stk: A python toolkit for supramolecular assembly

A tool for the automated assembly, molecular optimization and property calculation of supramolecular materials is presented. stk is a modular, extensible and open‐source Python library that provides a simple Python API and integration with third party computational codes. stk currently supports the construction of linear polymers, small linear oligomers, organic cages in multiple topologies and covalent organic frameworks (COFs) in multiple framework topologies, but is designed to be easy to extend to new, unrelated, supramolecules or new topologies. Extension to metal–organic frameworks (MOFs), metallocycles or supramolecules, such as catenanes, would be straightforward. Through integration with third party codes, stk offers the user the opportunity to explore the potential energy landscape of the assembled supramolecule and then calculate the supramolecule's structural features and properties. stk provides support for high‐throughput screening of large batches of supramolecules at a time. The source code of the program can be found at https://github.com/supramolecular-toolkit/stk . © 2018 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.     

PepFun: Open Source Protocols for Peptide-Related Computational Analysis

Peptide research has increased during the last years due to their applications as biomarkers, therapeutic alternatives or as antigenic sub-units in vaccines. The implementation of computational resources have facilitated the identification of novel sequences, the prediction of properties, and the modelling of structures. However, there is still a lack of open source protocols that enable their straightforward analysis. Here, we present PepFun, a compilation of bioinformatics and cheminformatics functionalities that are easy to implement and customize for studying peptides at different levels: sequence, structure and their interactions with proteins. PepFun enables calculating multiple characteristics for massive sets of peptide sequences, and obtaining different structural observables derived from protein-peptide complexes. In addition, random or guided library design of peptide sequences can be customized for screening campaigns. The package has been created under the python language based on built-in functions and methods available in the open source projects BioPython and RDKit. We present two tutorials where we tested peptide binders of the MHC class II and the Granzyme B protease.     

Thermodynamic coefficients of ideal Fermi gas

We present analytical formulae for the first and second derivatives of the Helmholtz free energy of non-relativistic ideal Fermi gas. Important thermodynamic quantities such as heat capacity, sound velocity, heat capacity ratio, and others are explicitly expressed through the derivatives. We demonstrate correct ideal Boltzmann gas and low-temperature Fermi gas asymptotes and derive corrections to thermodynamic functions for these limiting cases. Numerical computations of thermodynamic properties of ideal Fermi gas can be accurately performed using the developed freely available Python module ifg .     

缅甸蟒蛇腹鳞表面的摩擦机理及摩擦各向异性研究

采用原子力显微镜观察缅甸蟒蛇腹鳞表面的微观结构,采用UMT-2型摩擦磨损试验机研究不同载荷及运动方向的腹鳞表面的宏观摩擦各向异性,建立了摩擦运动的接触模型,分析了腹鳞表面的磨损机理.结果表明:腹鳞表面的微观结构由指状微突体和板结构部分周期排列而成,其结构可用9个特征参数定量描述;腹鳞表面摩擦力由分子作用力、表面微突体的犁沟力、楔形作用力以及材料弹性滞后共同引起;腹鳞表面的摩擦系数在0.07左右并与运动方向有关,摩擦系数随载荷增加而减小;后向运动及左、右侧向运动时摩擦系数基本相等,比前向运动时高33%左右;腹鳞表面微突体不同方向上倾斜角度的差异是引起摩擦各向异性的主要原因.研究结果对仿生制造摩擦各向异性表面提供实验依据.     

molSimplify: A toolkit for automating discovery in inorganic chemistry

We present an automated, open source toolkit for the first‐principles screening and discovery of new inorganic molecules and intermolecular complexes. Challenges remain in the automatic generation of candidate inorganic molecule structures due to the high variability in coordination and bonding, which we overcome through a divide‐and‐conquer tactic that flexibly combines force‐field preoptimization of organic fragments with alignment to first‐principles‐trained metal‐ligand distances. Exploration of chemical space is enabled through random generation of ligands and intermolecular complexes from large chemical databases. We validate the generated structures with the root mean squared (RMS) gradients evaluated from density functional theory (DFT), which are around 0.02 Ha/au across a large 150 molecule test set. Comparison of molSimplify results to full optimization with the universal force field reveals that RMS DFT gradients are improved by 40%. Seamless generation of input files, preparation and execution of electronic structure calculations, and post‐processing for each generated structure aids interpretation of underlying chemical and energetic trends. © 2016 Wiley Periodicals, Inc.     

