Relevant bibliographies by topics / Ab initio prediction

Academic literature on the topic 'Ab initio prediction'

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Published: 11 March 2023

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Journal articles on the topic "Ab initio prediction"

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Hardin, Corey, Taras V. Pogorelov, and Zaida Luthey-Schulten. "Ab initio protein structure prediction." Current Opinion in Structural Biology 12, no. 2 (April 2002): 176–81. http://dx.doi.org/10.1016/s0959-440x(02)00306-8.

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Derreumaux, Philippe. "Ab initio polypeptide structure prediction." Theoretical Chemistry Accounts: Theory, Computation, and Modeling (Theoretica Chimica Acta) 104, no. 1 (May 12, 2000): 1–6. http://dx.doi.org/10.1007/s002149900095.

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Leusen, Frank J. J. "Ab initio prediction of polymorphs." Journal of Crystal Growth 166, no. 1-4 (September 1996): 900–903. http://dx.doi.org/10.1016/0022-0248(96)00099-1.

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Popelier, Paul. "pKa prediction from ab initio calculations." Research Outreach, no. 109 (August 30, 2019): 90–93. http://dx.doi.org/10.32907/ro-109-9093.

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van Eijck, B. P. "Ab Initio Prediction of Crystal Structures." Acta Crystallographica Section A Foundations of Crystallography 56, s1 (August 25, 2000): s3. http://dx.doi.org/10.1107/s0108767300021188.

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Shang, Bo, Lan-Feng Yuan, Xiao Cheng Zeng, and Jinlong Yang. "Ab Initio Prediction of Amorphous B84." Journal of Physical Chemistry A 114, no. 6 (February 18, 2010): 2245–49. http://dx.doi.org/10.1021/jp907976y.

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Weinreich, Jan, Dominik Lemm, Guido Falk von Rudorff, and O. Anatole von Lilienfeld. "Ab initio machine learning of phase space averages." Journal of Chemical Physics 157, no. 2 (July 14, 2022): 024303. http://dx.doi.org/10.1063/5.0095674.

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Equilibrium structures determine material properties and biochemical functions. We here propose to machine learn phase space averages, conventionally obtained by ab initio or force-field-based molecular dynamics (MD) or Monte Carlo (MC) simulations. In analogy to ab initio MD, our ab initio machine learning (AIML) model does not require bond topologies and, therefore, enables a general machine learning pathway to obtain ensemble properties throughout the chemical compound space. We demonstrate AIML for predicting Boltzmann averaged structures after training on hundreds of MD trajectories. The AIML output is subsequently used to train machine learning models of free energies of solvation using experimental data and to reach competitive prediction errors (mean absolute error ∼ 0.8 kcal/mol) for out-of-sample molecules—within milliseconds. As such, AIML effectively bypasses the need for MD or MC-based phase space sampling, enabling exploration campaigns of Boltzmann averages throughout the chemical compound space at a much accelerated pace. We contextualize our findings by comparison to state-of-the-art methods resulting in a Pareto plot for the free energy of solvation predictions in terms of accuracy and time.
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Nishimura, T., S. Nakamura, and 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.

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Gdanitz, Robert J. "Ab initio prediction of molecular crystal structures." Current Opinion in Solid State and Materials Science 3, no. 4 (August 1998): 414–18. http://dx.doi.org/10.1016/s1359-0286(98)80054-5.

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Stanke, M., O. Keller, I. Gunduz, A. Hayes, S. Waack, and B. Morgenstern. "AUGUSTUS: ab initio prediction of alternative transcripts." Nucleic Acids Research 34, Web Server (July 1, 2006): W435—W439. http://dx.doi.org/10.1093/nar/gkl200.

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Dissertations / Theses on the topic "Ab initio prediction"

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Thomas, Geraint Llewllyn. "Ab initio protein fold prediction." Thesis, University of Leeds, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436019.

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Meyer, 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.

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Djurdjević, Dušan. "Ab initio protein fold prediction using evolutionary algorithms." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/13660.

