主族元素AB4型含氧酸根的成键分析——推荐一个研究型计算化学实验

介绍了一个面向高年级本科生的研究型计算化学实验。主族元素AB4型含氧酸根是无机和结构化学理论课程中讨论化学键类型的例子,然而其结果却存在争议。本实验利用常用量子化学软件,通过计算化学方法分析化学成键,验证猜测,并得出结论。旨在通过本实验,锻炼学生对量子化学计算方法的运用,进而加深对化学基础知识的理解。     

Advance of computational technology for simulating solvent extraction.

Due to recent significant enhancement of computer performance as well as computational techniques, molecular modeling and molecular simulations using computational chemistry can be achieved at the level of practical applications. Even in solvent extraction, the application of computational chemistry to simulations of extraction processes and the molecular design of high-performance extracting agents have gradually been increasing during the last decade. With combining the quantitative structure-property relationship between the molecule properties calculated by the computational chemistry methods and the thermodynamic properties obtained from experiments, researchers can precisely predict the next-generation of extracting agents and novel extraction processes. In this review, the concept of computational chemistry, such as molecular mechanics, molecular orbitals and molecular dynamics calculations, frequently used in the filed of solvent extraction, are outlined. Our systematic research on the solvent-extraction process utilizing MM, MO and MD calculations is also presented.     

Cuby: An integrative framework for computational chemistry

Cuby is a computational chemistry framework written in the Ruby programming language. It provides unified access to a wide range of computational methods by interfacing external software and it implements various protocols that operate on their results. Using structured input files, elementary calculations can be combined into complex workflows. For users, Cuby provides a unified and userfriendly way to automate their work, seamlessly integrating calculations carried out in different computational chemistry programs. For example, the QM/MM module allows combining methods across the interfaced programs and the builtin molecular dynamics engine makes it possible to run a simulation on the resulting potential. For programmers, it provides high‐level, object‐oriented environment that allows rapid development and testing of new methods and computational protocols. The Cuby framework is available for download at http://cuby4.molecular.cz . © 2016 Wiley Periodicals, Inc.     

Parallelism in computational chemistry

Summary An account is given of experience gained in implementing computational chemistry application software, including quantum chemistry and macromolecular refinement codes, on distributed memory parallel processors. In quantum chemistry we consider the coarse-grained implementation of Gaussian integral and derivative integral evaluation, the direct-SCF computation of an uncorrelated wavefunction, the 4-index transformation of two-electron integrals and the direct-CI calculation of correlated wavefunctions. In the refinement of macromolecular conformations, we describe domain decomposition techniques used in implementing general purpose molecular mechanics, molecular dynamics and free energy perturbation calculations. Attention is focused on performance figures obtained on the Intel iPSC/2 and iPSC/860 hypercubes, which are compared with those obtained on a Cray Y-MP/464 and Convex C-220 minisupercomputer. From this data we deduce the cost effectiveness of parallel processors in the field of computational chemistry.     

Computational Chemistry to the Rescue: Modern Toolboxes for the Assignment of Complex Molecules by GIAO NMR Calculations

The calculations of NMR properties of molecules using quantum chemical methods have deeply impacted several branches of organic chemistry. They are particularly important in structural or stereochemical assignments of organic compounds, with implications in total synthesis, stereoselective reactions, and natural products chemistry. In studying the evolution of the strategies developed to support (or reject) a structural proposal, it becomes clear that the most effective and accurate ones involve sophisticated procedures to correlate experimental and computational data. Owing to their relatively high mathematical complexity, such calculations (CP3, DP4, ANN‐PRA) are often carried out using additional computational resources provided by the authors (such as applets or Excel files). This Minireview will cover the state‐of‐the‐art of these toolboxes in the assignment of organic molecules, including mathematical definitions, updates, and discussion of relevant examples.     

Particle formation and surface processes on atmospheric aerosols: A review of applied quantum chemical calculations

Aerosols significantly influence atmospheric processes such as cloud nucleation, heterogeneous chemistry, and heavy-metal transport in the troposphere. The chemical and physical complexity of atmospheric aerosols results in large uncertainties in their climate and health effects. In this article, we review recent advances in scientific understanding of aerosol processes achieved by the application of quantum chemical calculations. In particular, we emphasize recent work in two areas: new particle formation and heterogeneous processes. Details in quantum chemical methods are provided, elaborating on computational models for prenucleation, secondary organic aerosol formation, and aerosol interface phenomena. Modeling of relative humidity effects, aerosol surfaces, and chemical kinetics of reaction pathways is discussed. Because of their relevance, quantum chemical calculations and field and laboratory experiments are compared. In addition to describing the atmospheric relevance of the computational models, this article also presents future challenges in quantum chemical calculations applied to aerosols.     