Performance of group additivity methods for predicting the stability of uranyl complexes

Herein, we investigated the viability of two group additivity methods for predicting Gibbs energies of a set of uranyl complexes. In first place, we proved that both density functional theory (DFT)-based methods and Serezhkin's stereoatomic model provide equivalent answers in terms of stability. Moreover, we proposed a novel methodology based on Mayer's population analysis for estimating Serezhkin's empirical parameters theoretically. On the other hand, we showed that Cheong and Persson linear algebra methodology can be successfully applied to uranyl complexes, and analyzed its performance in connection with the chemical nature of the compounds employed in the model.     

Building capacity for undergraduate education and training in computational molecular science: A collaboration between the MERCURY consortium and the Molecular Sciences Software Institute

The Molecular Sciences Software Institute (MolSSI) is an National Science Foundation (NSF) funded institute that focuses on improving software, education, and training in the computational molecular sciences. Through a collaboration with the Molecular Education and Research Consortium in Undergraduate computational chemistRY (MERCURY), the MolSSI has developed resources for undergraduate and other early career students to lay an educational foundation for the next generation of computational molecular scientists. The resources focus on introducing best practices in software engineering to students from the very start to make their software more useable, maintainable, and reproducible.     

PyRETIS 2: An improbability drive for rare events

The algorithmic development in the field of path sampling has made tremendous progress in recent years. Although the original transition path sampling method was mostly used as a qualitative tool to sample reaction paths, the more recent family of interface-based path sampling methods has paved the way for more quantitative rate calculation studies. Of the exact methods, the replica exchange transition interface sampling (RETIS) method is the most efficient, but rather difficult to implement. This has been the main motivation to develop the open-source Python-based computer library PyRETIS that was released in 2017. PyRETIS is designed to be easily interfaced with any molecular dynamics (MD) package using either classical or ab initio MD. In this study, we report on the principles and the software enhancements that are now included in PyRETIS 2, as well as the recent developments on the user interface, improvements of the efficiency via the implementation of new shooting moves, easier initialization procedures, analysis methods, and supported interfaced software. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.     

基于Python-Abaqus的混凝土三维细观随机模型的建立

混凝土在细观层次上是由粗骨料、砂浆及两者间过渡区(界面层)组成的三相复合材料,建立一个能反映实际骨料级配、含量及形态的随机骨料模型是进行混凝土细观力学数值模拟的前提。本文通过编写Python脚本实现了Abaqus的二次开发,获得了含球形、椭球形(卵石)及凹凸型多面体(碎石)骨料并考虑了界面层的三维混凝土细观随机模型。结果表明,在三级配下可投放球形骨料的体分比可超过55%,对椭球和多面体骨料形状的模拟也较为真实。同时,提出了一种可提高骨料体积含量的布尔切割入侵判别法,并成功地对椭球骨料和多面体骨料进行了投放试验。由于程序已将粗骨料、砂浆和界面层自动分离,在进行网格剖分时可避免复杂的单元属性判别,得到的网格剖分满足粗骨料、砂浆及界面层网格协调性要求。最后,利用建立的几何模型进行了单轴压缩静力学数值模拟,进一步验证了混凝土细观随机模型的可靠性。     

OpenMG: A new multigrid implementation in Python

In many large‐scale computations, systems of equations arise in the form Au = b, where A is a linear operation to be performed on the unknown data u, producing the known right‐hand side, b, which represents some constraint of known or assumed behavior of the system being modeled. Because such systems can be very large, solving them directly can be too slow. In contrast, a multigrid method removes different components of the error at different resolutions using smoothers that reduce high‐frequency components of the error more readily than low. Here, we present an open‐source multigrid solver written only in Python. OpenMG is a pure Python experimentation environment for testing multigrid concepts, not a production solver. The particular restriction method implemented is for ‘standard’ multigrid. By making the code simple and modular, we make the algorithmic details clear. The resulting solver is tested on an implicit pressure reservoir simulation problem with satisfactory results.Copyright © 2013 John Wiley & Sons, Ltd.