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A comprehensive study was undertaken for ab initio protein fold prediction using a fully atomistic protein model and a physicochemical potential. Twenty four EA designs where initially assessed on polyalanine, a prototypical α-helical polypeptide.  Design aspects varied include the encoding alphabet, crossover operator, replacement strategy and selection strategy. By undertaking a comprehensive parameter study, the best performing designs and associated control parameter values were identified for polyalanine. The scaling between the performance and polyalinine size was also identified for these best designs. This initial study was followed by a similar parametric study for met-enkephalin, a five residue polypeptide that has long been used as a de facto standard test case for protein structure prediction algorithms. It was found that the control parameter scalings identified from the polyalinine study were transferable to this real protein, and that the EA is superior to all existing ab initio approaches for met-enkephalin. The best design was finally applied to a series of real proteins ranging in size up to 45 residues to more generally assess the EA’s performance. The thesis is concluded with consideration of the future work required to extend the EA to larger proteins and ab initio structure prediction for non-native environments such as at interfaces, which are of relevance to, for example, biosensors.
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Wang, 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.

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At the time of the 50th anniversary of the Kohn-Sham method, ab initio calculations based on density functional theory have formed an accurate, efficient, and reliable method to work on the properties of engineering materials. In this thesis, we use the exact muffin-tin orbitals method combined with the coherent-potential approximation to study the mechanical properties of high-technology materials. The thesis includes two parts: a study of long-range chemical order effects and a study of alloying effects on the mechanical properties of alloys. In the first part, we concentrate on the impact of chemical ordering on the mechanical properties. The long range order effect on the elastic constants behaves in a very different way for non-magnetic materials and ferromagnetic materials. For a non-magnetic Cu3Au, the long-range order effect on the elastic constants is very small. The Debye temperature does not show a strong chemical order dependence either. For a ferromagnetic material, on the other hand, the long-range chemical order produces considerable influence on C' in the ferromagnetic state, but negligible effect on C' in the paramagnetic state. The lattice parameter, bulk modulus $B$, and shear elastic constant C44 change slightly with the degree of long-rang order for both magnetic states. The Young's modulus E and the shear modulus G increase significantly with the degree of order in the ferromagnetic state, but the effect becomes weak as the system approaches the random regime.In the second part, the alloying effect of Mn/Ni on the lattice parameter, elastic constants, surface energy, and unstable stacking fault energy of bcc Fe is examined. The calculated results show that the lattice parameter of ferrite Fe is slightly altered upon Ni/Mn alloying the trend of which can be explained by the magnetism-induced pressure. Nickel addition decreases C' but has a negligible effect on C44, whereas manganese addition increases C44 and has a weak influence on C'. In both systems, the bulk modulus B shows a smooth second order variation. On the other hand, the surface energy and the unstable stacking fault (USF) energy decrease by adding Mn or Ni to Fe. For both planar fault energies, Ni shows a stronger effect than Mn. Segregation seems to have a minor effect on the surface and USF energies for dilute Fe-Ni and Fe-Mn alloys. The ductility can be estimated using available physical parameters via traditional phenomenological criteria like the Pugh ratio B/G, the Poisson ratio ν, the Cauchy pressure C12-C44, and the Rice ratio γs/γu .According to dislocation theory, a dislocation can not cross a grain boundary. Therefore, the study of dislocations is assumed to be limited to single-crystals. Several theoretical studies indicate that the cleavage plane is {001} in bcc crystals. Based on these information, we suggest that the resolved single-crystal tensile strength E[001] and the resolved single crystal shear strength G{110}<111> should be used to describe brittle cleavage and dislocation movement rather than polycrystalline parameters such as B and G. We demonstrate that all shear moduli G{lmn}<111> associated with the <111> Burgers vector take the same value 3C44C'/(C'+2C44), which could in fact explain the observed multiple slip in bcc systems. Due to the discrepancy between the resolved single-crystal elastic constants and the averaged polycrystalline elastic constants, the Pugh ratio B/G and the traditional criteria based on polycrystalline elastic constants lead to large differences for magnetic systems. Finally, we propose a new measure of the ductile-bittle behavior based on the ratio σclevage/Gresolved which gives the right experimental trend for Fe-Mn and Fe-Ni system.

