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Linking the polyatomic arrangements of interstitial H2O and cations to bonding within Prussian blue analogues ab initio using gradient-boosted machine learning

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Physical Review Materials 2021年第3期5卷 035003-035003页

作者： Sizhe Liu Kyle C. Smith Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Materials Science and Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Computational Science and Engineering Program University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

Prussian blue analogues (PBAs) are model host compounds for the intercalation of monovalent cations for electrochemical energy storage and separations. However, the interactions among interstitial species and their effects on atomic arrangements therein are understood mainly at a phenomenological level. Analyzing correlations between electronic interactions and polyatomic arrangements in hydrated Prussian blue analogues is complicated by the nonlocal hydrogen-bonding interactions between zeolitic water and framework lattices. Here, we train machine-learning (ML) models to learn DFT-calculated energy landscapes of nickel hexacyanoferrate PBA lattices with various lattice hydration degrees, oxidation states, and types of intercalated alkali cations based on various three-particle feature parameters. This ML approach is enabled by using gradient-boosted regression trees with features that are rotationally invariant geometric parameters. ML model accuracy is shown to be a cation-specific indicator of correlations between energy and polyatomic arrangements. Overlap population analysis among correlated atoms further confirms that such correlations are caused by the competition for dative bonding between Lewis-acid intercalated cations and Lewis bases (cyanide and oxygen in H2O). Examination of lowest-energy structures reveals that cation hydrophilicity and bare ionic radius determine dative-bonding strength, resulting in cation-H2O ordering in interstitial space. The projected energy landscapes of hydrated PBA lattices is also explored in subspaces spanned by certain many-particle feature parameters inspired by ML analysis. The downhill traces in such landscapes indicate that lattice distortion is accompanied by two kinds of collective movements: (1) rearrangements in the hydration shells around small and hydrophilic cations and (2) collective attack of H2O molecules on nickel-cyanide bonds promoted by large, hydrophobic cations.

关键词： Chemical binding Density functional theory Electrochemistry Electron correlation calculations for atoms & ions Electronic structure of atoms & molecules Energy storage Interatomic & molecular potentials Organic & organometallic chemistry Water Data mining Electronic structure Machine learning

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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems 32

End-to-end symmetry preserving inter-atomic potential energy...

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32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Zhang, Linfeng Han, Jiequn Wang, Han Saidi, Wissam A. Car, Roberto Weinan, E. Program in Applied and Computational Mathematics Princeton University United States Institute of Applied Physics and Computational Mathematics China CAEP Software Center for High Performance Numerical Simulation China Department of Mechanical Engineering and Materials Science University of Pittsburgh United States Department of Chemistry Department of Physics Princeton University United States Princeton Institute for the Science and Technology of Materials Princeton University United States Department of Mathematics Princeton University United States Beijing Institute of Big Data Research China

Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity. © 2018 Curran Associates Inc..All rights reserved.

关键词： Potential energy

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Application of evidence theory in radiation oncology outcome analysis

Application of evidence theory in radiation oncology outcome...

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International Conference on BioMedical engineering and Informatics (BMEI)

作者： Ying Xiao Yunfeng Cui Wenzhou Chen Yan Yu James Galvin M. Yousuff Hussaini Department of Radiation Oncology Thomas Jefferson University Philadelphia USA Department of Mathematics Program in Computational Science & Engineering Florida State University Tallahassee USA

In this paper, we apply the Dempster-Shafer theory to outcome analysis in radiation oncology. Evidence from different sources, some of them inconsistent, is combined by Dempster's rule to provide an innovative met... 详细信息

关键词： Lungs Biomedical applications of radiation Oncology Probability Predictive models Cancer Context modeling

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Basic Issues in Vector High Dimensional Model Representation

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AIP Conference Proceedings 2011年第1期1389卷 1143-1146页

作者： Burcu Tunga Metin Demiralp aIstanbul Technical University Faculty of Computer and Informatics Maslak 34469 Istanbul Turkey bIstanbul Technical University Informatics Institute Computational Science and Engineering Program Maslak 34469 Istanbul Turkey

A multivariate modelling may involve a set of multivariate functions. A vector valued function structure can be used to mathematically express the given problem and each multivariate function can be considered as an element of this vector. This work aims to construct a new approach representing the elements of this vector structure in terms of less‐variate functions to reduce the computational complexity. For this purpose, a new method based on the plain High Dimensional Model Representation (HDMR) philosophy is developed. The basic concepts of this method and several illustrative numerical implementations are given here.

