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For all practical purposes the basic physical equations governing the behaviour of a system at the molecular level can only be solved approximately. The key issue in any reliable and accurate computational study in molecular physics and quantum chemistry is the adoption of a suitable model which contains the essential physics and chemistry, is computationally tractable, and preferably amenable to systematic refinement. The provision of advice on the choice of an appropriate model for a specific problem has so far received scant attention. This issue is becoming acute as `standard' software packages are becoming widely available and are being increasingly heavily used in both the academic and industrial sectors by researchers who have received no special training in the theoretical physics and chemistry that underpins them. This volume provides researchers whose background may not be in the computational molecular sciences with the necessary background to make intelligent use of the methods available by performing reliable calculations of appropriate accuracy and making a considered interpretation of the data so obtained.
The enormous complexity of biological systems at the molecular level must be answered with powerful computational methods. Computational biology is a young field, but has seen rapid growth and advancement over the past few decades. Surveying the progress made in this multidisciplinary field, the Handbook of Computational Molecular Biology of
This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.
This book covers all aspects of opacity and equations of state for gases, plasmas, and dust. The discussion emphasizes the continuous transformation of the equilibrium compositions of these phases as a function of temperature and density.
Bioinformatics is growing by leaps and bounds; theories/algorithms/statistical techniques are constantly evolving. Nevertheless, a core body of algorithmic ideas have emerged and researchers are beginning to adopt a "problem solving" approach to bioinformatics, wherein they use solutions to well-abstracted problems as building blocks to solve larger scope problems. Problem Solving Handbook for Computational Biology and Bioinformatics is an edited volume contributed by world renowned leaders in this field. This comprehensive handbook with problem solving emphasis, covers all relevant areas of computational biology and bioinformatics. Web resources and related themes are highlighted at every opportunity in this central easy-to-read reference. Designed for advanced-level students, researchers and professors in computer science and bioengineering as a reference or secondary text, this handbook is also suitable for professionals working in this industry.
The gap between introductory level textbooks and highly specialized monographs is filled by this modern textbook. It provides in one comprehensive volume the in-depth theoretical background for molecular modeling and detailed descriptions of the applications in chemistry and related fields like drug design, molecular sciences, biomedical, polymer and materials engineering. Special chapters on basic mathematics and the use of respective software tools are included. Numerous numerical examples, exercises and explanatory illustrations as well as a web site with application tools (http://www.amrita.edu/cen/ccmm) support the students and lecturers.
This textbook introduces a concise approach to the design of molecular algorithms for students or researchers who are interested in dealing with complex problems. Through numerous examples and exercises, you will understand the main difference of molecular circuits and traditional digital circuits to manipulate the same problem and you will also learn how to design a molecular algorithm of solving any a problem from start to finish. The book starts with an introduction to computational aspects of digital computers and molecular computing, data representation of molecular computing, molecular operations of molecular computing and number representation of molecular computing and provides many molecular algorithm to construct the parity generator and the parity checker of error-detection codes on digital communication, to encode integers of different formats, single precision and double precision of floating-point numbers, to implement addition and subtraction of unsigned integers, to construct logic operations including NOT, OR, AND, NOR, NAND, Exclusive-OR (XOR) and Exclusive-NOR (XNOR), to implement comparators, shifters, increase, decrease, and to complete two specific operations that are to find the maximum number of “1” and to find the minimum number of “1”. The book is also a useful reference source to people new for the field of molecular computing.
This book covers the essentials of Computational Science and gives tools and techniques to solve materials science problems using molecular dynamics (MD) and first-principles methods. The new edition expands upon the density functional theory (DFT) and how the original DFT has advanced to a more accurate level by GGA+U and hybrid-functional methods. It offers 14 new worked examples in the LAMMPS, Quantum Espresso, VASP and MedeA-VASP programs, including computation of stress-strain behavior of Si-CNT composite, mean-squared displacement (MSD) of ZrO2-Y2O3, band structure and phonon spectra of silicon, and Mo-S battery system. It discusses methods once considered too expensive but that are now cost-effective. New examples also include various post-processed results using VESTA, VMD, VTST, and MedeA.
Genetic programming (GP) is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, control, classification, system identification, and computational molecular biology. Among the solutions are 14 results competitive with human-produced results, including 10 rediscoveries of previously patented inventions.