Littérature scientifique sur le sujet « Ab initio prediction »
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Articles de revues sur le sujet "Ab initio prediction"
Hardin, Corey, Taras V. Pogorelov et Zaida Luthey-Schulten. « Ab initio protein structure prediction ». Current Opinion in Structural Biology 12, no 2 (avril 2002) : 176–81. http://dx.doi.org/10.1016/s0959-440x(02)00306-8.
Texte intégralDerreumaux, Philippe. « Ab initio polypeptide structure prediction ». Theoretical Chemistry Accounts : Theory, Computation, and Modeling (Theoretica Chimica Acta) 104, no 1 (12 mai 2000) : 1–6. http://dx.doi.org/10.1007/s002149900095.
Texte intégralLeusen, Frank J. J. « Ab initio prediction of polymorphs ». Journal of Crystal Growth 166, no 1-4 (septembre 1996) : 900–903. http://dx.doi.org/10.1016/0022-0248(96)00099-1.
Texte intégralPopelier, Paul. « pKa prediction from ab initio calculations ». Research Outreach, no 109 (30 août 2019) : 90–93. http://dx.doi.org/10.32907/ro-109-9093.
Texte intégralvan Eijck, B. P. « Ab Initio Prediction of Crystal Structures ». Acta Crystallographica Section A Foundations of Crystallography 56, s1 (25 août 2000) : s3. http://dx.doi.org/10.1107/s0108767300021188.
Texte intégralShang, Bo, Lan-Feng Yuan, Xiao Cheng Zeng et Jinlong Yang. « Ab Initio Prediction of Amorphous B84 ». Journal of Physical Chemistry A 114, no 6 (18 février 2010) : 2245–49. http://dx.doi.org/10.1021/jp907976y.
Texte intégralWeinreich, Jan, Dominik Lemm, Guido Falk von Rudorff et O. Anatole von Lilienfeld. « Ab initio machine learning of phase space averages ». Journal of Chemical Physics 157, no 2 (14 juillet 2022) : 024303. http://dx.doi.org/10.1063/5.0095674.
Texte intégralNishimura, T., S. Nakamura et K. Shimizu. « Application of ab initio prediction of protein structures ». Seibutsu Butsuri 43, supplement (2003) : S33. http://dx.doi.org/10.2142/biophys.43.s33_5.
Texte intégralGdanitz, Robert J. « Ab initio prediction of molecular crystal structures ». Current Opinion in Solid State and Materials Science 3, no 4 (août 1998) : 414–18. http://dx.doi.org/10.1016/s1359-0286(98)80054-5.
Texte intégralStanke, M., O. Keller, I. Gunduz, A. Hayes, S. Waack et B. Morgenstern. « AUGUSTUS : ab initio prediction of alternative transcripts ». Nucleic Acids Research 34, Web Server (1 juillet 2006) : W435—W439. http://dx.doi.org/10.1093/nar/gkl200.
Texte intégralThèses sur le sujet "Ab initio prediction"
Thomas, Geraint Llewllyn. « Ab initio protein fold prediction ». Thesis, University of Leeds, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436019.
Texte intégralMeyer, Irmtraud Margret. « Mathematical methods for comparative Ab initio gene prediction ». Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619669.
Texte intégralDjurdjević, Dušan. « Ab initio protein fold prediction using evolutionary algorithms ». Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/13660.
Texte intégralWang, Guisheng. « Ab initio prediction of the mechanical properties of alloys ». Doctoral thesis, KTH, Tillämpad materialfysik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169511.
Texte intégralQC 20150616
Kang, ShinYoung. « Ab initio prediction of thermodynamics in alkali metal-air batteries ». Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89952.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (pages 93-100).
