Computational Tools for Handling Molecular Clusters: Configurational Sampling, Storage, Analysis, and Machine Learning

Abstract

Computational modeling of atmospheric molecular clusters requires a comprehensive understanding of their complex configurational spaces, interaction patterns, stabilities against fragmentation, and even dynamic behaviors. To address these needs, we introduce the Jammy Key framework, a collection of automated scripts that facilitate and streamline molecular cluster modeling workflows. Jammy Key handles file manipulations between varieties of integrated third-party programs. The framework is divided into three main functionalities: (1) Jammy Key for configurational sampling (JKCS) to perform systematic configurational sampling of molecular clusters, (2) Jammy Key for quantum chemistry (JKQC) to analyze commonly used quantum chemistry output files and facilitate database construction, handling, and analysis, and (3) Jammy Key for machine learning (JKML) to manage machine learning methods in optimizing molecular cluster modeling. This automation and machine learning utilization significantly reduces manual labor, greatly speeds up the search for molecular cluster configurations, and thus increases the number of systems that can be studied. Following the example of the Atmospheric Cluster Database (ACDB) of Elm (ACS Omega, 4, 10965–10984, 2019), the molecular clusters modeled in our group using the Jammy Key framework have been stored in an improved online GitHub repository named ACDB 2.0. In this work, we present the Jammy Key package alongside its assorted applications, which underline its versatility. Using several illustrative examples, we discuss how to choose appropriate combinations of methodologies for treating particular cluster types, including reactive, multicomponent, charged, or radical clusters, as well as clusters containing flexible or multiconformer monomers or heavy atoms. Finally, we present a detailed example of using the tools for atmospheric acid–base clusters.

Reparameterization of GFN1-xTB for atmospheric molecular clusters: applications to multi-acid–multi-base systems

“…Single-point energies, gradients, and geometries for the reparameterization, configurational sampling, and comparisons were calculated using the xtb 6.4.0 program using the GFN1-xTB 33 and AMC-xTB parameterizations. A modified version of ArbAlign 45 available in the JKCS program 46 was used to calculate the root-mean-square differences (RMSD) between molecular structures. Gaussian 16, version B.01 47 was used for the DFT calculations.…”

Reparameterization of GFN1-xTB for Atmospheric Molecular Clusters: Applications to Multi-Acid–Multi-Base Systems

“…Single-point energies, gradients, and geometries for the reparameterization, configurational sampling, and comparisons were calculated using the xtb 6.4.0 program using the GFN1-xTB 27 and AMC-xTB parameterizations. A modified version of ArbAlign 39 available in the JKCS program 40 was used to calculate the root-mean-square differences (RMSD) between molecular structures. Gaussian 16, version B.01 41 was used for the DFT calculations.…”