QC 20150616

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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.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2014.
Cataloged 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.
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Mijajlovic, Milan. "Ab initio prediction of the conformation of solvated and adsorbed proteins." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3173.

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Proteins are among the most important groups of biomolecules, with their biological functions ranging from structural elements to signal transducers between cells. Apart from their biological role, phenomena related to protein behaviour in solutions and at solid interfaces can find a broad range of engineering applications such as in biomedical implants, scaffolds for artificial tissues, bioseparations, biomineralization and biosensors. For both biological and engineering applications, the functionality of a protein is directly related to its three-dimensional structure (i.e. conformation). Methods such as homology and threading that depend on a large database of existing experimental knowledge are the most popular means of predicting the conformation of proteins in their native environment. Lack of sufficient experimentally-derived information for non-native environments such as general solutions and solid interfaces prevents these knowledge-based methods being used for such environments. Resort must, instead, be made to so-called ab initio methods that rely upon knowledge of the primary sequence of the protein, its environment, and the physics of the interatomic interactions. The development of such methods for non-native environments is in its infancy – this thesis reports on the development of such a method and its application to proteins in water and at gas/solid and water/solid interfaces. After introducing the approach used – which is based on evolutionary algorithms (EAs) – we first report a study of polyalanine adsorbed at a gas/solid interface in which a switching behaviour is observed that, to our knowledge, has never been reported before. The next section reports work that shows the combination of the Langevin dipole (LD) solvent method with the Amber potential energy (PE) model is able to yield solvation energies comparable to those of more sophisticated methods at a fraction of the cost, and that the LD method is able to capture effects that arise from inhomogenities in the water structure such as H-bond bridges. The third section reports a study that shows that EA performance and optimal control parameters vary substantially with the PE model. The first three parts form the basis of the last part of the thesis, which reports pioneering work on predicting ab initio the conformation of proteins in solutions and at water/solid interfaces.
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DePristo, 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.

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Simons, 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.

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Shi, 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.

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Dans cette thèse nous utilisons des méthodes globaux de prédiction des structures cristallographiques combinés à des techniques de grande capacité de traitement de données afin de prédire la structure cristalline de différents systèmes et dans des conditions thermodynamiques variées. Nous avons réalisé des prédictions structurales utilisant l'analyse cristalline par optimisation par essaims particuliers (CALYPSO) combinés avec la Théorie Fonctionnel de la Densité (DFT) ce qui a permis de mettre en évidence la stabilité de plusieurs composés jusqu'à la inconnus dans le digramme de phases du système Ba-Si et dans le système N-H-O. Nous avons également réalisé une étude à haute capacité de traitement de données sur un système ternaire de composition ABX2. Nous avons utilisé la Théorie Fonctionnel de la Densité combinant calculs de prototypes structuraux à partir des prédictions structurelles avec la méthode. Dans les paragraphes suivants nous résumons le contenu de différents chapitres de cette thèse. Le premier chapitre qui constitue une brève introduction au travail de cette thèse est suivi du chapitre 2 présentant les aspects théoriques utilisés dans ce travail. D'abord il est fait une brève introduction à la Théorie Fonctionnel de la Densité. A continuation nous décrivons quelques fonctions d'échange-corrélation choisies qui constituent des approximations rendant l'utilisation de la DFT efficace. Ensuite nous présentons différents procédés de prédiction structurale, et en particulier les algorithmes d'optimisation par essaims particuliers et de « Minima Hopping » qeu nous avons utilisés dans cette thèse. Finalement il est discuté comment doit-on se prendre pour évaluer la stabilité thermodynamique des nouvelles phases identifiées. Dans le chapitre 3, nous considérons le système Ba-Si. A travers l'utilisation d'une recherche structurale non-biaisée basée sur l'algorithme d'optimisation par essaims particuliers combinée avec des calculs DFT, nous faisons une étude systématique de la stabilité des phases et de la diversité structurale du système binaire Ba-Si sous haute pression. Le diagramme de phases résultant est assez complexe avec plusieurs compositions se stabilisant et se déstabilisant en fonction de la pression. En particulier, nous avons identifié des nouvelles phases de stœchiométrie BaSi, BaSi2, BaSi3 et BaSi5 qui devraient pouvoir être synthétisées expérimentalement dans un domaine de pressions étendu. Dans le chapitre 4 est présentée notre étude du diagramme de phases du système N-H-O. S'appuyant sur une recherche structural «évolutive » de type ab initio, nous prédisons deux nouvelles phases du système ternaire N-H-O qui sont NOH4 et HNO3 à de pressions allant jusqu'à 150 GPa. La nouvelle phase de NOH4 est stable entre 71 et 150 GPa, tandis que HNO3 est stable entre 39 et 150 GPa (la pression maximum de cette étude). Ces deux nouvelles phases sont lamellaires. Nous confirmons également que la composition NOH5 perd son stabilité pour des pressions supérieures à 122 GPa se décomposant en NH3 et H2O à cette pression. Le chapitre 5 se focalise sur les électrodes transparentes de type-p à base des chalcogénures ternaires. Nous utilisons une approche à grande capacité de traitement de données basée sur la DFT pour obtenir la delafossite et d'autres phases voisines de composition ABX2. Nous trouvons 79 systèmes qui sont absents de la base de données « Materials project database », qui sont stables du point de vue thermodynamique et qui cristallisent soit dans la structure delafossite, soit dans des structures très proches. Cette caractérisation révèle une grande diversité de propriétés allant depuis les métaux ordinaires aux métaux magnétiques et permettant d'identifier quelques candidats pour des électrodes transparents de type-p. Nous présentons enfin à la fin du manuscrit nos conclusions générales et les perspectives de ce travail
In 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
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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.