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Coarse-grained modeling and simulation of actin filament behavior based on Brownian dynamics method

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MCB Molecular and Cellular Biomechanics 2009年第3期6卷 161-173页

作者： Shimada, Yoshitaka Adachi, Taiji Inoue, Yasuhiro Hojo, Masaki Department of Mechanical Engineering and Science Kyoto University Yoshida-Honmachi Sakyo Kyoto 606-8501 Japan Computational Cell Biomechanics Team VCAD System Research Program RIKEN Japan

The actin filament, which is the most abundant component of the cytoskeleton, plays important roles in fundamental cellular activities such as shape determination, cell motility, and mechanosensing. In each activity, the actin filament dynamically changes its structure by polymerization, depolymerization, and severing. These phenomena occur on the scales ranging from the dynamics of actin molecules to filament structural changes with its deformation due to the various forces, for example, by the membrane and solvent. To better understand the actin filament dynamics, it is important to focus on these scales and develop its mathematical model. Thus, the objectives of this study were to model and simulate actin filament polymerization, depolymerization, and severing based on the Brownian dynamics method. In the model, the actin monomers and the solvent were considered as globular particles and a continuum, respectively. The motion of the actin molecules was assumed to follow the Langevin equation. The polymerization, which increases the filament length, was determined by the distance between the center of the actin particle at the barbed end and actin particles in the solvent. The depolymerization, which decreases the filament length, was modeled such that the number of dissociation particles from the filament end per unit time was constant. In addition, the filament severing, in which one filament divides into two, was modeled to occur at an equal rate along the filament. Then, we simulated the actin filament dynamics using the developed model, and analyzed the filament elongation rate, its turnover, and the effects of filament severing on the polymerization and depolymerization. Results indicated that the model reproduced the linear dependence of the filament elongation on time, filament turnover process by polymerization and depolymerization, and acceleration of the polymerization and depolymerization by severing, which qualitatively agreed with those observed in ex

关键词： Coarse-grained modeling

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An integrated transcriptomic cell atlas of human neural organoids (vol 635, pg 690, 2024)

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NATURE 2025年第8045期637卷 E21-E21页

作者： He, Zhisong Dony, Leander Fleck, Jonas Simon Szalata, Artur Li, Katelyn X. Sliskovic, Irena Lin, Hsiu-Chuan Santel, Malgorzata Atamian, Alexander Quadrato, Giorgia Sun, Jieran Pasca, Sergiu P. Camp, J. Gray Theis, Fabian J. Treutlein, Barbara Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Institute of Computational Biology Computational Health Center Helmholtz Munich Neuherberg Germany Department Genes and Environment Max Planck Institute of Psychiatry Munich Germany International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Physiology and Pharmacology Karolinska Institutet Stockholm Sweden Institute of Human Biology (IHB) Roche Pharma Research and Early Development Roche Innovation Center Basel Basel Switzerland Biozentrum University of Basel Basel Switzerland Department of Stem Cell Biology and Regenerative Medicine Keck School of Medicine University of Southern California Los Angeles CA USA Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC Keck School of Medicine University of Southern California Los Angeles CA USA Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA Stanford Brain Organogenesis Program Wu Tsai Neurosciences Institute and Bio-X Stanford CA USA Neural Stem Cell Institute Albany NY USA UK Dementia Research Institute at the University of Edinburgh Edinburgh Bioquarter Edinburgh UK Centre for Discovery Brain Sciences School of Biomedical Sciences College of Medicine and Veterinary Medicine The University of Edinburgh Edinburgh UK Human Technopole Milan Italy Department of Oncology and Hemato-Oncology University of Milan Milan Italy

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Characterization and quantification of gait deficits within gait phases using fuzzy-granular computing

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Journal of Biomedical science and engineering 2012年第12期5卷 720-728页

作者： Melaku A. Bogale Huiying Yu Thompson Sarkodie-Gyan Amr Abdelgawad Texas Tech University Health Science Center El Paso USA University of Texas at El Paso Computational Science Program El Paso USA University of Texas at El Paso Department of Electrical and Computer Engineering El Paso USA