Some recent applications of ab initio electronic structure methods to metal,semimetal, and molecular clusters

Recent experimental and theoretical cluster studies are reviewed. Areas of current and developing interest in theoretical and computational chemistry are identified. Some promising methods applied to metal clusters, main group clusters, molecular clusters, spectroscopy, and models of cluster-molecule reactions are indicated. Results of calculations on small hydrogenated lithium clusters and hydrated sodium clusters are discussed in some detail.     

Ab initio aqueous thermochemistry: application to the oxidation of hydroxylamine in nitric acid solution

Ab initio molecular orbital calculations were performed and thermochemical parameters estimated for 46 species involved in the oxidation of hydroxylamine in aqueous nitric acid solution. Solution-phase properties were estimated using the several levels of theory in Gaussian03 and using COSMOtherm. The use of computational chemistry calculations for the estimation of physical properties and constants in solution is addressed. The connection between the pseudochemical potential of Ben-Naim and the traditional standard state-based thermochemistry is shown, and the connection of these ideas to computational chemistry results is established. This theoretical framework provides a basis for the practical use of the solution-phase computational chemistry estimates for real systems, without the implicit assumptions that often hide the nuances of solution-phase thermochemistry. The effect of nonidealities and a method to account for them is also discussed. A method is presented for estimating the solvation enthalpy and entropy for dilute aqueous solutions based on the solvation free energy from the ab initio calculations. The accuracy of the estimated thermochemical parameters was determined through comparison with (i) enthalpies of formation in the gas phase and in solution, (ii) Henry's law data for aqueous solutions, and (iii) various reaction equilibria in aqueous solution. Typical mean absolute deviations (MAD) for the solvation free energy in room-temperature water appear to be ~1.5 kcal/mol for most methods investigated. The MAD for computed enthalpies of formation in solution was 1.5-3 kcal/mol, depending on the methodology employed and the type of species (ion, radical, closed-shell) being computed. This work provides a relatively simple and unambiguous approach that can be used to estimate the thermochemical parameters needed to build detailed ab initio kinetic models of systems in aqueous solution. Technical challenges that limit the accuracy of the estimates are highlighted.     

cclib: a library for package-independent computational chemistry algorithms

There are now a wide variety of packages for electronic structure calculations, each of which differs in the algorithms implemented and the output format. Many computational chemistry algorithms are only available to users of a particular package despite being generally applicable to the results of calculations by any package. Here we present cclib, a platform for the development of package-independent computational chemistry algorithms. Files from several versions of multiple electronic structure packages are automatically detected, parsed, and the extracted information converted to a standard internal representation. A number of population analysis algorithms have been implemented as a proof of principle. In addition, cclib is currently used as an input filter for two GUI applications that analyze output files: PyMOlyze and GaussSum.     

机器学习在团簇研究中的可能应用

团簇的结构-能量关系非常复杂,存在大量的局部能量极小点,寻找全局稳定结构是个很艰巨的任务。对于中大尺寸团簇的计算,采用纯量子力学计算方法将面临非常重的计算负担,而如果采用传统力场的方法则会面临精度不足的困难。近年来崛起的机器学习浪潮,开始渗透到包括化学在内的各个学科领域。基于机器学习的方法,有可能提供一条介乎量子力学与传统力场之间的中间新路线。发展基于机器学习的团簇势能模型,用于寻找中大尺寸团簇的稳定结构,有望为团簇的理论与计算研究提供新思路与新手段。     

A versatile AMBER-Gaussian QM/MM interface through PUPIL

The PUPIL package (Program for User Package Interfacing and Linking) originally was developed to interface different programs for multiscale calculations in materials sciences (Torras et al., J Comput Aided Mater Des 2006, 13, 201; Torras et al., Comput Phys Commun 2007, 177, 265). Here we present an extension of PUPIL to computational chemistry by interfacing two widely used computational chemistry programs: AMBER (molecular dynamics) and Gaussian (quantum chemistry). The benefit is to allow the application of the advanced MD techniques available in AMBER to a hybrid QM/MM system in which the forces and energy on the QM part can be computed by any of the methods available in Gaussian. To illustrate, we present two example applications: A MD calculation of alanine dipeptide in explicit water, and a use of the steered MD capabilities in AMBER to calculate the free energy of reaction for the dissociation of Angeli's salt.     

