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Thermodynamics and dielectric response of BaTiO_(3)by data-driven modeling

npj computational Materials 2022年第1期8卷 1998-2014页

作者： Lorenzo Gigli Max Veit Michele Kotiuga Giovanni Pizzi Nicola Marzari Michele Ceriotti Laboratory of Computational Science and Modeling(COSMO) Institute of MaterialsÉcole Polytechnique Fédérale de LausanneCH-1015LausanneSwitzerland Theory and Simulation of Materials(THEOS)and National Centre for Computational Design and Discovery of Novel Materials(MARVEL) École Polytechnique Fédérale de LausanneCH-1015LausanneSwitzerland

modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort,requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic *** demonstrate the development and application of an integrated machine learning model that describes on the same footing structural,energetic,and functional properties of barium titanate(BaTiO_(3)),a prototypical *** model uses ab initio calculations as a reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio *** predictions allow us to assess the microscopic mechanism of the ferroelectric *** presence of an order-disorder transition for the Ti off-centered states is the main driver of the ferroelectric transition,even though the coupling between symmetry breaking and cell distortions determines the presence of intermediate,partly-ordered ***,we thoroughly probe the static and dynamical behavior of BaTiO_(3)across its phase diagram without the need to introduce a coarse-grained description of the ferroelectric ***,we apply the polarization model to calculate the dielectric response properties of the material in a full ab initio manner,again reproducing the correct qualitative experimental behavior.

关键词： transition ferroelectric dielectric

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Probing the effects of broken symmetries in machine learning

arXiv

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arXiv 2024年

作者： Langer, Marcel F. Pozdnyakov, Sergey N. Ceriotti, Michele Laboratory of Computational Science and Modeling National Centre for Computational Design Discovery of Novel Materials MARVEL Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properties of matter at the atomic scale. Both established and state-of-the-art approaches, with almost no exceptions, are built to be exactly equivariant to translations, permutations, and rotations of the atoms. Incorporating symmetries – rotations in particular – constrains the model design space and implies more complicated architectures that are often also computationally demanding. There are indications that non-symmetric models can easily learn symmetries from data, and that doing so can even be beneficial for the accuracy of the model. We put a model that obeys rotational invariance only approximately to the test, in realistic scenarios involving simulations of gas-phase, liquid, and solid water. We focus specifically on physical observables that are likely to be affected – directly or indirectly – by symmetry breaking, finding negligible consequences when the model is used in an interpolative, bulk, regime. Even for extrapolative gas-phase predictions, the model remains very stable, even though symmetry artifacts are noticeable. We also discuss strategies that can be used to systematically reduce the magnitude of symmetry breaking when it occurs, and assess their impact on the convergence of observables. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Conformational Folding Activates Photoinduced Electron Transfer

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CCS Chemistry 2024年第11期6卷 2804-2813页

作者： Shiqing Huang Syed Ali Abbas Abedi Zhifeng Li Rongrong Huang Xiaoyu Yan Mohammad Izadyar Qinglong Qiao Yu Fang Zhaochao Xu Xiaogang Liu Fluorescence Research Group Singapore University of Technology and DesignSingapore 487372 School of Chemistry and Life Resources Renmin University of ChinaBeijing 100872 CAS Key Laboratory of Separation Science for Analytical Chemistry Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023 Research Center for Modeling and Computational Sciences Faculty of ScienceFerdowsi University of MashhadMashhad *** Key Laboratory of Applied Surface and Colloid Chemistry(Ministry of Education) School of Chemistry and Chemical EngineeringShaanxi Normal UniversityXi’anShaanxi 710119

Photoinduced electron transfer(PET)is a critical process in many functional materials,underpinning various technological applications(i.e.,fluorescent probes and photocatalysts).Despite its signifi-cance,the detailed structural dynamics of PET,particularly during the excited state,remain poorly *** study investigates the mechanisms of conformational folding and their implications for activating PET in molecular systems characterized by a fluorophore-spacer-receptor *** demonstrate that traditional computational models,primarily based on frontier molecular orbitals,often fall short in capturing these conformational dynamics,leading to inadequate explanations of PET *** the incorporation of conformational folding,our computational model has achieved excellent agreement with experimental data,thereby resolving several long-standing debates on PET *** mechanistic advancement not only deepens our understanding of PET but also opens new avenues for designing advanced functional *** have thus successfully demonstrated the imaging of lysosomes in live cells using a PET probe.