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Books on the topic "Ab initio prediction"

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Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.
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Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic potential functions using NNs; (v) self-starting methods for obtaining analytic PES from ab inito electronic structure calculations using direct dynamics; (vi) development of a novel method, namely, combined function derivative approximation (CFDA) for simultaneous fitting of a PES and its corresponding force fields using feedforward neural networks; (vii) development of generalized PES using many-body expansions, NNs, and moiety energy approximations; (viii) NN methods for data analysis, reaction probabilities, and statistical error reduction in chemical reaction dynamics; (ix) accurate prediction of higher-level electronic structure energies (e.g. MP4 or higher) for large databases using NNs, lower-level (Hartree-Fock) energies, and small subsets of the higher-energy database; and finally (x) illustrative examples of NN applications to chemical reaction dynamics of increasing complexity starting from simple near equilibrium structures (vibrational state studies) to more complex non-adiabatic reactions. The monograph is prepared by an interdisciplinary group of researchers working as a team for nearly two decades at Oklahoma State University, Stillwater, OK with expertise in gas phase reaction dynamics; neural networks; various aspects of MD and Monte Carlo (MC) simulations of nanometric cutting, tribology, and material properties at nanoscale; scaling laws from atomistic to continuum; and neural networks applications to chemical reaction dynamics. It is anticipated that this emerging field of NN in chemical reaction dynamics will play an increasingly important role in MD, MC, and quantum mechanical studies in the years to come.
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Neupane, Raddha, and 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.

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Book chapters on the topic "Ab initio prediction"

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Lee, Jooyoung, Peter L. Freddolino, and Yang Zhang. "Ab Initio Protein Structure Prediction." In 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.

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Mishra, Akhilesh, Priyanka Siwach, Poonam Singhal, and B. Jayaram. "ChemGenome2.1: An Ab Initio Gene Prediction Software." In 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.

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Abbass, Jad, Jean-Christophe Nebel, and Nashat Mansour. "Ab Initio Protein Structure Prediction: Methods and challenges." In Biological Knowledge Discovery Handbook, 703–24. Hoboken, New Jersey: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch32.

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Liu, L. Angela, and Joel S. Bader. "Structure-Based Ab Initio Prediction of Transcription Factor–Binding Sites." In Methods in Molecular Biology, 23–41. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-243-4_2.

