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Journal articles on the topic 'Computational physics|Computational chemistry'

Author: Grafiati

Published: 9 July 2021

Last updated: 1 February 2022

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1

Houk, K. N., and Peng Liu. "Using Computational Chemistry to Understand & Discover Chemical Reactions." Daedalus 143, no. 4 (October 2014): 49–66. http://dx.doi.org/10.1162/daed_a_00305.

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Chemistry, the “science of matter,” is the investigation of the fabulously complex interchanges of atoms and bonds that happen constantly throughout our universe and within all living things. Computational chemistry is the computer modeling of chemistry using mathematical equations that come from physics. The field was made possible by advances in computer algorithms and computer power and continues to flourish in step with developments in those areas. Computational chemistry can be thought of as both a time-lapse video that slows down processes by a quadrillion-fold and an ultramicroscope that provides a billion-fold magnification. Computational chemists can quantitatively simulate simple chemistry, such as the chemical reactions between molecules in interstellar space. The chemistry inside a living organism is dramatically more complicated and cannot be simulated exactly, but even here computational chemistry enables understanding and leads to discovery of previously unrecognized phenomena. This essay describes how computational chemistry has evolved into a potent force for progress in chemistry in the twenty-first century.

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Schneider, Barry I. "Looking Back at 45 Years of Computational Physics and Chemistry." Computing in Science & Engineering 19, no. 5 (2017): 4–5. http://dx.doi.org/10.1109/mcse.2017.3421543.

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3

Alonso, Pedro, Ian P. Hamilton, and J. Vigo-Aguiar. "Mathematical and computational methods with applications in chemistry and physics." Journal of Mathematical Chemistry 48, no. 1 (February 14, 2010): 95–97. http://dx.doi.org/10.1007/s10910-010-9661-y.

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4

Mignon, David, Karen Druart, Eleni Michael, Vaitea Opuu, Savvas Polydorides, Francesco Villa, Thomas Gaillard, Nicolas Panel, Georgios Archontis, and Thomas Simonson. "Physics-Based Computational Protein Design: An Update." Journal of Physical Chemistry A 124, no. 51 (November 10, 2020): 10637–48. http://dx.doi.org/10.1021/acs.jpca.0c07605.

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5

Mazumder, Sandip. "Modeling Full-Scale Monolithic Catalytic Converters: Challenges and Possible Solutions." Journal of Heat Transfer 129, no. 4 (July 24, 2006): 526–35. http://dx.doi.org/10.1115/1.2709655.

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Modeling full-scale monolithic catalytic converters using state-of-the-art computational fluid dynamics algorithms and techniques encounters a classical multiscale problem: the channels within the monolith have length scales that are ∼1–2 mm, while the converter itself has a length scale that is ∼5–10 cm. This necessitates very fine grids to resolve all the length scales, resulting in few million computational cells. When complex heterogeneous chemistry is included, the computational problem becomes all but intractable unless massively parallel computation is employed. Two approaches to address this difficulty are reviewed, and their effectiveness demonstrated for the computation of full-scale catalytic converters with complex chemistry. The first approach is one where only the larger scales are resolved by a grid, while the physics at the smallest scale (channel scale) are modeled using subgrid scale models whose development entails detailed flux balances at the “imaginary” fluid–solid interfaces within each computational cell. The second approach makes use of the in situ adaptive tabulation algorithm, after significant reformulation of the underlying mathematics, to accelerate computation of the surface reaction boundary conditions. Preliminary results shown here for a catalytic combustion application involving 19 species and 24 reactions indicate that both methods have the potential of improving computational efficiency by several orders of magnitude.

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Fujisaki, Hiroshi. "Physics and Chemistry Based Computational Approach to Conformational Change of Biomolecules." Nihon Ika Daigaku Igakkai Zasshi 9, no. 4 (2013): 202–6. http://dx.doi.org/10.1272/manms.9.202.

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7

Koch, Wolfram. "Buchbesprechung: Computational Methods in Physics, Chemistry and Biology. Von Paul Harrison." Angewandte Chemie 114, no. 14 (July 15, 2002): 2726–27. http://dx.doi.org/10.1002/1521-3757(20020715)114:14<2726::aid-ange2726>3.0.co;2-8.

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Fujisaki, Hiroshi. "Physics- and Chemistry-based Computational Approaches to Ligand Binding for Proteins." Nihon Ika Daigaku Igakkai Zasshi 9, no. 2 (2013): 135–39. http://dx.doi.org/10.1272/manms.9.135.

