Systems composed of soft matter (e.g., liquids, polymers, foams, gels, colloids, and most biological materials) are ubiquitous in science and engineering, but molecular simulations of such systems pose particular computational challenges, requiring time and/or ensemble-averaged data to be collected over long simulation trajectories for property evaluation. Performing a molecular simulation of a soft matter system involves multiple steps, which have traditionally been performed by researchers in a "bespoke" fashion, resulting in many published soft matter simulations not being reproducible based on the information provided in the publications. To address the issue of reproducibility and to provide tools for computational screening, we have been developing the open-source Molecular Simulation and Design Framework (MoSDeF) software suite.In this paper, we propose a set of principles to create Transparent, Reproducible, Usable by others, and Extensible (TRUE) molecular simulations. MoSDeF facilitates the publication and dissemination of TRUE simulations by automating many of the critical steps in molecular simulation, thus enhancing their reproducibility. We provide several examples of TRUE molecular simulations: All of the steps involved in creating, running and extracting properties from the simulations are distributed on open-source platforms (within MoSDeF and on GitHub), thus meeting the definition of TRUE simulations.

Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.

MoSDeF-GOMC is a python interface for the Monte Carlo software GOMC to the Molecular Simulation Design Framework (MoSDeF) ecosystem. MoSDeF-GOMC automates the process of generating initial coordinates, assigning force field parameters, and writing coordinate (PDB), connectivity (PSF), force field parameter, and simulation control files. The software lowers entry barriers for novice users while allowing advanced users to create complex workflows that encapsulate simulation setup, execution, and data analysis in a single script. All relevant simulation parameters are encoded within the workflow, ensuring reproducible simulations. MoSDeF-GOMC’s capabilities are illustrated through a number of examples, including prediction of the adsorption isotherm for CO2 in IRMOF-1, free energies of hydration for neon and radon over a broad temperature range, and the vapor–liquid coexistence curve of a four-component surrogate for the jet fuel S-8. The MoSDeF-GOMC software is available on GitHub at .