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DeePKS + ABACUS as a Bridge between Expensive Quantum mechanical Models and Machine Learning Potentials

Journal of Physical Chemistry A 2022年第49期126卷 9154-9164页

作者： Li, Wenfei Ou, Qi Chen, Yixiao Cao, Yu Liu, Renxi Zhang, Chunyi Zheng, Daye Cai, Chun Wu, Xifan Wang, Han Chen, Mohan Zhang, Linfeng AI for Science Institute Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States HEDPS CAPT College of Engineering School of Physics Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China DP Technology Beijing100080 China

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different levels of QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training an ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), an ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model and then use the DeePKS model to label a much larger amount of configurations to train an ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open source and ready for use in various applications. © 2022 The Journal of Physical Chemistry A. All right reserved.

关键词： Density functional theory

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MULTI-OBJECTIVE DEVELOPMENT OF MACHINE-LEARNT CLOSURES FOR FULLY INTEGRATED TRANSITION AND WAKE MIXING PREDICTIONS IN LOW PRESSURE TURBINES

MULTI-OBJECTIVE DEVELOPMENT OF MACHINE-LEARNT CLOSURES FOR F...

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ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition, GT 2022

作者： Akolekar, Harshal D. Pacciani, Roberto Waschkowski, Fabian Zhao, Yaomin Department of Mechanical Engineering Indian Institute of Technology Jodhpur342037 India University of Melbourne VIC3010 Australia Department of Industrial Engineering University of Florence Italy Department of Mechanical Engineering University of Melbourne VIC3010 Australia Center for Applied Physics and Technology HEDPS College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9780791886113

In low pressure turbines (LPT), due to the low Reynolds number a large part of the blade boundary layer remains laminar and transition may occur due to flow separation. The boundary layer details at the blade trailing edge can change substantially depending on the transition region topology and can strongly influence the wake mixing occurring downstream. Accurately predicting these flow phenomena still poses a challenge for Reynolds averaged Navier-Stokes (RANS) and unsteady RANS methods. In this work a recently developed computational fluid dynamics (CFD) driven machine learning framework featuring multi-expression, multi-objective optimization is exploited for the first time to simultaneously develop transition models and turbulence closures in a fully coupled way, aimed at improving both transition and wake mixing predictions in LPTs. The T106A blade cascade with an isentropic Reynolds number of 100,000 is adopted as a training case. The baseline transition model is based on a laminar kinetic energy transport approach, and the machine learning approach is used to reformulate the source terms as functions of suitably defined non-dimensional ratios. Additionally, machine learning based explicit algebraic Reynolds stress models are used to improve wake mixing predictions, making use of a specifically and newly developed wake sensing function based strategy that allows an automated zonal application of the developed models. It is shown that both on-blade performance and wake mixing can be predicted accurately with data-driven transition and turbulence models that have benefited from CFD feedback in their development, ensuring that their mutual interactions are captured. © 2022 by ASME.

关键词： Forecasting

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Two-Layer High-Throughput:Effective Mass Calculations Including Warping

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engineering 2022年第3期8卷 74-80页

作者： Andrew Supka Nicholas A.Mecholsky Marco Buongiorno Nardelli Stefano Curtarolo Marco Fornari Department of Physics&Science of Advanced Materials Program Central Michigan UniversityMount PleasantMI 48859USA Department of Physics&Vitreous State Laboratory The Catholic University of AmericaWashingtonDC 20064USA Department of Physics&Department of Chemistry University of North TexasDentonTX 76203USA Center for Autonomous Materials Design Duke UniversityDurhamNC 27708USA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA

In this paper,we perform two-layer high-throughput *** the first layer,which involves changing the crystal structure and/or chemical composition,we analyze selected Ⅲ-Ⅴ semiconductors,filled and unfilled skutterudites,as well as rock salt and layered *** second layer searches the full Brillouin zone(BZ)for critical points within 1.5 eV(1 eV=1.602176×10^(-19)J)of the Fermi level and characterizes those points by computing the effective *** introduce several methods to compute the effective masses from first principles and compare them to each *** approach also includes the calculation of the density-of-states effective masses for warped critical points,where traditional approaches fail to give consistent results due to an underlying non-analytic behavior of the critical *** demonstrate the need to consider the band structure in its full complexity and the value of complementary approaches to compute the effective *** also provide computational evidence that warping occurs only in the presence of degeneracies.