Electric vehicles ("EVs") require high-energy-density batteries with reliable cyclability and rate capability. However, the current state-of-the-art Li-ion batteries only exhibit energy densities near ~150 Wh/kg, limiting the long-range driving of EVs with one charge and hindering their wide-scale commercial adoption.1-3 Recently, non-aqueous metal-O₂ batteries have drawn attention due to their high theoretical specific energy.2, 4-6 Specifically, the issues surrounding battery studies involve Li-O₂ and Na-O₂ batteries due to their high theoretical specific energies of 3.5 kWh/kg (assuming Li 20 2 as a discharge product in Li-O₂ batteries) and 1.6 and 1.1 kWh/kg (assuming Na₂O₂ and NaO₂ as discharge products, respectively, in Na-O₂ batteries). Since the potential of Li-O₂ batteries as an energy storage system was first proposed in 1996,1 various studies have criticized and verified their shortcomings, such as their low power density, poor cyclability, and poor rate capability. ₇, ₈ Substantial research attempts have been made to identify the cause of the high overpotentials and electrolyte decomposition and to search for better cathode/electrolyte/anode and/or catalyst material combinations. However, Li-O₂ battery technology remains in its infancy primarily due to the lack of understanding of the underlying mechanisms. Therefore, we investigate the charging mechanism, which contributes to the considerable energy loss using first-principles calculations and propose a new charging mechanism based on experimental observations and knowledge concerning Li-ion and Na-ion batteries. Most studies on metal-O₂ batteries have mainly focused on Li-O₂ batteries. However, recently, the promising performance of Na-O₂ systems has been reported.₉, ₁₀ Although Na-O₂ batteries exhibit slightly lower theoretical specific energies than those of the Li-O₂ batteries as specified above, the chemical difference between the two alkali metals substantially distinguishes the electrochemistry properties of Na-O₂ and Li-O₂. In the Na-O₂ system, both NaO₂ and Na₂O₂ are stable compounds, while in the Li-O system, LiO₂ is not a stable compound under standard state conditions (300 K and 1 atm).₁₁, ₁₂ Presumably, due to this chemical difference, the Na-O₂ system has exhibited a much smaller charging overpotential, as low as 0.2 V, when NaO₂ is formed as a discharge product, compared with that in Li-O₂ system, >1 V. Such a low charging overpotential in Na-O₂ batteries demonstrates their potential as a next generation electrochemical system for commercially viable EVs .₉,₁₀ In this thesis, we study the thermodynamic stability of Na-O compounds to identify the phase selection conditions that affect the performance of Na-O₂ batteries.
by ShinYoung Kang.
Ph. D.
Mijajlovic, Milan. « Ab initio prediction of the conformation of solvated and adsorbed proteins ». Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3173.
Texte intégralDePristo, Mark Andrew. « Ab initio conformational sampling for protein structure determination, analysis, and prediction ». Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615942.
Texte intégralSimons, Kim T. « Deciphering the protein folding code : ab initio prediction of protein structure / ». Thesis, Connect to this title online ; UW restricted, 1998. http://hdl.handle.net/1773/9234.
Texte intégralShi, Jingming. « Ab initio prediction of crystalline phases and their electronic properties : from ambient to extreme pressures ». Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1110/document.
Texte intégralIn this thesis we use global structural prediction methods (Particle Swarm Optimization and Minima Hopping Method) and high-throughput techniques to predict crystal structures of different systems under different conditions. We performed structural prediction by using the Crystal structure Analysis by Particle Swarm Optimization (CALYPSO) combined with Density Functional Theory (DFT) that made possible to unveil several stable compounds, so far unknown, on the phase diagrams of Ba-Si systerm and N-H-O system. Afterwards, we performed a high-throughput investigation on ternary compounds of composition ABX2, where A and B are elements of the periodic table up to Bi, and X is a chalcogen (O, S, Se, and Te) by using density functional theory and combining calculations of crystal prototypes with structural prediction (Minima Hopping Method). The following paragraphs summarize the content by chapter of this document. Chapter 1 is a short introduction of this thesis. Chapter 2 consists of the basic theory used in this thesis. Firstly, a short introduction of Density Function Theory (DFT) is presented. Then, we describe some approximate exchange- correlation functions that make DFT practical. Next, we introduce different structural prediction algorithms, especially Particle Swarm Optimization and Minima Hopping Method which we used in this thesis. Finally, we discuss the thermodynamic