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9

Oberkampf, William L., Timothy G. Trucano, and Charles Hirsch. "Verification, validation, and predictive capability in computational engineering and physics." Applied Mechanics Reviews 57, no. 5 (September 1, 2004): 345–84. http://dx.doi.org/10.1115/1.1767847.

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Developers of computer codes, analysts who use the codes, and decision makers who rely on the results of the analyses face a critical question: How should confidence in modeling and simulation be critically assessed? Verification and validation (V&V) of computational simulations are the primary methods for building and quantifying this confidence. Briefly, verification is the assessment of the accuracy of the solution to a computational model. Validation is the assessment of the accuracy of a computational simulation by comparison with experimental data. In verification, the relationship of the simulation to the real world is not an issue. In validation, the relationship between computation and the real world, ie, experimental data, is the issue. This paper presents our viewpoint of the state of the art in V&V in computational physics. (In this paper we refer to all fields of computational engineering and physics, eg, computational fluid dynamics, computational solid mechanics, structural dynamics, shock wave physics, computational chemistry, etc, as computational physics.) We describe our view of the framework in which predictive capability relies on V&V, as well as other factors that affect predictive capability. Our opinions about the research needs and management issues in V&V are very practical: What methods and techniques need to be developed and what changes in the views of management need to occur to increase the usefulness, reliability, and impact of computational physics for decision making about engineering systems? We review the state of the art in V&V over a wide range of topics, for example, prioritization of V&V activities using the Phenomena Identification and Ranking Table (PIRT), code verification, software quality assurance (SQA), numerical error estimation, hierarchical experiments for validation, characteristics of validation experiments, the need to perform nondeterministic computational simulations in comparisons with experimental data, and validation metrics. We then provide an extensive discussion of V&V research and implementation issues that we believe must be addressed for V&V to be more effective in improving confidence in computational predictive capability. Some of the research topics addressed are development of improved procedures for the use of the PIRT for prioritizing V&V activities, the method of manufactured solutions for code verification, development and use of hierarchical validation diagrams, and the construction and use of validation metrics incorporating statistical measures. Some of the implementation topics addressed are the needed management initiatives to better align and team computationalists and experimentalists in conducting validation activities, the perspective of commercial software companies, the key role of analysts and decision makers as code customers, obstacles to the improved effectiveness of V&V, effects of cost and schedule constraints on practical applications in industrial settings, and the role of engineering standards committees in documenting best practices for V&V. There are 207 references cited in this review article.

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10

Koch, Wolfram. "Book Review: Computational Methods in Physics, Chemistry and Biology. By Paul Harrison." Angewandte Chemie International Edition 41, no. 13 (July 3, 2002): 2416. http://dx.doi.org/10.1002/1521-3773(20020703)41:13<2416::aid-anie2416>3.0.co;2-w.

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11

Šob, Mojmír. "Editorial for the Special Issue on Computational Quantum Physics and Chemistry of Nanomaterials." Nanomaterials 10, no. 12 (November 30, 2020): 2395. http://dx.doi.org/10.3390/nano10122395.

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12

Kozmutza, Cornelia, and Yolanda Picó. "To address accuracy and precision using methods from analytical chemistry and computational physics." Environmental Monitoring and Assessment 151, no. 1-4 (March 28, 2008): 59–75. http://dx.doi.org/10.1007/s10661-008-0249-y.

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13

Huang, Heng, Li Shen, James Ford, Yu Hang Wang, and Yu Rong Xu. "Computational Issues in Biomedical Nanometrics and Nano-Materials." Journal of Nano Research 1 (January 2008): 50–58. http://dx.doi.org/10.4028/www.scientific.net/jnanor.1.50.

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Biomedical Nanotechnology is an emerging area of great scientific and technological opportunity. It is widely recognized as one of the most potentially beneficial applications of nanotechnology to industry and society to date. Work in this area has a number of computational aspects: information technology based tools and measurement techniques are used to study biosystems with micro- and nano-scale physics and chemistry, and computational methods are helping to generate remarkable new insights into how biological systems function, how metabolic processes interrelate, and how new molecular scale machines can operate. This paper reviews current advances in computational algorithms and tools applied to biomedical nanometrics and nano-materials. We categorize algorithms into three general areas, describe representative methods, and conclude with several promising directions of future investigation.

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14

Catlow, C. R. A. "Computational solid state chemistry." Computational Materials Science 2, no. 1 (January 1994): 6–18. http://dx.doi.org/10.1016/0927-0256(94)90042-6.

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Lam, S. H. "REDUCED CHEMISTRY MODELING IN REACTING FLOWS." International Journal of Modern Physics C 05, no. 02 (April 1994): 225–27. http://dx.doi.org/10.1142/s0129183194000209.

