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Deep learning of accurate force field of ferroelectric HfO2

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Physical Review B 2021年第2期103卷 024108-024108页

作者： Jing Wu Yuzhi Zhang Linfeng Zhang Shi Liu Fudan University Shanghai 200433 China School of Science Westlake University Hangzhou Zhejiang 310024 China Institute of Natural Sciences Westlake Institute for Advanced Study Hangzhou Zhejiang 310024 China Yuanpei College Peking University Beijing 100871 China Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Key Laboratory for Quantum Materials of Zhejiang Province Hangzhou Zhejiang 310024 China

The discovery of ferroelectricity in HfO2-based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density nonvolatile memories. The functional performances of ferroelectrics are intimately related to their dynamic responses to external stimuli such as electric fields at finite temperatures. Molecular dynamics is an ideal technique for investigating dynamical processes on large length and time scales, though its applications to new materials are often hindered by the limited availability and accuracy of classical force fields. Here we present a deep neural network–based interatomic force field of HfO2 learned from ab initio data using a concurrent learning procedure. The model potential is able to predict structural properties such as elastic constants, equation of states, phonon dispersion relationships, and phase transition barriers of various hafnia polymorphs with accuracy comparable with density functional theory calculations. The validity of this model potential is further confirmed by the reproduction of experimental sequences of temperature-driven ferroelectric-paraelectric phase transitions of HfO2 with isobaric-isothermal ensemble molecular dynamics simulations. We suggest a general approach to extend the model potential of HfO2 to related material systems including dopants and defects.

关键词： Interatomic & molecular potentials Structural properties Ferroelectrics Artificial neural networks Molecular dynamics

High-temperature mid-IR absorption and reaction kinetics of 2-methyl-1,3-dioxolane: An experimental and theoretical study

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Journal of Photochemistry and Photobiology 2023年 13卷

作者： Adil, Mohammad Giri, Binod Raj Mai, Tam V.-T. Szőri, Milán Huynh, Lam K. Farooq, Aamir Mechanical Engineering Program Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia KAUST Clean Combustion Research Center King Abdullah University of Science and Technology Saudi Arabia Molecular Science and Nano-Materials Lab Institute for Computational Science and Technology SBI Building Quang Trung Software City Tan Chanh Hiep Ward District 12 Ho Chi Minh Viet Nam University of Science Vietnam National University – HCMC 227 Nguyen Van Cu Ward 4 District 5 Ho Chi Minh Viet Nam Vietnam National University – HCMC Quarter 6 Linh Trung Ward Thu Duc Ho Chi Minh Viet Nam Institute of Chemistry University of Miskolc Egyetemváros A/2 Miskolc H-3515 Hungary School of Chemical and Environmental Engineering International University Quarter 6 Linh Trung Ward Thu Duc City Ho Chi Minh City Viet Nam

This work reports the mid-IR spectroscopy and reaction kinetics of 2-methyl-1,3-dioxolane (2M13DO). We carried out spectroscopic measurements to deduce temperature-dependent absorption cross-sections of 2M13DO over a broad wavelength range of 8.4–10.5 μm (950–1190 cm⁻¹). For these measurements, we employed a rapidly tuning MIRcat-QT™ laser that can be operated either at a fixed wavelength or scanned mode over wide wavelength regions. By operating the laser at a fixed wavelength, we monitored the decay of 2M13DO behind reflected shock waves over T₅ = 1050–1400 K and P₅ = 0.7 and 2.6 bar. Our measured concentration time-histories of 2M13DO allowed us to directly extract the overall rate coefficients for the unimolecular decomposition of 2M13DO using the first-order rate law. We did not observe any pressure dependence in the measured rate coefficients, indicating that the reaction is close to the high-pressure limit. By employing the W1U composite method, we explored the important pyrolysis reaction pathways of 2M13DO in the reactive potential energy surface. Three important reaction channels, namely, 2M13DO → CH₂[dbnd]CHOCH₂CH₂OH (IM1), 2M13DO → 2CH₃CHO (P3), 2M13DO → CH₃ + 1,3-dioxolan-2-yl (P4) were identified. Below 700 K, IM1 forming channel is dominant, whereas CH₃CHO formation is dominant under our experimental conditions. Above 1500 K, the radical forming channel (CH₃+P4) takes over other channels. At higher temperatures, the contribution of the radical forming channel continually increases, accounting for ∼ 99% at 2000 K. We used the stochastic RRKM-ME model to predict the pressure and temperature dependence of the rate coefficients, k(T, P), and time-resolved species profiles. Our theory showed excellent agreement with the measured rate coefficients. These are the first direct determination of the rate coefficients of the unimolecular decomposition of