stablility criteria for a new a new structure. In Chapter 3, we first consider Ba–Si system. Using an unbiased structural search based on a particle-swarm optimization algorithm combined with DFT calculations, we investigate systematically the ground-state phase stability and structural diversity of Ba–Si binaries under high pressure. The phase diagram turns out to be quite intricate, with several compositions stabilizing/destabilizing as a function of pressure. In particular, we identify novel phases of BaSi, BaSi2, BaSi3, and BaSi5 that might be synthesizable experimentally over a wide range of pressures. Chapter 4 contains the investigation of the phases diagram of the N–H–O system. By using ab initio evolutionary structural search, we report the prediction of two novel phases of the N–H–O ternary system, namely NOH4 and HNO3 (nitric acid) at pressure up to 150 GPa. Our calculations show that the new C2/m phase of NOH4 is stable under a large range of pressure from 71 GPa to 150 GPa while the P21/m phase of HNO3 (nitric acid) is stable from 39 GPa to 150 GPa (the maximum pressure which we have studied). We also confirmed that the composition NOH5 (NH3H2O) becomes unstable for pressures above 122 GPa. It decomposes into NH3 and H2O at this pressure. Chapter 5 focuses on p-type transparent electrodes of ternary chalcogenides. We use a high-throughput approach based on DFT to find delafossite and related layered phases of composition ABX2, where A and B are elements of the periodic table, and X is a chalcogen (O, S, Se, and Te). From the 15 624 compounds studied in the trigonal delafossite prototype structure, 285 are within 50 meV/atom from the convex hull of stability. These compounds are further investigated using global structural prediction methods to obtain their lowest- energy crystal structure. We find 79 systems not present in the "Materials project database" that are thermodynamically stable and crystallize in the delafossite or in closely related structures. These novel phases are then characterized by calculating their band gaps and hole effective masses. This characterization unveils a large diversity of properties, ranging from normal metals, magnetic metals, and some candidate compounds for p-type transparent electrodes. At the end of the thesis, we give our general conclusion and an outlook
McLean, Malcolm Arthur. « Potential energy functions and search routines for ab initio protein structure prediction ». Thesis, University of Leeds, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522963.
Texte intégralLivres sur le sujet "Ab initio prediction"
Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.
Texte intégralRaff, Lionel, Ranga Komanduri, Martin Hagan et Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.
Texte intégralNeupane, Raddha, et Tara Prasad. Quantum ESPRESSO - Easy Way to Use It for Research Project and PhD : Quantum ESPRESSO - a Tool for Ab-Initio and Density Functional Theory Based Computational Approximation and Predictions. Independently Published, 2021.
Trouver le texte intégralChapitres de livres sur le sujet "Ab initio prediction"
Lee, Jooyoung, Peter L. Freddolino et Yang Zhang. « Ab Initio Protein Structure Prediction ». Dans From Protein Structure to Function with Bioinformatics, 3–35. Dordrecht : Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1069-3_1.
Texte intégralMishra, Akhilesh, Priyanka Siwach, Poonam Singhal et B. Jayaram. « ChemGenome2.1 : An Ab Initio Gene Prediction Software ». Dans Methods in Molecular Biology, 121–38. New York, NY : Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9173-0_7.
Texte intégralAbbass, Jad, Jean-Christophe Nebel et Nashat Mansour. « Ab Initio Protein Structure Prediction : Methods and challenges ». Dans Biological Knowledge Discovery Handbook, 703–24. Hoboken, New Jersey : John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch32.
Texte intégralLiu, L. Angela, et Joel S. Bader. « Structure-Based Ab Initio Prediction of Transcription Factor–Binding Sites ». Dans Methods in Molecular Biology, 23–41. Totowa, NJ : Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-243-4_2.
Texte intégralHoque, Md T., M. Chetty et L. S. Dooley. « Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction ». Dans Hybrid Evolutionary Algorithms, 241–68. Berlin, Heidelberg : Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_10.
Texte intégralCeci, G., A. Mucherino, M. D’Apuzzo, D. Di Serafino, S. Costantini, A. Facchiano et G. Colonna. « Computational Methods for Protein Fold Prediction : an Ab-initio Topological Approach ». Dans Data Mining in Biomedicine, 391–429. Boston, MA : Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-69319-4_21.
Texte intégralZhang, Z., L. Lu, P. Wu et C. Shu. « Prediction of Defects in PZT Thin Film Using Ab-Initio Method ». Dans Frontiers in Materials Science and Technology, 53–56. Stafa : Trans Tech Publications Ltd., 2008. http://dx.doi.org/10.4028/0-87849-475-8.53.