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This paper reviews the need for reduced chemistry modeling for hypersonic reactive flows, and describes the method of computational singular perturbation which can computationally derive reduced chemistry models for a massively complex reaction system.

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Krstić, P. S., D. J. Dean, X. G. Zhang, D. Keffer, Y. S. Leng, P. T. Cummings, and J. C. Wells. "Computational chemistry for molecular electronics." Computational Materials Science 28, no. 2 (October 2003): 321–41. http://dx.doi.org/10.1016/s0927-0256(03)00116-2.

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17

Xu, Ziyang, Lijuan Gao, Pengyu Chen, and Li-Tang Yan. "Diffusive transport of nanoscale objects through cell membranes: a computational perspective." Soft Matter 16, no. 16 (2020): 3869–81. http://dx.doi.org/10.1039/c9sm02338k.

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Clarifying the diffusion dynamics of nanoscale objects with cell membrane is critical for revealing fundamental physics in biological systems. This perspective highlights the advances in computational and theoretical aspects of this emerging field.

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18

Varela, Luis González, Cesar Álvarez Bermúdez, Sergio Chapela, Jacobo Porteiro, and José L. Míguez Tabarés. "Improving Bed Movement Physics in Biomass Computational Fluid Dynamics Combustion Simulations." Chemical Engineering & Technology 42, no. 12 (November 4, 2019): 2556–64. http://dx.doi.org/10.1002/ceat.201800674.

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19

TAO, JIANMIN, JOHN P. PERDEW, and ADRIENN RUZSINSZKY. "LONG-RANGE VAN DER WAALS INTERACTION." International Journal of Modern Physics B 27, no. 18 (July 10, 2013): 1330011. http://dx.doi.org/10.1142/s0217979213300119.

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Van der Waals interaction is an elusive many-body effect arising from instantaneous charge fluctuations. Fundamental understanding of this effect plays an important role in computational chemistry, physics and materials science. In this article, recent advances in the evaluation of van der Waals coefficients, in particular the higher-order ones, are reviewed.

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20

Economou, E. N. "Activities, issues and perspectives in computational physics: a view from Greece." Computational Materials Science 2, no. 1 (January 1994): 131–36. http://dx.doi.org/10.1016/0927-0256(94)90055-8.

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21

Feng, Jinchao, Joshua L. Lansford, Markos A. Katsoulakis, and Dionisios G. Vlachos. "Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences." Science Advances 6, no. 42 (October 2020): eabc3204. http://dx.doi.org/10.1126/sciadv.abc3204.

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Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.

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22

Illas, Francesc. "Ab INITIO COMPUTATIONAL MODELS IN MATERIALS SCIENCE: A COMMON PLAYGROUND FOR SURFACE CHEMISTRY AND SOLID-STATE PHYSICS." Chemical Engineering Communications 195, no. 11 (July 14, 2008): 1465–76. http://dx.doi.org/10.1080/00986440801967338.

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23

Giovannini, Tommaso, Franco Egidi, and Chiara Cappelli. "Molecular spectroscopy of aqueous solutions: a theoretical perspective." Chemical Society Reviews 49, no. 16 (2020): 5664–77. http://dx.doi.org/10.1039/c9cs00464e.

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We review a mixed quantum-classical theoretical model and computational technique designed to accurately reproduce spectral signals of aqueous systems and provide a rationalization for the underlying physics.

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24

de Borst, René. "Challenges in computational materials science: Multiple scales, multi-physics and evolving discontinuities." Computational Materials Science 43, no. 1 (July 2008): 1–15. http://dx.doi.org/10.1016/j.commatsci.2007.07.022.

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25

Chen, Xing-Qiu. "Boosting the discovery of 3D topological materials: mixing chemistry with physics via a two-step computational screening strategy." National Science Review 5, no. 3 (May 12, 2017): 316–18. http://dx.doi.org/10.1093/nsr/nwx053.

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26

Doi, Masao. "Challenge in polymer physics." Pure and Applied Chemistry 75, no. 10 (January 1, 2003): 1395–402. http://dx.doi.org/10.1351/pac200375101395.

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One of the challenges in polymer physics is to predict the structure and properties of polymeric materials from their microstructures and processing conditions by integrating various theories and simulation methods. We have been involved in such a challenge through a governmental project involving computational modeling of polymeric materials. This article discusses how we tackled the challenge and describes the system we developed.

