Open-Source Molecular Modeling Software in Chemical Engineering Focusing on the Molecular Simulation Design Framework

Abstract

Molecular simulation has emerged as an important sub-field of chemical engineering, due in no small part to the leadership of Keith Gubbins. A characteristic of the chemical engineering molecular simulation community is the commitment to freely share simulation codes and other key software components required to perform a molecular simulation under open-source licenses and distribution on public repositories such as GitHub. Here we provide an overview of open-source molecular modeling software in Chemical Engineering, with focus on the Molecular Simulation Design Framework (MoSDeF). MoSDeF is an open-source Python software stack that enables facile use of multiple open-source molecular simulation engines, while at the same time ensuring maximum reproducibility.

An accurate and interpretable deep learning model for environmental properties prediction using hybrid molecular representations

“…Environmental benefit, which is one of the three pillars of sustainable development, 1 promotes the application of green chemistry and moves the chemical industry forward 2–6 . Many environmental impact factors (such as toxicity, CO 2 emissions, and water consumption) should be incorporated in molecule design, 7–9 solvent selection 10–12 as well as chemical synthesis 13–16 . As a fundamental environmental property, lipophilicity plays a vital role in governing kinetic and thermodynamics aspects of organic compound reaction 17 and provides crucial information on absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) 18–20 …”

Investigation of membrane fouling using molecular dynamics simulation

“…On the other hand, compared to another atomistic simulation method like Monte Carlo (MC) simulation, MD has a larger spatial scale. The reason is that MC requires a very large number of mutations of system configuration until convergence, which are generated via a Markov chain process based on the appropriate equilibrium ensemble probability [22], so the final system configuration represents the equilibrium at infinite time. For MD, the trajectory generated is an evolution of the system configuration with time, from which physical quantities can be extracted and time-averaged.…”

“…Moreover, a fundamental problem of MD is that the system tends to be trapped in a local minimum of adsorption without tending towards the global minimum spontaneously [24,38]. To circumvent such issues, some studies chose to perform a few replicate runs starting from different initial foulant positions/orientations to improve the statistical validity [22], as shown in Table 2-1. In addition to the limitations mentioned above, other factors that usually are not accounted for in MD simulation are described as follows.…”

“…provides a convenient and user-friendly platform to run MD from scratch, and many open-source codes and related supporting utilities have been developed (as summarized by Cummings et al [22]) to ease implementation of MD simulations.…”

“…Colors of the triangle boxes indicate whether the step belongs to the initialization phase (blue), simulation phase (red), or postprocessing and data analysis phase (yellow). Redrawn and modified based on the conceptual figure from[22].…”