关键词： Photoinduced electron transfer conformational folding bioimaging excited-state dynamics advanced materials

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Efficient SVV stabilized triangular spectral element methods for incompressible flows of high Reynolds numbers

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Advances in Aerodynamics 2022年第1期4卷 58-77页

作者： Lizhen Chen Tao Tang Chuanju Xu Beijing Computational Science Research Center Beijing100193People’s Republic of China Division of Science and Technology BNU-HKBU United International CollegeZhuhaiGuangdongPeople’s Republic of China Guangdong Provincial Key Laboratory of Computational Science and Material Design Southern University of Science and TechnologyShenzhenPeople’s Republic of China School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific ComputingXiamen UniversityXiamen361005People’s Republic of China

In this paper,we propose a spectral vanishing viscosity method for the triangular spectral element computation of high Reynolds number incompressible *** can be regarded as an extension of a similar stabilization technique for the standard spectral element *** difficulty of this extension lies in the fact that a suitable definition of spectral vanishing viscosity operator in non-structured elements does not exist,and it is not clear that if a suitably defined spectral vanishing viscosity provides desirable dissipation for the artificially accumulated *** main contribution of the paper includes:1)a well-defined spectral vanishing viscosity operator is proposed for non-standard spectral element methods for the Navier-Stokes equations based on triangular or tetrahedron partitions;2)an evaluation technique is introduced to efficiently implement the stabilization term without extra computational cost;3)the accuracy and efficiency of the proposed method is carefully examined through several numerical *** numerical results show that the proposed method not only preserves the exponential convergence,but also produces improved accuracy when applied to the unsteady Navier-Stokes equations having smooth ***,the stabilized triangular spectral element method efficiently stabilizes the simulation of high Reynolds incompressible flows.

关键词： Navier-Stokes equations Triangular spectral element method Spectral vanishing viscosity

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Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

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Urban Informatics 2024年第1期3卷 1-44页

作者： Xu, Haowen Omitaomu, Femi Sabri, Soheil Zlatanova, Sisi Li, Xiao Song, Yongze Computational Urban Sciences Group Oak Ridge National Laboratory Oak Ridge USA Urban Digital Twin Lab School of Modeling Simulation and Training University of Central Florida Orlando USA The School of Built Environment UNSW Sydney Australia Transport Studies Unit University of Oxford Oxford UK School of Design and the Built Environment Curtin University Bentley Australia

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

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First-principles-based prediction of yield strength in the RhIrPdPtNiCu high-entropy alloy

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npj computational Materials 2019年第1期5卷 1017-1023页

作者： Binglun Yin William A.Curtin Laboratory for Multiscale Mechanics Modeling(LAMMM)and National Centre for Computational Design and Discovery of Novel Materials(NCCR MARVEL) École Polytechnique Fédérale de Lausanne1015 LausanneSwitzerland

High-entropy alloys are random alloys with five or more components,often near equi-composition,that often exhibit excellent mechanical *** the design of new materials across the wide composition space requires an ability to compute necessary underlying material parameters via ab initio ***,density functional theory is used to compute the elemental misfit volumes,alloy lattice constant,elastic constants,and stable stacking fault energy in the fcc noble metal *** properties are then used in a recent theory for the temperature and strain-rate dependent yield *** parameter-free prediction of 583 MPa is in excellent agreement with the measured value of 527 *** quantitative connection between alloy composition and yield strength,without any experimental input,motivates this general density functional theory-based methodological path for exploring new potential high-strength high-entropy alloys,in this and other alloy classes,with the chemical accuracy of first-principles methods.

关键词： alloy strength properties.

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Novel ligand-based docking;molecular dynamic simulations;and absorption, distribution, metabolism, and excretion approach to analyzing potential acetylcholinesterase inhibitors for Alzheimer's disease

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Journal of Pharmaceutical Analysis 2018年第6期8卷 413-420页

作者： Subramaniyan Vijayakumar Palani Manogar Srinivasan Prabhu Ram Avadhar Sanjeevkumar Singh Computational Phytochemistry Laboratory PG and Research Department of Botany and Microbiology AVVM Sri Pushpam College (Autonomous) Poondi Computer Aided Drug Design and Molecular Modeling Laboratory Department of Bioinformatics Alagappa University

Acetylcholinesterase(AChE) plays an important role in Alzheimer's disease(AD). The excessive activity of AChE causes various neuronal problems, particularly dementia and neuronal cell deaths. Generally, antiAChE drugs induce some serious neuronal side effects in humans. Therefore, this study sought to identify alternative drug molecules from natural products with fewer side effects than those of conventional drugs for treating AD. To achieve this, we developed computational methods for predicting drug and target binding affinities using the Schrodinger suite. The target and ligand molecules were retrieved from established databases. The target enzyme has 539 amino acid residues in its sequence alignment. Ligand molecules of 20 bioactive molecules were obtained from different kinds of plants, after which we performed critical analyses such as molecular docking; molecular dynamic(MD) simulations; and absorption, distribution, metabolism, and excretion(ADME) analysis. In the docking studies, the natural compound rutin showed a superior docking score of à 12.335 with a good binding energy value ofà73.313 kcal/mol. Based on these findings, rutin and the target complex was used to perform MD simulations to analyze rutin stability at 30 ns. In conclusion, our study demonstrates that rutin is a superior drug candidate for AD. Therefore, we propose that this molecule is worth further investigation using in vitro studies.