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27

Steinhauser, Martin, and Stefan Hiermaier. "A Review of Computational Methods in Materials Science: Examples from Shock-Wave and Polymer Physics." International Journal of Molecular Sciences 10, no. 12 (December 1, 2009): 5135–216. http://dx.doi.org/10.3390/ijms10125135.

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28

KADAU, KAI, TIMOTHY C. GERMANN, and PETER S. LOMDAHL. "MOLECULAR DYNAMICS COMES OF AGE: 320 BILLION ATOM SIMULATION ON BlueGene/L." International Journal of Modern Physics C 17, no. 12 (December 2006): 1755–61. http://dx.doi.org/10.1142/s0129183106010182.

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As computational power is increasing, molecular dynamics simulations are becoming more important in materials science, chemistry, physics, and other fields of science. We demonstrate weak and strong scaling of our classical molecular dynamics code SPaSM on Livermore's BlueGene/L architecture containing 131 072 IBM PowerPC440 processors. A maximum of 320 billion atoms have been simulated in double precision, corresponding to a cubic piece of solid copper with an edge length of 1.56 μm.

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Hasnip, Philip J., Keith Refson, Matt I. J. Probert, Jonathan R. Yates, Stewart J. Clark, and Chris J. Pickard. "Density functional theory in the solid state." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2011 (March 13, 2014): 20130270. http://dx.doi.org/10.1098/rsta.2013.0270.

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Density functional theory (DFT) has been used in many fields of the physical sciences, but none so successfully as in the solid state. From its origins in condensed matter physics, it has expanded into materials science, high-pressure physics and mineralogy, solid-state chemistry and more, powering entire computational subdisciplines. Modern DFT simulation codes can calculate a vast range of structural, chemical, optical, spectroscopic, elastic, vibrational and thermodynamic phenomena. The ability to predict structure–property relationships has revolutionized experimental fields, such as vibrational and solid-state NMR spectroscopy, where it is the primary method to analyse and interpret experimental spectra. In semiconductor physics, great progress has been made in the electronic structure of bulk and defect states despite the severe challenges presented by the description of excited states. Studies are no longer restricted to known crystallographic structures. DFT is increasingly used as an exploratory tool for materials discovery and computational experiments, culminating in ex nihilo crystal structure prediction, which addresses the long-standing difficult problem of how to predict crystal structure polymorphs from nothing but a specified chemical composition. We present an overview of the capabilities of solid-state DFT simulations in all of these topics, illustrated with recent examples using the CASTEP computer program.

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Jarrahbashi, Dorrin, Sayop Kim, Benjamin W. Knox, and Caroline L. Genzale. "Computational analysis of end-of-injection transients and combustion recession." International Journal of Engine Research 18, no. 10 (April 5, 2017): 1088–110. http://dx.doi.org/10.1177/1468087417701280.

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Mixing and combustion of engine combustion network Spray A after end of injection are modeled using highly resolved multidimensional numerical simulations to explore the physics underlying recent experimental observations of combustion recession. Reacting spray simulations are performed using a traditional Lagrangian–Eulerian coupled formulation for two-phase mixture transport with a Reynolds-averaged Navier–Stokes approach using the open-source computational fluid dynamics code OpenFOAM. Chemical kinetics models for n-dodecane by Cai et al. and Yao et al. are deployed to evaluate the impact of mechanism formulation and low-temperature chemistry on predictions of combustion recession behavior. Simulations with the Cai mechanism show that under standard Spray A conditions, the end-of-injection transient induces second-stage ignition in distinct regions near the nozzle that are initially spatially separated from the lifted diffusion flame, but then rapidly merge with flame. By contrast, the Yao mechanism fails to predict sufficient low-temperature chemistry in mixtures upstream of the diffusion flame during the end-of-injection transient and does not predict combustion recession for the same conditions. The effects of the shape and duration of the end-of-injection transient on the entrainment wave near the nozzle, the likelihood of combustion recession, and the spatiotemporal development of mixing and chemistry in near-nozzle mixtures are also investigated. With a more rapid ramp-down injection profile (ramp-down duration < 400 µs), a weaker combustion recession occurs earlier in time after the start of ramp-down. For extremely fast ramp-down (ramp-down duration = 0), the entrainment flux varies rapidly near the nozzle and over-leaning of the mixture completely suppresses combustion recession. For a slower ramp-down profile with respect to the standard Spray A condition, complete combustion recession back toward the nozzle is observed and combustion recession occurred later in time. Simulations qualitatively agreed with the past experimental and modeling observations of combustion recession with different end-of-injection transients.

