AutoSolvate: A Toolkit for Automating Quantum Chemistry Design and Discovery of Solvated Molecules

Abstract

The availability of large, high-quality data sets is crucial for artificial intelligence design and discovery in chemistry. Despite the essential roles of solvents in chemistry, the rapid computational data set generation of solution-phase molecular properties at the quantum mechanical level of theory was previously hampered by the complicated simulation procedure. Software toolkits that can automate the procedure to set up high-throughput explicit-solvent quantum chemistry (QC) calculations for arbitrary solutes and solvents in an open-source framework are still lacking. We developed AutoSolvate, an open-source toolkit to streamline the workflow for QC calculation of explicitly solvated molecules. It automates the solvated-structure generation, force field fitting, configuration sampling, and the final extraction of microsolvated cluster structures that QC packages can readily use to predict molecular properties of interest. AutoSolvate is available through both a command line interface and a graphical user interface, making it accessible to the broader scientific community. To improve the quality of the initial structures generated by AutoSolvate, we investigated the dependence of solute-solvent closeness on solute/solvent identities and trained a machine learning model to predict the closeness and guide initial structure generation. Finally, we tested the capability of AutoSolvate for rapid data set curation by calculating the outer-sphere reorganization energy of a large data set of 166 redox couples, which demonstrated the promise of the AutoSolvate package for chemical discovery efforts.

RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics

“…However, currently, the number of samples is small, and the calculation is limited to seven electrical and optical properties. For the all-atom classical molecular dynamics (MD) simulations, which are powerful techniques for computing the equilibrium and non-equilibrium properties of the condensed-phase systems of polymeric materials, there are only a few reported works that have constructed large datasets with high-throughput calculations [22][23][24] . Afzal et al created a dataset of 315 polymers using high-throughput MD simulations; however, the target properties were limited to the glass transition temperature (T g ) and thermal expansion coefficient 24 .…”

QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization

“…Hence, to incorporate QC computation in black-box optimization, it is necessary to perform QC computation in a black box by automating the multi-step calculations and the analysis of the obtained results (usually text files). There are several tools for constructing inputs to perform complex computations and parsing output files such as cclib, 25 ASE, 26 and AutoSovate 27 for managing solvent systems, and QChASM 28 that target mainly on transition states of catalysis systems. However, these tools are not enough to incorporate QC computation in black-box optimization for observable properties because their target is managing structure, distilling the total energy of the system, and one-electron-state-based values.…”

ARMer: A Python Library for Adaptive Resource Allocation in High-Throughput Workflow

“…In modern computational chemistry, high-throughput computation emerges as a new paradigm to address challenges in studying reaction mechanisms, [1][2][3] screening catalysts, [4][5][6][7][8] designing functional materials, [9][10][11][12][13] discovering drug candidates, 14,15 and modeling enzymes [16][17][18] . With the advent of machine learning, these workflows allow facile collection of molecular features, or even provide high-accuracy computational data for model training.…”