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1. 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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2. A Python tutorial for 3DRISM solvation calculations of chemical and biological molecules

   https://doi.org/10.1063/5.0299937  

   Swanson, Peter C; Cao, Siqin; Huang, Xuhui (November 2025, The Journal of Chemical Physics)

   The 3-Dimensional Reference Interaction Site Model (3DRISM) provides a powerful grid-based solvation model for chemical and biological solutes, which balances the calculation accuracy and efficiency. We previously developed EPISOL (Expanded Package of Integral Equation Theory-Based Solvation) to enable efficient 3DRISM calculations. EPISOL implements 22 different closures and several variations of 3DRISM. EPISOL is compatible with both AMBER and GROMACS simulation packages. The original EPISOL was written in C++ and includes a kernel library that allows integration of EPISOL routines into other software. In this work, we introduce EPIPY, a Python-based package that leverages the EPISOL kernel library to streamline 3DRISM calculations. We first provide an overview of 3DRISM and then present a step-by-step tutorial on running and analyzing 3DRISM calculations using EPIPY. Our tutorial examples demonstrate how to generate water distributions around chemical compounds and ions, identify the most probable water coordinates near a protein, and compute the solvation free energies of small organic molecules. The EPIPY source code and accompanying tutorial files are available at https://github.com/EPISOLrelease/EPIPY. 

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3. An Algorithm for Generating Explainable Corrections to Student Code

   https://doi.org/10.1145/3564721.3564731  

   Malysheva, Yana; Kelleher, Caitlin (November 2022, Proceedings of the 22nd Koli Calling International Conference on Computing Education Research)

   Jormanainen, Ilkka; Petersen, Andrew (Ed.)

   Students in introductory computer science courses often need individualized help when they get stuck solving programming problems. But providing such help can be time-consuming and thought-intensive, and therefore difficult to scale as Computer Science classes grow larger in size. Automatically generated fixes with explanations have the potential to integrate into a variety of mechanisms for providing help to students who are stuck on a programming problem. In this paper, we present a data-driven algorithm for generating explainable fixes to student code. We evaluate a Python implementation of the algorithm by comparing its output at different stages of the algorithm to state-of-the-art systems with similar goals. Our algorithm outperforms existing systems that can analyze and fix beginner-written Python code. Further, fixes it generates conform very well to corrections written by human experts for an existing benchmark of code correction quality. 

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4. Mahakala : A Python -based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Spacetimes

   https://doi.org/10.3847/1538-4357/adc104  

   Sharma, Aniket; Medeiros, Lia; Wong, George N; Chan, Chi-kwan; Halevi, Goni; Mullen, Patrick D; Stone, James M (May 2025, The Astrophysical Journal)

   Abstract We introduceMahakala, aPython-based, modular, radiative ray-tracing code for curved spacetimes. We employ Google’sJAXframework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr spacetimes.JAXalso enablesMahakalato run in parallel on both CPUs and GPUs.Mahakalanatively uses the Cartesian Kerr–Schild coordinate system, which avoids numerical issues caused by the pole in spherical coordinate systems. We demonstrateMahakala’s capabilities by simulating 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature ofMahakalaallows us to quantitatively explore how different regions of the flow influence different image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole and peaks near the photon orbit. We also quantify the relative contribution of the disk, forward jet, and counterjet to 1.3 mm images. 

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5. StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code

   https://doi.org/10.18653/v1/2024.findings-acl.501  

   Babe, Hannah; Nguyen, Sydney; Zi, Yangtian; Guha, Arjun; Feldman, Molly; Anderson, Carolyn (January 2024, Association for Computational Linguistics)

   Code LLMs have the potential to make it easier for non-experts to understand and write code. However, current CodeLLM benchmarks rely on a single expert-written prompt per problem, making it hard to generalize their success to non-expert users. In this paper, we present a new natural-language-to-code benchmark of prompts written by a key population of non-experts: beginning programmers. StudentEval contains 1,749 prompts written by 80 students who have only completed one introductory Python course. StudentEval contains numerous non-expert prompts describing the same problem, enabling exploration of key factors in prompt success. We use StudentEval to evaluate 12 Code LLMs and find that StudentEval is a better discriminator of model performance than existing benchmarks. Our analysis of student prompting strategies reveals that nondeterministic LLM sampling can mislead students about the quality of their descriptions, a finding with key implications for Code LLMs in education. 

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