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Kasabov, Nikola, and Lubica Benuskova. "Computational Neurogenetics." Journal of Computational and Theoretical Nanoscience 1, no. 1 (March 1, 2004): 47–61. http://dx.doi.org/10.1166/jctn.2004.006.

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32

De Lucia, Marco, and Michael Kühn. "DecTree v1.0 – chemistry speedup in reactive transport simulations: purely data-driven and physics-based surrogates." Geoscientific Model Development 14, no. 7 (July 29, 2021): 4713–30. http://dx.doi.org/10.5194/gmd-14-4713-2021.

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Abstract. The computational costs associated with coupled reactive transport simulations are mostly due to the chemical subsystem: replacing it with a pre-trained statistical surrogate is a promising strategy to achieve decisive speedups at the price of small accuracy losses and thus to extend the scale of problems which can be handled. We introduce a hierarchical coupling scheme in which “full-physics” equation-based geochemical simulations are partially replaced by surrogates. Errors in mass balance resulting from multivariate surrogate predictions effectively assess the accuracy of multivariate regressions at runtime: inaccurate surrogate predictions are rejected and the more expensive equation-based simulations are run instead. Gradient boosting regressors such as XGBoost, not requiring data standardization and being able to handle Tweedie distributions, proved to be a suitable emulator. Finally, we devise a surrogate approach based on geochemical knowledge, which overcomes the issue of robustness when encountering previously unseen data and which can serve as a basis for further development of hybrid physics–AI modelling.

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Marino, A., M. Peltomäki, J. Lim, and A. Aerts. "A multi-physics computational tool based on CFD and GEM chemical equilibrium solver for modeling coolant chemistry in nuclear reactors." Progress in Nuclear Energy 120 (February 2020): 103190. http://dx.doi.org/10.1016/j.pnucene.2019.103190.

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34

Abdel-Mottaleb, M. S. A., Mohamed M. S. Abdel-Mottaleb, Hoda S. Hafez, and Mona Saif. "J-Aggregates of Amphiphilic Cyanine Dyes for Dye-Sensitized Solar Cells: A Combination between Computational Chemistry and Experimental Device Physics." International Journal of Photoenergy 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/579476.

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We report on the design and structure principles of 5,5′-6,6′-tetrachloro-1,1′-dioctyl-3,3′-bis-(3-carboxypropyl)-benzimidacarbocyanine (Dye 1). Such metal-free amphiphilic cyanine dyes have many applications in dye-sensitized solar cells. AFM surface topographic investigation of amphiphilic molecules of Dye 1 adsorbed on TiO2anode reveals the ability of spontaneous self-organization into highly ordered aggregates of fiber-like structure. These aggregates are known to exhibit outstanding optical properties of J-aggregates, namely, efficient exciton coupling and fast exciton energy migration, which are essential for building up artificial light harvesting to the photovoltaic device. A light-to-electricity conversion efficiency of DSSC based on the metal free amphiphilic Dye 1 isη=3.75, which is about 50% of that based on metal-based N719 Ru-dye (Di-tetrabutylammoniumcis-bis(isothiocyanato)bis(2,2′-bipyridyl-4,4′-dicarboxylato)ruthenium(II)). DFT and TD-DFT studies show that large intramolecular charge transfer takes place from the HOMO to LUMO. HOMO is localized on a part of the molecule with almost no contribution from the carboxylic moiety. This clearly indicates that the anchoring carboxylic group plays a minor role.

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van der Kamp, Marc W., Katherine E. Shaw, Christopher J. Woods, and Adrian J. Mulholland. "Biomolecular simulation and modelling: status, progress and prospects." Journal of The Royal Society Interface 5, suppl_3 (July 8, 2008): 173–90. http://dx.doi.org/10.1098/rsif.2008.0105.focus.

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Molecular simulation is increasingly demonstrating its practical value in the investigation of biological systems. Computational modelling of biomolecular systems is an exciting and rapidly developing area, which is expanding significantly in scope. A range of simulation methods has been developed that can be applied to study a wide variety of problems in structural biology and at the interfaces between physics, chemistry and biology. Here, we give an overview of methods and some recent developments in atomistic biomolecular simulation. Some recent applications and theoretical developments are highlighted.

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Mazurov, Mikhail. "Nonlinear Concave Spiral Waves in Active Media Transferring Energy." EPJ Web of Conferences 224 (2019): 02011. http://dx.doi.org/10.1051/epjconf/201922402011.

