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Linker T, Nomura KI, Fukushima S, Kalia RK, Krishnamoorthy A, Nakano A, Shimamura K, Shimojo F, Vashishta P. Squishing Skyrmions: Symmetry-Guided Dynamic Transformation of Polar Topologies Under Compression. J Phys Chem Lett 2022; 13:11335-11345. [PMID: 36454058 DOI: 10.1021/acs.jpclett.2c03029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Mechanical controllability of recently discovered topological defects (e.g., skyrmions) in ferroelectric materials is of interest for the development of ultralow-power mechano-electronics that are protected against thermal noise. However, fundamental understanding is hindered by the "multiscale quantum challenge" to describe topological switching encompassing large spatiotemporal scales with quantum mechanical accuracy. Here, we overcome this challenge by developing a machine-learning-based multiscale simulation framework─a hybrid neural network quantum molecular dynamics (NNQMD) and molecular mechanics (MM) method. For nanostructures composed of SrTiO3 and PbTiO3, we find how the symmetry of mechanical loading essentially controls polar topological switching. We find under symmetry-breaking uniaxial compression a squishing-to-annihilation pathway versus formation of a topological composite named skyrmionium under symmetry-preserving isotropic compression. The distinct pathways are explained in terms of the underlying materials' elasticity and symmetry, as well as the Landau-Lifshitz-Kittel scaling law. Such rational control of ferroelectric topologies will likely facilitate exploration of the rich ferroelectric "topotronics" design space.
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Affiliation(s)
- Thomas Linker
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California90089-0242, United States of America
| | - Ken-Ichi Nomura
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California90089-0242, United States of America
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto860-8555, Japan
| | - Rajiv K Kalia
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California90089-0242, United States of America
| | - Aravind Krishnamoorthy
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California90089-0242, United States of America
| | - Aiichiro Nakano
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California90089-0242, United States of America
| | - Kohei Shimamura
- Department of Physics, Kumamoto University, Kumamoto860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto860-8555, Japan
| | - Priya Vashishta
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California90089-0242, United States of America
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Linker T, Nomura KI, Aditya A, Fukshima S, Kalia RK, Krishnamoorthy A, Nakano A, Rajak P, Shimmura K, Shimojo F, Vashishta P. Exploring far-from-equilibrium ultrafast polarization control in ferroelectric oxides with excited-state neural network quantum molecular dynamics. SCIENCE ADVANCES 2022; 8:eabk2625. [PMID: 35319991 PMCID: PMC8942355 DOI: 10.1126/sciadv.abk2625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
Ferroelectric materials exhibit a rich range of complex polar topologies, but their study under far-from-equilibrium optical excitation has been largely unexplored because of the difficulty in modeling the multiple spatiotemporal scales involved quantum-mechanically. To study optical excitation at spatiotemporal scales where these topologies emerge, we have performed multiscale excited-state neural network quantum molecular dynamics simulations that integrate quantum-mechanical description of electronic excitation and billion-atom machine learning molecular dynamics to describe ultrafast polarization control in an archetypal ferroelectric oxide, lead titanate. Far-from-equilibrium quantum simulations reveal a marked photo-induced change in the electronic energy landscape and resulting cross-over from ferroelectric to octahedral tilting topological dynamics within picoseconds. The coupling and frustration of these dynamics, in turn, create topological defects in the form of polar strings. The demonstrated nexus of multiscale quantum simulation and machine learning will boost not only the emerging field of ferroelectric topotronics but also broader optoelectronic applications.
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Affiliation(s)
- Thomas Linker
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, USA
| | - Ken-ichi Nomura
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, USA
| | - Anikeya Aditya
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, USA
| | - Shogo Fukshima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Rajiv K. Kalia
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, USA
| | - Aravind Krishnamoorthy
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, USA
| | - Aiichiro Nakano
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, USA
| | - Pankaj Rajak
- Amazon, 410 Terry Ave. North, Seattle, WA 98109-5210 USA
| | - Kohei Shimmura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Priya Vashishta
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, CA 90089-0242, USA
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Rajak P, Baradwaj N, Nomura KI, Krishnamoorthy A, Rino JP, Shimamura K, Fukushima S, Shimojo F, Kalia R, Nakano A, Vashishta P. Neural Network Quantum Molecular Dynamics, Intermediate Range Order in GeSe 2, and Neutron Scattering Experiments. J Phys Chem Lett 2021; 12:6020-6028. [PMID: 34165308 DOI: 10.1021/acs.jpclett.1c01272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
A remarkable property of certain covalent glasses and their melts is intermediate range order, manifested as the first sharp diffraction peak (FSDP) in neutron-scattering experiments, as was exhaustively investigated by Price, Saboungi, and collaborators. Atomistic simulations thus far have relied on either quantum molecular dynamics (QMD), with systems too small to resolve FSDP, or classical molecular dynamics, without quantum-mechanical accuracy. We investigate prototypical FSDP in GeSe2 glass and melt using neural-network quantum molecular dynamics (NNQMD) based on machine learning, which allows large simulation sizes with validated quantum mechanical accuracy to make quantitative comparisons with neutron data. The system-size dependence of the FSDP height is determined by comparing QMD and NNQMD simulations with experimental data. Partial pair distribution functions, bond-angle distributions, partial and neutron structure factors, and ring-size distributions are presented. Calculated FSDP heights agree quantitatively with neutron scattering data for GeSe2 glass at 10 K and melt at 1100 K.