关键词： 2-Methyl-1,3-dioxolane Absorption spectra High-temperature Pyrolysis RRKM-ME calculations Shock tube

Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer's disease ( Oct, 10.1038/s43587-021-00122-7, 2021)

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NATURE AGING

NATURE AGING 2021年第12期1卷 1202-1202页

作者： Taubes, Alice Nova, Phil Zalocusky, Kelly A. Kosti, Idit Bicak, Mesude Zilberter, Misha Y. Hao, Yanxia Yoon, Seo Yeon Oskotsky, Tomiko Pineda, Silvia Chen, Bin Jones, Emily Aery A. Choudhary, Krishna Grone, Brian Balestra, Maureen E. Chaudhry, Fayzan Paranjpe, Ishan De Freitas, Jessica Koutsodendris, Nicole Chen, Nuo Wang, Celine Chang, William An, Alice Glicksberg, Benjamin S. Sirota, Marina Huang, Yadong Gladstone Institute of Neurological Disease Gladstone Institutes San Francisco CA USA Biomedical Sciences Graduate Program University of California San Francisco CA USA Gladstone Center for Translational Advancement Gladstone Institutes San Francisco CA USA Department of Neurology University of California San Francisco CA USA Development and Stem Cell Biology Graduate Program University of California San Francisco CA USA Department of Pathology University of California San Francisco CA USA Bakar Computational Health Sciences Institute University of California San Francisco CA USA Department of Pediatrics University of California San Francisco CA USA Department of Surgery University of California San Francisco CA USA Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA Gladstone Institute of Data Science and Biotechnology Gladstone Institutes San Francisco CA USA

The evident genetic, pathological and clinical heterogeneity of Alzheimer’s disease (AD) poses challenges for traditional drug development. We conducted a computational drug-repurposing screen for drugs to treat apolipoprotein E4 (APOE4)-related AD. We first establishedAPOEgenotype-dependent transcriptomic signatures of AD by analyzing publicly available human brain databases. We then queried these signatures against the Connectivity Map database, which contains transcriptomic perturbations of more than 1,300 drugs, to identify those that best reverseAPOEgenotype-specific AD signatures. Bumetanide was identified as a top drug forAPOE4-related AD. Treatment ofAPOE4-knock-in mice without or with amyloid β (Aβ) accumulation using bumetanide rescued electrophysiological, pathological or cognitive deficits. Single-nucleus RNA sequencing revealed transcriptomic reversal of AD signatures in specific cell types in these mice, a finding confirmed inAPOE4induced pluripotent stem cell (iPSC)-derived neurons. In humans, bumetanide exposure was associated with a significantly lower AD prevalence in individuals over the age of 65 years in two electronic health record databases, suggesting the effectiveness of bumetanide in preventing AD.

关键词： OBSTRUCTIVE SLEEP-APNEA APOLIPOPROTEIN-E APOE EPSILON-4 EXPRESSION RISK E4 AGE ASSOCIATION METABOLISM MECHANISMS

Detecting Abnormal PCG Signals and Extracting Cardiac Information Employing Deep Learning and the Shannon Energy Envelope

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Detecting Abnormal PCG Signals and Extracting Cardiac Inform...

IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Chowdhury K. Poudel Y. Hu Computational Science Program Middle Tennessee State University Murfreesboro Tennessee USA East Central University Ada Oklahoma USA Middle Tennessee State University Murfreesboro Tennessee USA

ISBN: (数字)9781728188201

ISBN: (纸本)9781728188218

Phonocardiogram (PCG) is a computerized system that represents the heart sound recording. PCG reflects the acoustic behavior of the heart graphically through intensity, frequency, time duration, and other valuable information. An acoustic signal like PCG provides supplemental diagnostic information to Electrocardiogram (ECG) by extracting cardiac information from the heart sound those cannot be identified by hearing the heart sound. PCG is an objective and standard evaluation technique that can record the heart sound continuously for a long period of time and can also overcome the human hearing limitation [1] [2]. So, PCG plays a vital role to examine the heart sound as well as cardiac abnormalities which improves the overall diagnosis efficiency. We proposed a PCG classification method using the deep convolutional neural network (CNN). Our proposed method is not only able to classify PCG signals using CNN but can also segment PCG signals using the Shannon energy envelope method. This signal processing technique provides significant information regarding the heart condition that helps to detect heart diseases in the primary phase.

关键词： Heart Time-frequency analysis Auditory system Signal processing Acoustics Data mining Phonocardiography

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

Universal relation for supernova gravitational waves

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Physical Review D 2021年第12期104卷 123009-123009页

作者： Hajime Sotani Tomoya Takiwaki Hajime Togashi Astrophysical Big Bang Laboratory RIKEN Saitama 351-0198 Japan Interdisciplinary Theoretical and Mathematical Science Program (iTHEMS) RIKEN Saitama 351-0198 Japan Division of Science National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Center for Computational Astrophysics National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo 181-8588 Japan Department of Physics Tohoku University Sendai 980-8578 Japan

Using the numerical simulation data for two-dimensional core-collapse supernova, we examine the protoneutron star (PNS) asteroseismology with the relativistic Cowling approximation. As shown in the previous study, e.g., [H. Sotani and T. Takiwaki, Mon. Not. R. Astron. Soc. 498, 3503 (2020).], the gravitational wave signals appearing in the numerical simulation can be well identified with the gravity (fundamental) oscillation in the early (later) phase before (after) the avoided crossing between the gravity and fundamental oscillations. On the other hand, the time evolution of supernova gravitational waves strongly depends on the PNS models, such as the progenitor mass and the equation of state for dense matter. Nevertheless, we find that the fundamental and gravity mode frequencies according to the gravitational wave signals appearing in the numerical simulations can be expressed as a function of the protoneutron star average density independently of the PNS models. Using the average density, we derive the empirical formula for supernova gravitational wave frequency. In addition, we confirm that the dependence of the PNS surface density on the PNS average density is almost independent of the PNS models, and we also discuss how the different treatment of the nonuniform matter in the equation of state affects the observables.

关键词： Equations of state Gravitational waves Neutron stars & pulsars

Core packing of well-defined x-ray and NMR structures is the same

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arXiv 2022年

作者： Grigas, Alex T. Liu, Zhuoyi Regan, Lynne O’Hern, Corey S. Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Centre for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh United Kingdom Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Numerous studies have investigated the differences and similarities between protein structures determined by solution NMR spectroscopy and those determined by x-ray crystallography. A fundamental question is whether any observed differences are due to differing methodologies, or to differences in the behavior of proteins in solution versus in the crystalline state. Here, we compare the properties of the hydrophobic cores of high-resolution protein crystal structures and those in NMR structures, determined using increasing numbers and types of restraints. Prior studies have reported that many NMR structures have denser cores compared to those of high-resolution x-ray crystal structures. Our current work investigates this result in more detail, and finds that these NMR structures tend to violate basic features of protein stereochemistry, such as small non-bonded atomic overlaps and few Ramachandran and side chain dihedral angle outliers. We find that NMR structures solved with more restraints, and which do not significantly violate stereochemistry, have hydrophobic cores that have a similar size and packing fraction as their counterparts determined by x-ray crystallography at high-resolution. These results lead us to conclude that, at least regarding the core packing properties, high-quality structures determined by NMR and x-ray crystallography are the same, and the differences reported earlier are most likely a consequence of methodology, rather than fundamental differences between the protein in the two different environments. Significance: Dense packing of hydrophobic residues is key to protein structure and stability. Previously, it has been noted that structures solved by NMR spectroscopy have denser cores than x-ray crystal structures. Here, we calculate the core size and packing fraction of NMR structures with experimental restraints in the PDB. Their cores are typically smaller, but denser. However, NMR structures with accurate stereochemistry possess core pa