关键词： Alzheimer's disease Acetylcholinesterase Phytocompounds Molecular docking Free energy calculations Molecular dynamic simulations

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Aerodynamic design exploration through point-by-point pareto set identification with local surrogate models

Aerodynamic design exploration through point-by-point pareto...

引用

AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 2018

作者： Amrit, Anand Leifsson, Leifur Koziel, Slawomir Computational Design Laboratory Iowa State University Department of Aerospace Engineering AmesIA50011 United States Engineering Optimization & Modeling Center Reykjavik University School of Science and Engineering Menntavegur 1 Reykjavik101 Iceland

ISBN: (数字)9781624105326

ISBN: (纸本)9781624105326

Aerodynamic design is inherently a multi-objective problem. Obtaining the best possible trade-offs between various aerodynamic objectives can be computationally expensive when only high-fidelity computational fluid dynamics (CFD) simulations are utilized. In this work, aerodynamic design exploration is performed using a unique and computationally cheap methodology. The best-possible designs near the single-objective (SO) optimal design are determined using point-by-point exploration. This is performed using a trust-region-based multi-fidelity optimization algorithm with locally constructed response surface approximations (RSAs). In this work, the RSAs are constructed using second order polynomials without mixed terms, multi-fidelity models, and adaptive corrections. The algorithm is applied on a two-dimensional benchmark aerodynamic design case involving lift-constrained drag minimization and pitching moment minimization in inviscid transonic flow. The Pareto front is obtained at a cost of few evaluations of the high-fidelity CFD model. At the end, it is clearly shown how point-by-point can be a fast method to obtain Pareto or part of the Pareto for problems involving physics based simulations. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： computational fluid dynamics

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Efficient inverse design of transonic airfoils using variable-resolution models and manifold mapping

Efficient inverse design of transonic airfoils using variabl...

引用

AIAA Aerospace Sciences Meeting, 2018

作者： Du, Xiaosong Leifsson, Leifur Koziel, Slawomir Department of Aerospace Engineering Computational Design Laboratory Iowa State University AmesIA50011 United States School of Science and Engineering Engineering Optimization & Modeling Center Reykjavik University Menntavegur 1 Reykjavik101 Iceland

ISBN: (纸本)9781624105241

This paper presents an efficient approach for simulation-based inverse design of airfoil shapes using variable-fidelity computational fluid dynamics models and manifold mapping (MM). Inverse design involves determining an airfoil shape fulfilling a given target performance characteristic. In particular, the pressure coefficient distribution is typically used in aerodynamic inverse design. Such a task can be challenging when using computationally expensive simulations. In the context of local optimization, the MM technique searches for a new design in the vicinity of the current design by constructing a fast multi-fidelity model, which is setup by the available evaluations of each of the high- and low-fidelity models at the current design. The MM-based multi-fidelity model predicts the high-fidelity model response at the new design by evaluating the low-fidelity model at the new design and applying the MM mapping. The MM-based multi-fidelity model is embedded within the trust-region algorithm and terminates based on the convergence of the argument, objective, and trust-region radius to yield the optimal design. The MM-based multi-fidelity algorithm only needs one high-fidelity model evaluation per design iteration. The proposed approach is illustrated on the inverse design of airfoils in transonic inviscid flow with the NACA 2412 airfoil as baseline and the pressure distribution of the RAE 2822 airfoil at Mach 0.734 and lift coefficient 0.824 as the target. Using eight B-spline design variables, the results indicate the MM technique is able to reach the target distribution at a low computational cost when compared to derivative-free local search. © 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Airfoils

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A transferable machine-learning model of the electron density

arXiv

引用

arXiv 2018年

作者： Grisa, Andrea Wilkins, David M. Meyer, Benjamin A.R. Fabrizio, Alberto Corminboeuf, Clemence Ceriotti, Michele Laboratory of Computational Science and Modeling IMX Ecole Polytechnique Federale de Lausanne Lausanne1015 Switzerland Laboratory for Computational Molecular Design Institute of Chemical Sciences and Engineering Ecole Polytechnique Federale de Lausanne LausanneCH-1015 Switzerland

The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost. Applications are shown for various hydrocarbon molecules of increasing complexity and exibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, initialize electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Molecules

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