关键词： High-throughput Electronic structure Band warping Effective mass

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Defect-engineered localized exciton in monolayer WSe2

Defect-engineered localized exciton in monolayer WSe2

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CLEO: Science and Innovations, S and I 2022

作者： Maiti, Rishi Chen, Cecilia Rajendran, Anjaly Yao, Kaiyuan Bhatt, Gaurang Hone, James Lipson, Michal Gaeta, Alex L. Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States Department of Electrical Engineering Columbia University New YorkNY10027 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States

Here, we demonstrate controlled defect engineering in monolayer WSe2 via UV-ozone treatment. The creation of Tungsten oxyselenide (TOS) shows broad red-shifted PL emission centered at ~770 nm at room temperature. ... 详细信息

ISBN: (纸本)9781557528209

关键词： Monolayers

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Control of Magnetic Skyrmions in an Exchange Biased van der Waals Ferromagnet

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1710-1711页

作者： McCray, Arthur R C Lebedev, Dmitry Arpaci, Sevdenur Te Velthuis, Suzanne G E Lopez-Dominguez, Victor Amiri, Pedram Khalili Hersam, Mark C. Petford-Long, Amanda K. Phatak, Charudatta Materials Science Division Argonne National Laboratory Lemont IL United States Applied Physics Program Northwestern University Evanston IL United States Department of Materials Science and Engineering Northwestern University Evanston IL United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States

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3D Optofluidic Control Using Reconfigurable Thermal Barriers

arXiv

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arXiv 2024年

作者： Schmidt, Falko González-Gómez, Carlos David Ruiz-Reina, Emilio Rica, Raúl A. Arroyo, Jaime Ortega Quidant, Romain Department of Mechanical and Process Engineering ETH Zurich Zurich8092 Switzerland Universidad de Granada Department of Applied Physics Granada18071 Spain Department of Applied Physics II University of Malaga Malaga29071 Spain Granada18071 Spain

Microfluidics has revolutionized control over small volumes through the use of physical barriers. However, the rigidity of these barriers limits flexibility in applications. We present an optofluidic toolbox that leverages structured light and photothermal conversion to create dynamic, reconfigurable fluidic boundaries. This system enables precise manipulation of fluids and particles by generating 3D thermal landscapes with high spatial control. Our approach replicates the functions of traditional barriers while additionally allowing real-time reconfiguration for complex tasks, such as individual particle steering and size-based sorting in heterogeneous mixtures. These results highlight the platform’s potential for adaptive and multifunctional microfluidic systems in applications such as chemical synthesis, lab-on-chip devices, and microbiology, seamlessly integrating with existing setups due to its flexibility and minimal operation requirements. © 2024, CC BY.

关键词： Microfluidics

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Low-symmetry vacancy-related spin qubit in hexagonal boron nitride

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npj Computational Materials 2024年第1期10卷 1318-1326页

作者： Rohit Babar Gergely Barcza Anton Pershin Hyoju Park Oscar Bulancea Lindvall GergőThiering Örs Legeza Jamie H.Warner Igor A.Abrikosov Adam Gali Viktor Ivády HUN-REN Wigner Research Centre for Physics PO Box 49H-1525 BudapestHungary MTA-ELTE Lendület“Momentum”NewQubit Research Group Pázmány PéterSétány 1/A1117 BudapestHungary Department of Atomic Physics Insitute of PhysicsBudapest University of Technology and EconomicsMűegyetem rakpart 3.H-1111 BudapestHungary Walker Department of Mechanical Engineering and Materials Graduate Program Texas Materials InstituteThe University of Texas at AustinAustinTX78712USA Department of Physics Chemistry and BiologyLinköping UniversitySE-58183 LinköpingSweden Institute for Advanced Study Technical University of MunichLichtenbergstrasse 2a85748 GarchingGermany MTA-WFK Lendület“Momentum”Semiconductor Nanostructures Research Group PO Box 49H-1525 BudapestHungary Department of Physics of Complex Systems Eötvös Loránd UniversityEgyetem tér 1-3H-1053 BudapestHungary

Point defect qubits in semiconductors have demonstrated their outstanding capabilities for high spatial resolution sensing generating broad multidisciplinary *** boron nitride(hBN)hosting point defect qubits have recently opened up new horizons for quantum sensing by implementing sensing *** sensitivity of point defect sensors in hBN is currently limited by the linewidth of the magnetic resonance signal,which is broadened due to strong hyperfine couplings.

