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1. PyQMC : An all-Python real-space quantum Monte Carlo module in PySCF

   https://doi.org/10.1063/5.0139024  

   Wheeler, William A.; Pathak, Shivesh; Kleiner, Kevin G.; Yuan, Shunyue; Rodrigues, João N. B.; Lorsung, Cooper; Krongchon, Kittithat; Chang, Yueqing; Zhou, Yiqing; Busemeyer, Brian; et al (March 2023, The Journal of Chemical Physics)

   We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions. 

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2. QChASM : Quantum chemistry automation and structure manipulation

   https://doi.org/10.1002/wcms.1510  

   Ingman, Victoria M.; Schaefer, Anthony J.; Andreola, Laura R.; Wheeler, Steven E. (December 2020, WIREs Computational Molecular Science)

   Abstract As the tools of computational quantum chemistry have continued to mature, larger and more complex molecular systems have become amenable to computational study. However, studies of these complex systems often require the execution of enormous numbers of computations, which can be a tedious and error‐prone process if done manually. We have developed a suite of free, open‐source tools to facilitate the automation of quantum chemistry workflows. These tools are collected under the organization QChASM (Quantum Chemistry Automation and Structure Manipulation) and include functionality for building and manipulating complex molecular structures and performing routine tasks (AaronTools), a toolkit for automating TS optimizations and predictions of the outcomes of selective homogeneous catalytic reactions, and a plug‐in for UCSF ChimeraX that provides a graphical interface for building complex molecular structures and representing output from quantum chemistry computations. These tools are described below, with a focus on the recent Python implementation of AaronTools. This article is categorized under:Structure and Mechanism > Reaction Mechanisms and CatalysisSoftware > Quantum Chemistry 

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3. MCell4 with BioNetGen: A Monte Carlo simulator of rule-based reaction-diffusion systems with Python interface

   https://doi.org/10.1371/journal.pcbi.1011800  

   Husar, Adam; Ordyan, Mariam; Garcia, Guadalupe C; Yancey, Joel G; Saglam, Ali S; Faeder, James R; Bartol, Thomas M; Kennedy, Mary B; Sejnowski, Terrence J (April 2024, PLOS Computational Biology)

   Meier-Schellersheim, Martin (Ed.)

   Biochemical signaling pathways in living cells are often highly organized into spatially segregated volumes, membranes, scaffolds, subcellular compartments, and organelles comprising small numbers of interacting molecules. At this level of granularity stochastic behavior dominates, well-mixed continuum approximations based on concentrations break down and a particle-based approach is more accurate and more efficient. We describe and validate a new version of the open-source MCell simulation program (MCell4), which supports generalized 3D Monte Carlo modeling of diffusion and chemical reaction of discrete molecules and macromolecular complexes in solution, on surfaces representing membranes, and combinations thereof. The main improvements in MCell4 compared to the previous versions, MCell3 and MCell3-R, include a Python interface and native BioNetGen reaction language (BNGL) support. MCell4’s Python interface opens up completely new possibilities for interfacing with external simulators to allow creation of sophisticated event-driven multiscale/multiphysics simulations. The native BNGL support, implemented through a new open-source library libBNG (also introduced in this paper), provides the capability to run a given BNGL model spatially resolved in MCell4 and, with appropriate simplifying assumptions, also in the BioNetGen simulation environment, greatly accelerating and simplifying model validation and comparison. 

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4. Tuning chain interaction entropy in complex coacervation using polymer stiffness, architecture, and salt valency

   https://doi.org/10.1039/c7me00108h  

   Lytle, Tyler K.; Sing, Charles E. (January 2018, Molecular Systems Design & Engineering)

   Oppositely-charged polyelectrolytes can undergo a liquid–liquid phase separation in a salt solution, resulting in a polymer-dense ‘coacervate’ phase that has found use in a wide range of applications from food science to self-assembled materials. Coacervates can be tuned for specific applications by varying parameters such as salt concentration and valency, polyelectrolyte length, and polyelectrolyte identity. Recent simulation and theory has begun to clarify the role of molecular structure on coacervation phase behavior, especially for common synthetic polyelectrolytes that exhibit high charge densities. In this manuscript, we use a combination of transfer matrix theory and Monte Carlo simulation to understand at a physical level how a range of molecular features, in particular polymer architecture and stiffness, and salt valency can be used to design the phase diagrams of these materials. We demonstrate a physical picture of how the underlying entropy-driven process of complex coacervation is affected by this wide range of physical attributes. 

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5. gmxapi: A GROMACS-native Python interface for molecular dynamics with ensemble and plugin support

   https://doi.org/10.1371/journal.pcbi.1009835  

   Irrgang, M. Eric; Davis, Caroline; Kasson, Peter M. (February 2022, PLOS Computational Biology)

   Schneidman-Duhovny, Dina (Ed.)

   Gmxapi provides an integrated, native Python API for both standard and advanced molecular dynamics simulations in GROMACS. The Python interface permits multiple levels of integration with the core GROMACS libraries, and legacy support is provided via an interface that mimics the command-line syntax, so that all GROMACS commands are fully available. Gmxapi has been officially supported since the GROMACS 2019 release and is enabled by default in current versions of the software. Here we describe gmxapi 0.3 and later. Beyond simply wrapping GROMACS library operations, the API permits several advanced operations that are not feasible using the prior command-line interface. First, the API allows custom user plugin code within the molecular dynamics force calculations, so users can execute custom algorithms without modifying the GROMACS source. Second, the Python interface allows tasks to be dynamically defined, so high-level algorithms for molecular dynamics simulation and analysis can be coordinated with loop and conditional operations. Gmxapi makes GROMACS more accessible to custom Python scripting while also providing support for high-level data-flow simulation algorithms that were previously feasible only in external packages. 

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