Symmetrizer: Algorithmic determination of point groups in nearly symmetric molecules

Symmetry is an extremely useful and powerful tool in computational chemistry, both for predicting the properties of molecules and for simplifying calculations. Although methods for determining the point groups of perfectly symmetric molecules are well‐known, finding the closest point group for a “nearly” symmetric molecule is far less studied, although it presents many useful applications. For this reason, we introduce Symmetrizer, an algorithm designed to determine a molecule's symmetry elements and closest matching point groups based on a user‐adjustable tolerance, and then to symmetrize that molecule to a given point group geometry. In contrast to conventional methods, Symmetrizer takes a bottom‐up approach to symmetry detection by locating all possible symmetry elements and uses this set to deduce the most probable point groups. We explain this approach in detail, and assess the flexibility, robustness, and efficiency of the algorithm with respect to various input parameters on several test molecules. We also demonstrate an application of Symmetrizer by interfacing it with the WebMO web‐based interface to computational chemistry packages as a showcase of its ease of integration. © 2012 Wiley Periodicals, Inc.     

Identification of hydrolysis products of AlCl(3)*6H(2)O in the presence of sulfate by electrospray ionization time-of-flight mass spectrometry and computational methods

ElectroSpray Ionization-Mass Spectrometry (ESI-MS) and computational methods (DFT, MP2, and COSMO) were used to investigate the hydrolysis products of aluminium chloride as a function of sulfate concentration at pH 3.7. With the aid of computational chemistry, structural information was deduced from the chemical compositions observed with ESI-MS. Many novel types of hydrolysis products were noted, revealing that our present understanding of aluminium speciation is too simple. The role of counterions was found to be critical: the speciation of aluminium changed markedly as a function of sulfate concentration. Ab initio calculations were used to reveal the energetically most favoured structures of aluminium sulfate anions and cations selected from the ESI-MS results. Several interesting observations were made. Most importantly, the bonding behaviour of the sulfate group changed as the number of aqua ligands increased. The accompanying structural rearrangement of the clusters revealed the highly active role of sulfate as a ligand. The gas phase calculations were expanded to the aquatic environment using a conductor-like screening model. As expected, the bonding behaviour of the sulfate group in the minimum energy structures was distinctly different in the aquatic environment compared to the gas phase. Together, these methods open a new window for research in the solution chemistry of aluminium species.     

Kinetic barriers in the isomerization of substituted ureas: implications for computer-aided drug design

Urea derivatives are ubiquitously found in many chemical disciplines. N,N′-substituted ureas may show different conformational preferences depending on their substitution pattern. The high energetic barrier for isomerization of the cis and trans state poses additional challenges on computational simulation techniques aiming at a reproduction of the biological properties of urea derivatives. Herein, we investigate energetics of urea conformations and their interconversion using a broad spectrum of methodologies ranging from data mining, via quantum chemistry to molecular dynamics simulation and free energy calculations. We find that the inversion of urea conformations is inherently slow and beyond the time scale of typical simulation protocols. Therefore, extra care needs to be taken by computational chemists to work with appropriate model systems. We find that both knowledge-driven approaches as well as physics-based methods may guide molecular modelers towards accurate starting structures for expensive calculations to ensure that conformations of urea derivatives are modeled as adequately as possible.     

Acceleration of catalyst discovery with easy,fast, and reproducible computational alchemy

The expense of quantum chemistry calculations significantly hinders the search for novel catalysts. Here, we provide a tutorial for using an easy and highly cost‐efficient calculation scheme, called alchemical perturbation density functional theory (APDFT), for rapid predictions of binding energies of reaction intermediates and reaction barrier heights based on the Kohn‐Sham density functional theory (DFT) reference data. We outline standard procedures used in computational catalysis applications, explain how computational alchemy calculations can be carried out for those applications, and then present benchmarking studies of binding energy and barrier height predictions. Using a single OH binding energy on the Pt(111) surface as a reference case, we use computational alchemy to predict binding energies of 32 variations of this system with a mean unsigned error of less than 0.05 eV relative to single‐point DFT calculations. Using a single nudged elastic band calculation for CH₄ dehydrogenation on Pt(111) as a reference case, we generate 32 new pathways with barrier heights having mean unsigned errors of less than 0.3 eV relative to single‐point DFT calculations. Notably, this easy APDFT scheme brings no appreciable computational cost once reference calculations are performed, and this shows that simple applications of computational alchemy can significantly impact DFT‐driven explorations for catalysts. To accelerate computational catalysis discovery and ensure computational reproducibility, we also include Python modules that allow users to perform their own computational alchemy calculations.