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Quantum fluids in nanoporous media——Effects of the confinement and fractal geometry

Chinese Science Bulletin 2011年第34期56卷 3617-3622页

作者： TAYURSKII Dmitrii lysogorskiy yury Department of Physics Kazan Federal University

The complex behavior of such quantum fluids like liquid 4He and liquid 3He in nanoporous media is determined by spatial quantization because of geometrical confinement as well as by significant contribution from the surface atoms. In the present report we will review the procedure, results and discuss the issues for fractionalized nonextensive hydrodynamical approach to describe the properties of quantum fluids inside nanopores and propose consideration of strong correlated quantum liquid by means of fractionalized Schro¨dinger equation.

关键词：量子流体多孔介质分形几何薛定谔方程复杂行为报告审查力学方法量子液体

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Performant implementation of the atomic cluster expansion(PACE)and application to copper and silicon

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npj Computational Materials 2021年第1期7卷 878-889页

作者： yury lysogorskiy Cas van der Oord Anton Bochkarev Sarath Menon Matteo Rinaldi Thomas Hammerschmidt Matous Mrovec Aidan Thompson Gábor Csányi Christoph Ortner Ralf Drautz ICAMS Ruhr-Universität BochumBochumGermany Engineering Laboratory University of CambridgeCambridgeUK Center for Computing Research Sandia National LaboratoriesAlbuquerqueNMUSA Department of Mathematics University of British ColumbiaVancouverBCCanada

The atomic cluster expansion is a general polynomial expansion of the atomic energy in multi-atom basis *** we implement the atomic cluster expansion in the performant C++code PACE that is suitable for use in large-scale atomistic *** briefly review the atomic cluster expansion and give detailed expressions for energies and forces as well as efficient algorithms for their *** demonstrate that the atomic cluster expansion as implemented in PACE shifts a previously established Pareto front for machine learning interatomic potentials toward faster and more accurate ***,general purpose parameterizations are presented for copper and silicon and evaluated in *** show that the Cu and Si potentials significantly improve on the best available potentials for highly accurate large-scale atomistic simulations.

关键词： functions expansion cluster

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Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition

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npj Computational Materials 2019年第1期5卷 150-160页

作者： Christopher Sutton Luca M.Ghiringhelli Takenori Yamamoto yury lysogorskiy Lars Blumenthal Thomas Hammerschmidt Jacek R.Golebiowski Xiangyue Liu Angelo Ziletti Matthias Scheffler Fritz Haber Institute of the Max Planck Society BerlinGermany Research Institute for Mathematical and Computational Sciences(RIMCS) YokohamaJapan ICAMS Ruhr-Universität BochumBochumGermany EPSRC Centre for Doctoral Training on Theory and Simulation of Materials Department of PhysicsImperial College LondonLondonUK Thomas Young Centre for Theory and Simulation of Materials Department of MaterialsImperial College LondonLondonUK

A public data-analytics competition was organized by the Novel Materials Discovery(NOMAD)Centre of Excellence and hosted by the online platform Kaggle by using a dataset of 3,000(Al_(x)GayIn_(1-x-y))_(2)O_(3) *** aim was to identify the best machinelearning(ML)model for the prediction of two key physical properties that are relevant for optoelectronic applications:the electronic bandgap energy and the crystalline formation ***,we present a summary of the top-three ranked ML *** first-place solution was based on a crystal-graph representation that is novel for the ML of properties of *** second-place model combined many candidate descriptors from a set of compositional,atomic-environment-based,and average structural properties with the light gradient-boosting machine regression *** third-place model employed the smooth overlap of atomic position representation with a neural *** Pearson correlation among the prediction errors of nine ML models(obtained by combining the top-three ranked representations with all three employed regression models)was examined by using the Pearson correlation to gain insight into whether the representation or the regression model determines the overall model *** relatively decorrelated models(based on the Pearson correlation)leads to an even higher prediction accuracy.

关键词： energy prediction overlap

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