoretical Physics and Center for Computational Materials Science (CMS), TU Wien, Vienna, Austria
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Sato R, Akagi K, Takagi S, Sau K, Kisu K, Li H, Orimo SI. Topological Data analysis of Ion Migration Mechanism. J Chem Phys 2023; 158:144116. [PMID: 37061477 DOI: 10.1063/5.0143387] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2023] Open
Abstract
Topological data analysis based on persistent homology has been applied to the molecular dynamics simulation for the fast ion-conducting phase (α-phase) of AgI to show its effectiveness on the ion migration mechanism analysis. Time-averaged persistence diagrams of α-AgI, which quantitatively record the shape and size of the ring structures in the given atomic configurations, clearly showed the emergence of the four-membered rings formed by two Ag and two I ions at high temperatures. They were identified as common structures during the Ag ion migration. The averaged potential energy change due to the deformation of the four-membered ring during Ag migration agrees well with the activation energy calculated from the conductivity Arrhenius plot. The concerted motion of two Ag ions via the four-membered ring was also successfully extracted from molecular dynamics simulations by our approach, providing new insight into the specific mechanism of the concerted motion.
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Affiliation(s)
- Ryuhei Sato
- Advanced Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Kazuto Akagi
- Advanced Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Shigeyuki Takagi
- Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Kartik Sau
- Advanced Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Kazuaki Kisu
- Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Hao Li
- Advanced Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Shin-Ichi Orimo
- Advanced Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
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Jakse N, Sandberg J, Granz LF, Saliou A, Jarry P, Devijver E, Voigtmann T, Horbach J, Meyer A. Machine learning interatomic potentials for aluminium: application to solidification phenomena. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 51:035402. [PMID: 36301702 DOI: 10.1088/1361-648x/ac9d7d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
In studying solidification process by simulations on the atomic scale, the modeling of crystal nucleation or amorphization requires the construction of interatomic interactions that are able to reproduce the properties of both the solid and the liquid states. Taking into account rare nucleation events or structural relaxation under deep undercooling conditions requires much larger length scales and longer time scales than those achievable byab initiomolecular dynamics (AIMD). This problem is addressed by means of classical molecular dynamics simulations using a well established high dimensional neural network potential trained on a set of configurations generated by AIMD relevant for solidification phenomena. Our dataset contains various crystalline structures and liquid states at different pressures, including their time fluctuations in a wide range of temperatures. Applied to elemental aluminium, the resulting potential is shown to be efficient to reproduce the basic structural, dynamics and thermodynamic quantities in the liquid and undercooled states. Early stages of crystallization are further investigated on a much larger scale with one million atoms, allowing us to unravel features of the homogeneous nucleation mechanisms in the fcc phase at ambient pressure as well as in the bcc phase at high pressure with unprecedented accuracy close to theab initioone. In both cases, a single step nucleation process is observed.
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Affiliation(s)
- Noel Jakse
- Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
| | - Johannes Sandberg
- Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
- Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany
- Department of Physics, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Leon F Granz
- Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany
- Department of Physics, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Anthony Saliou
- Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
| | - Philippe Jarry
- C-TEC, Parc Economique Centr'alp, 725 rue Aristide Bergès, CS10027, Voreppe 38341 CEDEX, France
| | - Emilie Devijver
- Université Grenoble Alpes, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France
| | - Thomas Voigtmann
- Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany
- Department of Physics, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Jürgen Horbach
- Institut für Theoretische Physik II, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany
| | - Andreas Meyer
- Institut für Materialphysik im Weltraum, Deutsches Zentrum für Luft- und Raumfahrt (DLR), 51170 Köln, Germany
- Institut Laue-Langevin (ILL), 38042 Grenoble, France
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Crystal nucleation and growth processes in Cu-rich glass-forming Cu-Zr alloys . J Chem Phys 2022; 157:014506. [DOI: 10.1063/5.0097023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
The glass formation ability of an alloy depends on two competing processes: glass-transition on one hand, crystal nucleation and growth on the other hand. While these phenomena have been widely studied before in nearly equiatomic Cu-Zr alloys, studies are lacking for solute/solvent-rich ones. In the present work, molecular dynamics simulations show that the addition of a small amount of Zr (1-10 at. %) to Cu drastically increases incubation time and slows down crystal growth, thus leading to an improved glass forming ability. The crystal nucleation and growth processes of a competing face-centered cubic (FCC) Cu crystalline phase are analyzed in detail. In particular, the values of critical cooling rate, incubation period for crystallization and growth rate of FCC Cu crystals in these Cu-rich alloys are obtained. The growth of a supersaturated FCC Cu solid solution is found to be polymorphic at the interface (except for alloys with 9 and 10 at. % Zr) though a Zr concentration gradient is observed within growing crystals at high enough Zr content. The crystal growth rate before crystal impingement is nearly constant in all alloys, though it decreases exponentially with Zr content. Crystallization kinetics are also analyzed within the existing theories and compared with the experimental values available in the literature.
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Becker S, Devijver E, Molinier R, Jakse N. Unsupervised topological learning for identification of atomic structures. Phys Rev E 2022; 105:045304. [PMID: 35590625 DOI: 10.1103/physreve.105.045304] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 03/04/2022] [Indexed: 06/15/2023]
Abstract
We propose an unsupervised learning methodology with descriptors based on topological data analysis (TDA) concepts to describe the local structural properties of materials at the atomic scale. Based only on atomic positions and without a priori knowledge, our method allows for an autonomous identification of clusters of atomic structures through a Gaussian mixture model. We apply successfully this approach to the analysis of elemental Zr in the crystalline and liquid states as well as homogeneous nucleation events under deep undercooling conditions. This opens the way to deeper and autonomous study of complex phenomena in materials at the atomic scale.
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Affiliation(s)
- Sébastien Becker
- University of Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
- University of Grenoble Alpes, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France
| | - Emilie Devijver
- University of Grenoble Alpes, CNRS, Grenoble INP, LIG, F-38000 Grenoble, France
| | - Rémi Molinier
- University of Grenoble Alpes, CNRS, IF, F-38000 Grenoble, France
| | - Noël Jakse
- University of Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
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