People with neurological disorders like Cerebral Palsy (CP) and Multiple Sclerosis (MS) suffer associated functional gait problems. The symptoms and sign of these gait deficits are different between subjects and even within a subject at different stage of the disease. Identifying these gait related abnormalities helps in the treatment planning and rehabilitation process. The current gait assessment process does not provide very specific information within the seven gait phases. The objective of this study is to investigate the possible application of granular computing to quantify gait parameters within the seven gait phases. In this process we applied fuzzy-granular computing on the vertical ground reaction force (VGRF) and surface electromyography (sEMG) data to obtain respective characteristic values for each gait phase. A fuzzy similarity (FS) measure is used to compare patient values with age and sex matched control able-bodied group. We specifically applied and tested this approach on 10 patients (4 Cerebral Palsy and 6 Multiple Sclerosis) to identify possible gait abnormalities. Different FS values for VGRF for right and left leg is observed. The VGRF analysis shows smaller FS values during the swing phase in CP and MS subjects that are evidence of associated stability problem. Similarly, FS values for muscle activates of the four-selected muscle display a broad range of values due to difference between subjects. Degraded FS values for different muscles at different stage of the gait cycle are reported. Smaller FS values are sign of abnormal activity of the respective muscles. This approach provides individual centered and very specific information within the gait phases that can be employed for diagnosis, treatment and rehabilitation process.

关键词： Fuzzy-Granular Algorithm Gait Phases Fuzzy Similarity Cerebral Palsy Multiple Sclerosis

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Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

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Physical Review B 2020年第21期102卷 214113-214113页

作者： Jianhang Xu Chunyi Zhang Linfeng Zhang Mohan Chen Biswajit Santra Xifan Wu Department of Physics Temple University Philadelphia Pennsylvania 19122 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA CAPT HEDPS College of Engineering Peking University Beijing 100871 China Institute for Computational Molecular Science Temple University Philadelphia Pennsylvania 19122 USA

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H2O) and heavy water (D2O) within the isothermalisobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water are more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H2O and D2O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects.

关键词： Atomic & molecular structure Electronic structure Isotope effect Water Ab initio calculations Density functional theory Machine learning Molecular dynamics Path-integral methods

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computational fluid dynamics applied to the study of falling liquid films in a wave film generation facility 6

Computational fluid dynamics applied to the study of falling...

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6th ECCOMAS European Conference on computational Mechanics: Solids, Structures and Coupled Problems, ECCM 2018 and 7th ECCOMAS European Conference on computational Fluid Dynamics, ECFD 2018

作者： Iglesias, Susana M. Dominguez, Dany S. Escrivá, Alberto Muñoz-Cobo, José L. Berna, César Cuadros, José L. Rivera, Yago Universidade Estadual de Santa Cruz Postgraduate Program of Computational Modeling in Science and Technology Rodovia Jorge Amado km 16 Salobrinho Ilhéus BA45662-900 Brazil Universitat Politècnica de València Institute for Energy Engineering Camino de Vera 14 Valencia46022 Spain

ISBN: (纸本)9788494731167

Two phase gas-liquid flow appears in several engineering applications, such as boiling nuclear reactors, oil and gas industry, chemical plants, among others. For this phenomenon's study the experimental facility Wave Film Generator (GEPELON, Generador de Película Ondulatoria) was constructed. It uses conductivity sensors for the multi-phase flow analysis in vertical pipes. In this work, we propose the use of computational Fluid Dynamics for modelling the falling liquid films regime in this facility. Specifically, this air-water flow regime consists of a water film that falls down the pipe wall and a central region with stagnant air. We build up a simplified installation's geometrical model, considering a mass flow boundary condition in the inlet and constant pressure in the outlet. To capture the water-air interface a non-uniform structured mesh with fine elements close to the wall was constructed. Using a Volume of Fluid (VOF) model and the FLUENT solver, we simulate different flow rates and from the water volumetric fraction we estimate the film thickness in all the domain. The simulations are stationary and due to the hydrodynamic complexity, a pseudo-transient approach was used to stabilize the iterative process. The simulation values were compared with the experimental results obtained in the GEPELON facility. A mesh independence analysis was performed and several mass flows were simulated, showed good agreement with the experimental values for lower flows and reasonable for higher flows. copyright © Crown copyright (2018).All right reserved.

关键词： computational fluid dynamics

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Bayesian Parameter Inference for Partially Observed SDEs driven by Fractional Brownian Motion

arXiv

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arXiv 2022年

作者： Maama, Mohamed Jasra, Ajay Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion (fBM) models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time discretization, which seeks to sample from an approximation of the posterior and then corrects via importance sampling;the approximation reduces the time (in terms of total observation time T) by O(T). This method is extended by using a multilevel MCMC method which can reduce the computational cost to achieve a given mean square error (MSE) versus using a single time discretization. Our methods are illustrated on simulated and real data. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error

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