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Spiral concave autowaves are widely implemented in physics, chemistry, hydrodynamics, meteorology and other fields. A mathematical model of spiral concave autowaves based on the Fitzhugh-Nagumo equation and modified axiomatic models are presented. The existence of spiral concave autowaves transferring energy was predicted via computational experiments. Applications of spiral concave autowaves carrying energy in hydrodynamics, generation of tornadoes, breaking waves, and tsunamis and examples of such autowaves in biology and medicine are reviewed and the importance of concave spiral autowaves transferring energy is emphasized.

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Hergart, C., and N. Peters. "Applying the Representative Interactive Flamelet Model to Evaluate the Potential Effect of Wall Heat Transfer on Soot Emissions in a Small-Bore Direct-Injection Diesel Engine." Journal of Engineering for Gas Turbines and Power 124, no. 4 (September 24, 2002): 1042–52. http://dx.doi.org/10.1115/1.1473147.

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Capturing the physics related to the processes occurring in the two-phase flow of a direct-injection diesel engine requires a highly sophisticated modeling approach. The representative interactive flamelet (RIF) model has gained widespread attention owing to its ability of correctly describing ignition, combustion, and pollutant formation phenomena. This is achieved by incorporating very detailed chemistry for the gas phase as well as for the soot particle growth and oxidation, without imposing any significant computational penalty. This study addresses the part load soot underprediction of the model, which has been observed in previous investigations. By assigning flamelets, which are exposed to the walls of the combustion chamber, with heat losses calculated in a computational fluid dynamics (CFD) code, predictions of the soot emissions in a small-bore direct-injection diesel engine are substationally improved. It is concluded that the experimentally observed emissions of soot may have their origin in flame quenching at the relatively cold combustion chamber walls.

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Di Staso, G., H. J. H. Clercx, S. Succi, and F. Toschi. "Lattice Boltzmann accelerated direct simulation Monte Carlo for dilute gas flow simulations." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2080 (November 13, 2016): 20160226. http://dx.doi.org/10.1098/rsta.2016.0226.

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Hybrid particle–continuum computational frameworks permit the simulation of gas flows by locally adjusting the resolution to the degree of non-equilibrium displayed by the flow in different regions of space and time. In this work, we present a new scheme that couples the direct simulation Monte Carlo (DSMC) with the lattice Boltzmann (LB) method in the limit of isothermal flows. The former handles strong non-equilibrium effects, as they typically occur in the vicinity of solid boundaries, whereas the latter is in charge of the bulk flow, where non-equilibrium can be dealt with perturbatively, i.e. according to Navier–Stokes hydrodynamics. The proposed concurrent multiscale method is applied to the dilute gas Couette flow, showing major computational gains when compared with the full DSMC scenarios. In addition, it is shown that the coupling with LB in the bulk flow can speed up the DSMC treatment of the Knudsen layer with respect to the full DSMC case. In other words, LB acts as a DSMC accelerator. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.

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Flick, Johannes, Nicholas Rivera, and Prineha Narang. "Strong light-matter coupling in quantum chemistry and quantum photonics." Nanophotonics 7, no. 9 (September 8, 2018): 1479–501. http://dx.doi.org/10.1515/nanoph-2018-0067.

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AbstractIn this article, we review strong light-matter coupling at the interface of materials science, quantum chemistry, and quantum photonics. The control of light and heat at thermodynamic limits enables exciting new opportunities for the rapidly converging fields of polaritonic chemistry and quantum optics at the atomic scale from a theoretical and computational perspective. Our review follows remarkable experimental demonstrations that now routinely achieve the strong coupling limit of light and matter. In polaritonic chemistry, many molecules couple collectively to a single-photon mode, whereas, in the field of nanoplasmonics, strong coupling can be achieved at the single-molecule limit. Theoretical approaches to address these experiments, however, are more recent and come from a spectrum of fields merging new developments in quantum chemistry and quantum electrodynamics alike. We review these latest developments and highlight the common features between these two different limits, maintaining a focus on the theoretical tools used to analyze these two classes of systems. Finally, we present a new perspective on the need for and steps toward merging, formally and computationally, two of the most prominent and Nobel Prize-winning theories in physics and chemistry: quantum electrodynamics and electronic structure (density functional) theory. We present a case for how a fully quantum description of light and matter that treats electrons, photons, and phonons on the same quantized footing will unravel new quantum effects in cavity-controlled chemical dynamics, optomechanics, nanophotonics, and the many other fields that use electrons, photons, and phonons.

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Vera, Julio, Christopher Lischer, Momchil Nenov, Svetoslav Nikolov, Xin Lai, and Martin Eberhardt. "Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic." International Journal of Molecular Sciences 22, no. 2 (January 7, 2021): 547. http://dx.doi.org/10.3390/ijms22020547.