Texte intégralEyrich, Volker A., Richard A. Friesner et Daron M. Standley. « Ab Initio Protein Structure Prediction Using a Size-dependent Tertiary Folding Potential ». Dans Computational Methods for Protein Folding, 223–63. New York, USA : John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471224421.ch6.
Texte intégralPantelides, Constantinos C., Claire S. Adjiman et Andrei V. Kazantsev. « General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules ». Dans Topics in Current Chemistry, 25–58. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/128_2013_497.
Texte intégralLipinski-Paes, Thiago, Michele dos Santos da Silva Tanus, José Fernando Ruggiero Bachega et Osmar Norberto de Souza. « A Multiagent Ab Initio Protein Structure Prediction Tool for Novices and Experts ». Dans Bioinformatics Research and Applications, 163–74. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38782-6_14.
Texte intégralActes de conférences sur le sujet "Ab initio prediction"
Haskins, Peter J. « Ab-Initio Prediction of Impact Sensitivity ». Dans SHOCK COMPRESSION OF CONDENSED MATTER - 2005 : Proceedings of the Conference of the American Physical Society Topical Group on Shock Compression of Condensed Matter. AIP, 2006. http://dx.doi.org/10.1063/1.2263376.
Texte intégralLesk, Arthur M. « Assessment of ab initio protein structure prediction ». Dans the second annual international conference. New York, New York, USA : ACM Press, 1998. http://dx.doi.org/10.1145/279069.279110.
Texte intégralSrinivasan, Rajgopal, et George D. Rose. « Protein structure prediction — An Ab initio approach ». Dans 2003 European Control Conference (ECC). IEEE, 2003. http://dx.doi.org/10.23919/ecc.2003.7086562.
Texte intégralLIU, L. ANGELA, et JOEL S. BADER. « AB INITIO PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES ». Dans Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772435_0046.
Texte intégral« Comparison of Four Ab Initio MicroRNA Prediction Tools ». Dans International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004248201900195.
Texte intégralZhou, Peng-Fang, Fei Zhang, Yang Zhang, Zhen-Hua Zhao, De-Li Zhang et Wen-Qian Zhang. « SusMiRPred : Ab Initio SVM Classification for Porcine MicroRNA Precursor Prediction ». Dans 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5516745.
Texte intégralHerndon, Nic, et Doina Caragea. « Ab initio Splice Site Prediction with Simple Domain Adaptation Classifiers ». Dans 7th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005710502450252.
Texte intégralHautier, Geoffroy. « Prediction of new battery materials based on ab initio computations ». Dans ELECTROCHEMICAL STORAGE MATERIALS : SUPPLY, PROCESSING, RECYCLING AND MODELLING : Proceedings of the 2nd International Freiberg Conference on Electrochemical Storage Materials. Author(s), 2016. http://dx.doi.org/10.1063/1.4961901.
Texte intégralBecerra, David, Angelica Sandoval, Daniel Restrepo-Montoya et F. Nino Luis. « A parallel multi-objective ab initio approach for protein structure prediction ». Dans 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706552.
Texte intégralFerreira, A. L. C. « Prediction of liquid C[sub 60] from ab initio intermolecular potential ». Dans Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386838.
Texte intégralRapports d'organisations sur le sujet "Ab initio prediction"
Gregurick, S. K. AB Initio Protein Tertiary Structure Prediction : Comparative-Genetic Algorithm with Graph Theoretical Methods. Office of Scientific and Technical Information (OSTI), avril 2001. http://dx.doi.org/10.2172/834523.
Texte intégralYao, Yongxin. Thermodynamic prediction of glass formation tendency, cluster-in-jellium model for metallic glasses, ab initio tight-binding calculations, and new density functional theory development for systems with strong electron correlation. Office of Scientific and Technical Information (OSTI), janvier 2009. http://dx.doi.org/10.2172/972073.
Texte intégralThompson, Donald L. Ab Initio-Based Predictions of Hydrocarbon Combustion Chemistry. Fort Belvoir, VA : Defense Technical Information Center, juillet 2015. http://dx.doi.org/10.21236/ada624250.
Texte intégralAndersson, Anders, et Chao Jiang. Predicting thermodynamic and thermophysical properties of molten chloride salts from ab-initio and classical molecular dynamics simulations. Office of Scientific and Technical Information (OSTI), août 2021. http://dx.doi.org/10.2172/1813811.
Texte intégral