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Affiliation(s)
- Pankaj Rajak
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles 90089, United States
- Argonne National Laboratory, Lemont, Illinois 60439, United States
| | - Nitish Baradwaj
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles 90089, United States
| | - Ken-Ichi Nomura
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles 90089, United States
| | - Aravind Krishnamoorthy
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles 90089, United States
| | - Jose P Rino
- Departamento de Fisica, Universidade Federal de São Carlos, São Carlos, São Paulo13565-905, Brazil
| | - Kohei Shimamura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Rajiv Kalia
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles 90089, United States
| | - Aiichiro Nakano
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles 90089, United States
| | - Priya Vashishta
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles 90089, United States
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Krishnamoorthy A, Nomura KI, Baradwaj N, Shimamura K, Rajak P, Mishra A, Fukushima S, Shimojo F, Kalia R, Nakano A, Vashishta P. Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics. PHYSICAL REVIEW LETTERS 2021; 126:216403. [PMID: 34114857 DOI: 10.1103/physrevlett.126.216403] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 03/30/2021] [Indexed: 06/12/2023]
Abstract
The static dielectric constant ϵ_{0} and its temperature dependence for liquid water is investigated using neural network quantum molecular dynamics (NNQMD). We compute the exact dielectric constant in canonical ensemble from NNQMD trajectories using fluctuations in macroscopic polarization computed from maximally localized Wannier functions (MLWF). Two deep neural networks are constructed. The first, NNQMD, is trained on QMD configurations for liquid water under a variety of temperature and density conditions to learn potential energy surface and forces and then perform molecular dynamics simulations. The second network, NNMLWF, is trained to predict locations of MLWF of individual molecules using the atomic configurations from NNQMD. Training data for both the neural networks is produced using a highly accurate quantum-mechanical method, DFT-SCAN that yields an excellent description of liquid water. We produce 280×10^{6} configurations of water at 7 temperatures using NNQMD and predict MLWF centers using NNMLWF to compute the polarization fluctuations. The length of trajectories needed for a converged value of the dielectric constant at 0°C is found to be 20 ns (40×10^{6} configurations with 0.5 fs time step). The computed dielectric constants for 0, 15, 30, 45, 60, 75, and 90°C are in good agreement with experiments. Our scalable scheme to compute dielectric constants with quantum accuracy is also applicable to other polar molecular liquids.
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Affiliation(s)
- Aravind Krishnamoorthy
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Ken-Ichi Nomura
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Nitish Baradwaj
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Kohei Shimamura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Pankaj Rajak
- Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Ankit Mishra
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Rajiv Kalia
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Aiichiro Nakano
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
| | - Priya Vashishta
- Collaboratory for Advanced Computing and Simulations, Department of Chemical Engineering and Materials Science, Department of Physics & Astronomy, and Department of Computer Science, University of Southern California, Los Angeles, California 90089, USA
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An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6638919. [PMID: 33575333 PMCID: PMC7864739 DOI: 10.1155/2021/6638919] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/08/2021] [Accepted: 01/20/2021] [Indexed: 12/11/2022]
Abstract
Background Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significant. This study was aimed to develop an artificial neural networks (ANN) model for early prediction of in-hospital mortality in AP. Methods Patients with AP were identified from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. Clinical and laboratory data were utilized to perform a predictive model by back propagation ANN approach. Results A total of 337 patients with AP were analyzed in the study, and the in-hospital mortality rate was 11.2%. A total of 12 variables that differed between patients in survivor group and nonsurvivor group were applied to construct ANN model. Three independent variables were identified as risk factors associated with in-hospital mortality by multivariate logistic regression analysis. The predictive performance based on the area under the receiver operating characteristic curve (AUC) was 0.769 for ANN model, 0.607 for logistic regression, 0.652 for Ranson score, and 0.401 for SOFA score. Conclusion An ANN predictive model for in-hospital mortality in patients with AP in MIMIC-III database was first performed. The patients with high risk of fatal outcome can be screened out easily in the early stage of AP by our model.
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Shimamura K, Takeshita Y, Fukushima S, Koura A, Shimojo F. Computational and training requirements for interatomic potential based on artificial neural network for estimating low thermal conductivity of silver chalcogenides. J Chem Phys 2020; 153:234301. [PMID: 33353316 DOI: 10.1063/5.0027058] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
We examined the estimation of thermal conductivity through molecular dynamics simulations for a superionic conductor, α-Ag2Se, using the interatomic potential based on an artificial neural network (ANN potential). The training data were created using the existing empirical potential of Ag2Se to help find suitable computational and training requirements for the ANN potential, with the intent to apply them to first-principles calculations. The thermal conductivities calculated using different definitions of heat flux were compared, and the effect of explicit long-range Coulomb interaction on the conductivities was investigated. We clarified that using a rigorous heat flux formula for the ANN potential, even for highly ionic α-Ag2Se, the resulting thermal conductivity was reasonably consistent with the reference value without explicitly considering Coulomb interaction. It was found that ANN training including the virial term played an important role in reducing the dependency of thermal conductivity on the initial values of the weight parameters of the ANN.
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Affiliation(s)
- Kohei Shimamura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Yusuke Takeshita
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Akihide Koura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
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Misawa M, Fukushima S, Koura A, Shimamura K, Shimojo F, Tiwari S, Nomura KI, Kalia RK, Nakano A, Vashishta P. Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials. J Phys Chem Lett 2020; 11:4536-4541. [PMID: 32443935 DOI: 10.1021/acs.jpclett.0c00637] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.
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Affiliation(s)
- Masaaki Misawa
- Graduate School of Natural Science and Technology, Okayama University, Okayama 700-8530, Japan
| | - Shogo Fukushima
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Akihide Koura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Kohei Shimamura
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Fuyuki Shimojo
- Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan
| | - Subodh Tiwari
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
| | - Ken-Ichi Nomura
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
| | - Rajiv K Kalia
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
| | - Aiichiro Nakano
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
| | - Priya Vashishta
- Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States
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