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In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today’s level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.

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41

Vera, Julio, Christopher Lischer, Momchil Nenov, Svetoslav Nikolov, Xin Lai, and Martin Eberhardt. "Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic." International Journal of Molecular Sciences 22, no. 2 (January 7, 2021): 547. http://dx.doi.org/10.3390/ijms22020547.

Full text

Abstract:

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today’s level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment.

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Zanotti, Alex, Alberto Savino, Michele Palazzi, Matteo Tugnoli, and Vincenzo Muscarello. "Assessment of a Mid-Fidelity Numerical Approach for the Investigation of Tiltrotor Aerodynamics." Applied Sciences 11, no. 8 (April 9, 2021): 3385. http://dx.doi.org/10.3390/app11083385.

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The study of the complex aerodynamics that characterise tiltrotors represents a challenge for computational fluid dynamics tools. URANS numerical solvers are typically used to explore the aerodynamic features that characterise the different flight conditions of these aircraft, but their computational cost limits their applications to a few vehicle configurations. The present work explores the capabilities of a new mid-fidelity aerodynamic code that is based on the vortex particle method, DUST, to investigate the performance and flow physics of tiltrotors. With this aim, numerical simulations were performed in DUST while considering XV-15 tiltrotor configurations with increasing complexity. The study started with the investigation of a simpler configuration made up of a single wing and a proprotor. Subsequently, the full aircraft was studied in steady-level flights and its major operating flight conditions were explored—i.e., hover, conversion phase, and cruise. A thorough assessment of the code capabilities was performed by the comparison of the numerical results with high-fidelity Computational Fluid Dynamics (CFD) data. This thorough comparison showed that the mid-fidelity numerical approach implemented in DUST is suitable for capturing the flow physics related to the complex aerodynamic interactions between the proprotors and the wing along with the entire flight envelope of the tiltrotor. Moreover, a good representation of the aerodynamic performance of the vehicle was obtained, particularly for the flight conditions that are characterised by limited flow separations. The good accuracy obtained for both the performance and flow physics, combined with the relatively lower computational costs required by the mid-fidelity solver with respect to the URANS simulations, indicates that DUST could be considered a valuable tool for use in the preliminary design of novel tiltrotor configurations.

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Wang, Ying, Pragya Verma, Lujia Zhang, Yaqi Li, Zhonghua Liu, Donald G. Truhlar, and Xiao He. "M06-SX screened-exchange density functional for chemistry and solid-state physics." Proceedings of the National Academy of Sciences 117, no. 5 (January 17, 2020): 2294–301. http://dx.doi.org/10.1073/pnas.1913699117.

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Screened-exchange hybrid density functionals are especially recommended for solid-state systems because they combine the advantages of hybrid functionals with the correct physics and lower computational cost associated with the attenuation of Hartree–Fock exchange at long range. We present a screened-exchange hybrid functional, M06-SX, that combines the functional form of the local revM06-L functional with a percentage of short-range nonlocal Hartree–Fock exchange. The M06-SX functional gives good results not only for a large set of training data but also for several databases quite different from the training data. The mean unsigned error (MUE) of the M06-SX functional is 2.85 kcal/mol for 418 atomic and molecular energies (AME418) in Minnesota Database 2019, which is better than all five other screened-exchange hybrid functionals tested in this work. The M06-SX functional also gives especially good results for semiconductor band gaps, molecular dissociation energies, noncovalent interactions, barrier heights, and electronic excitation energies excluding long-range charge transfer excitations. For the LC18 lattice constants database, the M06-SX functional gives an MUE of only 0.034 Å. Therefore, the M06-SX functional is well suited for studying molecular chemistry as well as solid-state physics.

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Axenie, Cristian, Roman Bauer, and María Rodríguez Martínez. "The Multiple Dimensions of Networks in Cancer: A Perspective." Symmetry 13, no. 9 (August 25, 2021): 1559. http://dx.doi.org/10.3390/sym13091559.

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This perspective article gathers the latest developments in mathematical and computational oncology tools that exploit network approaches for the mathematical modelling, analysis, and simulation of cancer development and therapy design. It instigates the community to explore new paths and synergies under the umbrella of the Special Issue “Networks in Cancer: From Symmetry Breaking to Targeted Therapy”. The focus of the perspective is to demonstrate how networks can model the physics, analyse the interactions, and predict the evolution of the multiple processes behind tumour-host encounters across multiple scales. From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests approaches that mark a viable path towards adoption in the clinic.