关键词： boron defect hexagonal

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Unsupervised Skin Feature Tracking with Deep Neural Networks

arXiv

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arXiv 2024年

作者： Chang, Jose Ramon Nordling, Torbjörn E.M. Department of Mechanical Engineering National Cheng Kung University Tainan701 Taiwan Department of Applied Physics and Electronics Umeå University Umeå90187 Sweden

Facial feature tracking is essential in imaging ballistocardiography for accurate heart rate estimation and enables motor degradation quantification in Parkinson’s disease through skin feature tracking. While deep convolutional neural networks have shown remarkable accuracy in tracking tasks, they typically require extensive labeled data for supervised training. Our proposed pipeline employs a convolutional stacked autoencoder to match image crops with a reference crop containing the target feature, learning deep feature encodings specific to the object category in an unsupervised manner, thus reducing data requirements. To overcome edge effects making the performance dependent on crop size, we introduced a Gaussian weight on the residual errors of the pixels when calculating the loss function. Training the autoencoder on facial images and validating its performance on manually labeled face and hand videos, our Deep Feature Encodings (DFE) method demonstrated superior tracking accuracy with a mean error ranging from 0.6 to 3.3 pixels, outperforming traditional methods like SIFT, SURF, Lucas Kanade, and the latest transformers like PIPs++ and CoTracker. Overall, our unsupervised learning approach excels in tracking various skin features under significant motion conditions, providing superior feature descriptors for tracking, matching, and image registration compared to both traditional and state-of-the-art supervised learning methods. © 2024, CC BY.

关键词： Crops

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Connecting polymer collapse and the onset of jamming

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Physical Review E 2024年第3期109卷 034406-034406页

作者： Alex T. Grigas Aliza Fisher Mark D. Shattuck Corey S. O'Hern Graduate Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical and Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Mechanical Engineering and Materials Science Yale University New Haven Connecticut 06520 USA Benjamin Levich Institute and Physics Department The City College of New York New York New York 10031 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA

Previous studies have shown that the interiors of proteins are densely packed, reaching packing fractions that are as large as those found for static packings of individual amino-acid-shaped particles. How can the interiors of proteins take on such high packing fractions given that amino acids are connected by peptide bonds and many amino acids are hydrophobic with attractive interactions? We investigate this question by comparing the structural and mechanical properties of collapsed attractive disk-shaped bead-spring polymers to those of three reference systems: static packings of repulsive disks, of attractive disks, and of repulsive disk-shaped bead-spring polymers. We show that the attractive systems quenched to temperatures below the glass transition T≪Tg and static packings of both repulsive disks and bead-spring polymers possess similar interior packing fractions. Previous studies have shown that static packings of repulsive disks are isostatic at jamming onset, i.e., the number of interparticle contacts Nc matches the number of degrees of freedom, which strongly influences their mechanical properties. We find that repulsive polymer packings are hypostatic at jamming onset (i.e., with fewer contacts than degrees of freedom) but are effectively isostatic when including stabilizing quartic modes, which give rise to quartic scaling of the potential energy with displacements along these modes. While attractive disk and polymer packings are often considered hyperstatic with excess contacts over the isostatic number, we identify a definition for interparticle contacts for which they can also be considered as effectively isostatic. As a result, we show that the mechanical properties (e.g., scaling of the potential energy with excess contact number and low-frequency contribution to the density of vibrational modes) of weakly attractive disk and polymer packings are similar to those of isostatic repulsive disk and polymer packings. Our results demonstrate that static

关键词： Jamming Polymer folding Protein folding Single polymer chains

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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