Peter V. Coveney

Bio: Peter V. Coveney is an academic researcher from University College London. The author has contributed to research in topics: Lattice Boltzmann methods & Grid computing. The author has an hindex of 64, co-authored 551 publications receiving 17025 citations. Previous affiliations of Peter V. Coveney include Queen Mary University of London & University of London.

Scalable Quantum Simulation of Molecular Energies

Abstract: We report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation. We use a programmable array of superconducting qubits to compute the energy surface of molecular hydrogen using two distinct quantum algorithms. First, we experimentally execute the unitary coupled cluster method using the variational quantum eigensolver. Our efficient implementation predicts the correct dissociation energy to within chemical accuracy of the numerically exact result. Second, we experimentally demonstrate the canonical quantum algorithm for chemistry, which consists of Trotterization and quantum phase estimation. We compare the experimental performance of these approaches to show clear evidence that the variational quantum eigensolver is robust to certain errors. This error tolerance inspires hope that variational quantum simulations of classically intractable molecules may be viable in the near future.

Scalable Quantum Simulation of Molecular Energies

Abstract: We report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation. We use a programmable array of superconducting qubits to compute the energy surface of molecular hydrogen using two distinct quantum algorithms. First, we experimentally execute the unitary coupled cluster method using the variational quantum eigensolver. Our efficient implementation predicts the correct dissociation energy to within chemical accuracy of the numerically exact result. Second, we experimentally demonstrate the canonical quantum algorithm for chemistry, which consists of Trotterization and quantum phase estimation. We compare the experimental performance of these approaches to show clear evidence that the variational quantum eigensolver is robust to certain errors. This error tolerance inspires hope that variational quantum simulations of classically intractable molecules may be viable in the near future.

Monte Carlo Molecular Modeling Studies of Hydrated Li-, Na-, and K-Smectites: Understanding the Role of Potassium as a Clay Swelling Inhibitor

Abstract: Monte Carlo molecular modeling simulations have been performed to investigate some of the microscopic mechanisms underlying smectite clay swelling. The systems we have studied represent hydrated Wyoming montmorillonites in which the counterions are Li+, Na+, or K+. For each of these three counterions we have conducted a series of 15 simulations in which the water content is increased systematically from 0 to 300 mg/g of clay. In this paper we introduce a new set of interaction potentials that can be used to study clay-water-cation systems and which lend themselves to future studies of intercalated organic molecules. We compare simulation data for the clay layer spacing with experimental swelling curves and find that the level of agreement is striking. Interestingly, in regions where the experimental curves exhibit hysteresis for adsorption/desorption, these loops enclose our calculated data. This indicates that our simulations indeed represent states of thermodynamic equilibrium, conditions which are harder to achieve experimentally. We are therefore content with our choice of atomistic interaction potentials for the current work. Analysis of the microscopic interlayer structure reveals a qualitative difference between Li+ and Na+ on the one hand and K+ on the other. We find that as the water content is increased, both Li+ and Na+ are able to hydrate, thereby becoming detached from the clay surface. In contrast, we find that K+ ions migrate to and bind to the clay surface during our simulations. Therefore ECC ions screen the negatively charged, mutually repelling clay surfaces more effectively than Naf and Lif ions do. The reluctance of K+ ions to fully hydrate will reduce the tendency of K+-saturated clays to expand. Our simulations therefore provide a qualitatively satisfying insight into the role of K+ ions as a clay swelling inhibitor. Indeed, the differences between Na+ and K+ ions displayed in these simulations may also be of more general relevance to colloid chemistry and physiology.

A critical appraisal of polymer-clay nanocomposites.

Abstract: The surge of interest in and scientific publications on the structure and properties of nanocomposites has made it rather difficult for the novice to comprehend the physical structure of these new materials and the relationship between their properties and those of the conventional range of composite materials. Some of the questions that arise are: How should the reinforcement volume fraction be calculated? How can the clay gallery contents be assessed? How can the ratio of intercalate to exfoliate be found? Does polymerization occur in the clay galleries? How is the crystallinity of semi-crystalline polymers affected by intercalation? What role do the mobilities of adsorbed molecules and clay platelets have? How much information can conventional X-ray diffraction offer? What is the thermodynamic driving force for intercalation and exfoliation? What is the elastic modulus of clay platelets? The growth of computer simulation techniques applied to clay materials has been rapid, with insight gained into the structure, dynamics and reactivity of polymer–clay systems. However these techniques operate on the basis of approximations, which may not be clear to the non-specialist. This critical review attempts to assess these issues from the viewpoint of traditional composites thereby embedding these new materials in a wider context to which conventional composite theory can be applied. (210 references)

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.