关键词： X ray crystallography

Characterization of void space, large-scale structure, and transport properties of maximally random jammed packings of superballs

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arXiv 2021年

作者： Maher, Charles Emmett Stillinger, Frank H. Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Dense, disordered packings of particles are useful models of low-temperature amorphous phases of matter, biological systems, granular media, and colloidal systems. The study of dense packings of nonspherical particles enables one to ascertain how rotational degrees of freedom affect packing behavior. Here, we study superballs, a large family of deformations of the sphere, defined in three dimensions by |x1|2p + |x2|2p + |x3|2p ≤ 1, where p ∈ (0, ∞) is a deformation parameter indicating to what extent the shape deviates from a sphere. As p increases from the sphere point (p = 1), the superball tends to a cuboidal shape and approaches a cube in the p → ∞ limit. As p → 0.5, it approaches an octahedron, becomes a concave body with octahedral symmetry for p ¯) vary nonanalytically as p diverges from unity. Here, we use an event-driven molecular dynamics algorithm to produce MRJ superball packings with 0.85 ≤ p ≤ 1.50. To supplement the previous work on such packings, we characterize their large-scale structure by examining the behaviors of their structure factors S(Q) and spectral densities χV (Q), as the wave number Q tends to zero, and find that these packings are effectively hyperuniform for all values of p examined. We show that the mean width w¯ is a useful length scale to make distances dimensionless in order to compare systematically superballs of different shape. Moreover, we compute the complementary cumulative pore-size distribution F (δ) and find that the pore sizes tend to decrease as |1 − p| increases. From F (δ), we estimate how the fluid permeability, mean survival time, and principal diffusion relaxation time vary as a function of p. Additionally, we compute the diffusion "spreadability" S(t) [Torquato, Phys. Rev. E, 104, 054102, (2021)] of these packings and find that the long-time power-law scaling indicates these packings are hyperuniform with a small-Q power law scaling of the spectral density χV (Q) ∼ Qα with an exponent α that ranges from 0.64 at th

关键词： Spheres

Kinetic Frustration Effects on Dense Two-Dimensional Packings of Convex Particles and Their Structural Characteristics

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arXiv 2021年

The study of hard-particle packings is of fundamental importance in physics, chemistry, cell biology, and discrete geometry. Much of the previous work on hard-particle packings concerns their densest possible arrangements. By contrast, we examine kinetic effects inevitably present in both numerical and experimental packing protocols. Specifically, we determine how changing the compression/shear rate of a two-dimensional packing of noncircular particles causes it to deviate from its densest possible configuration, which is always periodic. The adaptive shrinking cell (ASC) optimization scheme maximizes the packing fraction of a hard-particle packing by first applying random translations and rotations to the particles and then isotropically compressing and shearing the simulation box repeatedly until a possibly jammed state is reached. We use a stochastic implementation of the ASC optimization scheme to mimic different effective time scales by varying the number of particle moves between compressions/shears. We generate dense, effectively jammed, monodisperse, two-dimensional packings of obtuse scalene triangle, rhombus, curved triangle, lens, and "ice cream cone" (a semicircle grafted onto an isosceles triangle) shaped particles, with a wide range of packing fractions and degrees of order. To quantify these kinetic effects, we introduce the kinetic frustration index K, which measures the deviation of a packing from its maximum possible packing fraction. To investigate how kinetics affect short- and long-range ordering in these packings, we compute their spectral densities χV (k) and characterize their contact networks. We find that kinetic effects are most significant when the particles have greater asphericity, less curvature, and less rotational symmetry. This work may be relevant to the design of laboratory packing protocols. Copyright © 2021, The Authors. All rights reserved.

关键词： Kinetic theory