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Chen, Guanhua. "A Special Section on Theoretical and Computational Chemistry of Complex Systems." Journal of Computational and Theoretical Nanoscience 3, no. 5 (October 1, 2006): i. http://dx.doi.org/10.1166/jctn.2006.021.

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Longaretti, Massimo, Giovambattista Marino, Bice Chini, Joseph W. Jerome, and Riccardo Sacco. "Computational Models in Nano-Bioelectronics: Simulation of Ionic Transport in Voltage Operated Channels." Journal of Nanoscience and Nanotechnology 8, no. 7 (July 1, 2008): 3686–94. http://dx.doi.org/10.1166/jnn.2008.18334.

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In this article, a novel mathematical and computational model is proposed for the numerical simulation of Voltage Operated ionic Channels (VOC) in Nano-bioelectronics applications. This is a first step towards a multi-physics description of hybrid bio-electronical devices such as bio-chips. The model consists of a coupled system of nonlinear partial differential equations, comprising a Poisson-Nernst-Planck system to account for electro-chemical phenomena, and a Navier-Stokes system to account for fluid-mechanical phenomena. Suitable functional iteration techniques for problem decoupling and finite element methods for discretization are proposed and discussed. Numerical results on realistic VOCs illustrate the validity of the model and its accuracy by comparison with relevant computed channel equivalent electrical parameters with measured data.

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Varandas, A. J. C. "Extrapolation in quantum chemistry: Insights on energetics and reaction dynamics." Journal of Theoretical and Computational Chemistry 19, no. 07 (September 2, 2020): 2030001. http://dx.doi.org/10.1142/s0219633620300013.

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Since there is no exact solution for problems in physics and chemistry, extrapolation methods may assume a key role in quantitative quantum chemistry. Two topics where it bears considerable impact are addressed, both at the heart of computational quantum chemistry: electronic structure and reaction dynamics. In the first, the problem of extrapolating the energy obtained by solving the electronic Schrödinger equation to the limit of the complete one-electron basis set is addressed. With the uniform-singlet-and-triplet-extrapolation (USTE) scheme at the focal point, the emphasis is on recent updates covering from the energy itself to other molecular properties. The second topic refers to extrapolation of quantum mechanical reactive scattering probabilities from zero total angular momentum to any of the values that it may assume when running quasiclassical trajectories, QCT/QM-[Formula: see text]J. With the extrapolation guided in both cases by physically motivated asymptotic theories, realism is seeked by avoiding unsecure jumps into the unknown. Although, mostly review oriented, a few issues are addressed for the first time here and there. Prospects for future work conclude the overview.

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Tegnér, Jesper, Hector Zenil, Narsis A. Kiani, Gordon Ball, and David Gomez-Cabrero. "A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, no. 2080 (November 13, 2016): 20160144. http://dx.doi.org/10.1098/rsta.2016.0144.

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Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.

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Sparta, Manuel, Damiano Varagnolo, Kristian Stråbø, Svenn Anton Halvorsen, Egil Vålandsmyr Herland, and Harald Martens. "Metamodeling of the Electrical Conditions in Submerged Arc Furnaces." Metallurgical and Materials Transactions B 52, no. 3 (March 2, 2021): 1267–78. http://dx.doi.org/10.1007/s11663-021-02089-7.

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AbstractPhysics-based Finite Element Methods models can be used to investigate the electrical conditions in submerged arc furnaces (SAFs). However, their explicit solution may be very demanding in terms of time and computational resources. This makes these models difficult to employ during control operations and in fast prototyping. To obviate these inconveniences, we developed metamodels that are grounded on the physics-based model. In this context, a metamodel is a surrogate of an original model obtained using statistical analysis tools to determine approximate input–output relationships in a database of simulations from the original model. The metamodels for the SAF electrical conditions are shown to retain the same generalization capabilities as the original model while being computationally lightweight.

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Abdou, Mohamed A., and Essam M. Abulwafa. "Application of the Exp-Functionmethod to the Riccati Equation and New Exact Solutions with Three Arbitrary Functions of Quantum Zakharov Equations." Zeitschrift für Naturforschung A 63, no. 10-11 (November 1, 2008): 646–52. http://dx.doi.org/10.1515/zna-2008-10-1107.

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The Exp-function method with the aid of the symbolic computational system is used for constructing generalized solitary solutions of the generalized Riccati equation. Based on the Riccati equation and its generalized solitary solutions, new exact solutions with three arbitrary functions of quantum Zakharov equations are obtained. It is shown that the Exp-function method provides a straightforward and important mathematical tool for nonlinear evolution equations in mathematical physics.

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