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Hoque, Md T., M. Chetty, and L. S. Dooley. "Significance of Hybrid Evolutionary Computation for Ab Initio Protein Folding Prediction." In Hybrid Evolutionary Algorithms, 241–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_10.

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Ceci, G., A. Mucherino, M. D’Apuzzo, D. Di Serafino, S. Costantini, A. Facchiano, and G. Colonna. "Computational Methods for Protein Fold Prediction: an Ab-initio Topological Approach." In Data Mining in Biomedicine, 391–429. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-69319-4_21.

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Zhang, Z., L. Lu, P. Wu, and C. Shu. "Prediction of Defects in PZT Thin Film Using Ab-Initio Method." In 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.

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Eyrich, Volker A., Richard A. Friesner, and Daron M. Standley. "Ab Initio Protein Structure Prediction Using a Size-dependent Tertiary Folding Potential." In Computational Methods for Protein Folding, 223–63. New York, USA: John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471224421.ch6.

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Pantelides, Constantinos C., Claire S. Adjiman, and Andrei V. Kazantsev. "General Computational Algorithms for Ab Initio Crystal Structure Prediction for Organic Molecules." In Topics in Current Chemistry, 25–58. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/128_2013_497.

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Lipinski-Paes, Thiago, Michele dos Santos da Silva Tanus, José Fernando Ruggiero Bachega, and Osmar Norberto de Souza. "A Multiagent Ab Initio Protein Structure Prediction Tool for Novices and Experts." In Bioinformatics Research and Applications, 163–74. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38782-6_14.

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Conference papers on the topic "Ab initio prediction"

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Haskins, Peter J. "Ab-Initio Prediction of Impact Sensitivity." In 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.

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Lesk, Arthur M. "Assessment of ab initio protein structure prediction." In the second annual international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/279069.279110.

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Srinivasan, Rajgopal, and George D. Rose. "Protein structure prediction — An Ab initio approach." In 2003 European Control Conference (ECC). IEEE, 2003. http://dx.doi.org/10.23919/ecc.2003.7086562.

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LIU, L. ANGELA, and JOEL S. BADER. "AB INITIO PREDICTION OF TRANSCRIPTION FACTOR BINDING SITES." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772435_0046.

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"Comparison of Four Ab Initio MicroRNA Prediction Tools." In International Conference on Bioinformatics Models, Methods and Algorithms. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004248201900195.

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Zhou, Peng-Fang, Fei Zhang, Yang Zhang, Zhen-Hua Zhao, De-Li Zhang, and Wen-Qian Zhang. "SusMiRPred: Ab Initio SVM Classification for Porcine MicroRNA Precursor Prediction." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5516745.

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Herndon, Nic, and Doina Caragea. "Ab initio Splice Site Prediction with Simple Domain Adaptation Classifiers." In 7th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005710502450252.

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Hautier, Geoffroy. "Prediction of new battery materials based on ab initio computations." In 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.

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Becerra, David, Angelica Sandoval, Daniel Restrepo-Montoya, and F. Nino Luis. "A parallel multi-objective ab initio approach for protein structure prediction." In 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706552.

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Ferreira, A. L. C. "Prediction of liquid C[sub 60] from ab initio intermolecular potential." In Modeling complex systems. AIP, 2001. http://dx.doi.org/10.1063/1.1386838.

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Reports on the topic "Ab initio prediction"

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Gregurick, S. K. AB Initio Protein Tertiary Structure Prediction: Comparative-Genetic Algorithm with Graph Theoretical Methods. Office of Scientific and Technical Information (OSTI), April 2001. http://dx.doi.org/10.2172/834523.

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Yao, 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), January 2009. http://dx.doi.org/10.2172/972073.

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Thompson, Donald L. Ab Initio-Based Predictions of Hydrocarbon Combustion Chemistry. Fort Belvoir, VA: Defense Technical Information Center, July 2015. http://dx.doi.org/10.21236/ada624250.

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Andersson, Anders, and 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), August 2021. http://dx.doi.org/10.2172/1813811.

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