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

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.

Further cautionary tales on thermostatting in molecular dynamics: Energy equipartitioning and non-equilibrium processes in gas-phase simulations.

Molecular dynamics (MD) simulations of gas-phase chemical reactions are typically carried out on a small number of molecules near thermal equilibrium by means of various thermostatting algorithms. Correct equipartitioning of kinetic energy among translations, rotations, and vibrations of the simulated reactants is critical for many processes occurring in the gas phase. As thermalizing collisions are infrequent in gas-phase simulations, the thermostat has to efficiently reach equipartitioning in the system during equilibration and maintain it throughout the actual simulation. Furthermore, in non-equilibrium simulations where heat is released locally, the action of the thermostat should not lead to unphysical changes in the overall dynamics of the system. Here, we explore issues related to both obtaining and maintaining thermal equilibrium in MD simulations of an exemplary ion-molecule dimerization reaction. We first compare the efficiency of global (Nosé-Hoover and Canonical Sampling through Velocity Rescaling) and local (Langevin) thermostats for equilibrating a system of flexible compounds and find that of these three only the Langevin thermostat achieves equipartition in a reasonable simulation time. We then study the effect of the unphysical removal of latent heat released during simulations involving multiple dimerization events. As the Langevin thermostat does not produce the correct dynamics in the free molecular regime, we only consider the commonly used Nosé-Hoover thermostat, which is shown to effectively cool down the reactants, leading to an overestimation of the dimerization rate. Our findings underscore the importance of thermostatting for the proper thermal initialization of gas-phase systems and the consequences of global thermostatting in non-equilibrium simulations.

Atmospheric Sulfuric Acid-Multi-Base New Particle Formation Revealed through Quantum Chemistry Enhanced by Machine Learning.

The formation of molecular clusters and secondary aerosols in the atmosphere has a significant impact on the climate. Studies typically focus on the new particle formation (NPF) of sulfuric acid (SA) with a single base molecule (e.g., dimethylamine or ammonia). In this work, we examine the combinations and synergy of several bases. Specifically, we used computational quantum chemistry to perform configurational sampling (CS) of (SA)0-4(base)0-4 clusters with five different types of bases: ammonia (AM), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA), and ethylenediamine (EDA). Overall, we studied 316 different clusters. We used a traditional multilevel funnelling sampling approach augmented by a machine-learning (ML) step. The ML made the CS of these clusters possible by significantly enhancing the speed and quality of the search for the lowest free energy configurations. Subsequently, the cluster thermodynamics properties were evaluated at the DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p) level of theory. The calculated binding free energies were used to evaluate the cluster stabilities for population dynamics simulations. The resultant SA-driven NPF rates and synergies of the studied bases are presented to show that DMA and EDA act as nucleators (although EDA becomes weak in large clusters), TMA acts as a catalyzer, and AM/MA is often overshadowed by strong bases.

Ion-Molecule Rate Constants for Reactions of Sulfuric Acid with Acetate and Nitrate Ions.

Atmospheric nucleation from precursor gases is a significant source of cloud condensation nuclei in the troposphere and thus can affect the Earth's radiative balance. Sulfuric acid, ammonia, and amines have been identified as key nucleation precursors in the atmosphere. Studies have also shown that atmospheric ions can react with sulfuric acid to form stable clusters in a process referred to as ion-induced nucleation (IIN). IIN follows similar reaction pathways as chemical ionization, which is used to detect and measure nucleation precursors via atmospheric pressure chemical ionization mass spectrometers. The rate at which ions form clusters depends on the ion-molecule rate constant. However, the rate constant varies based on the ion composition, which is often not known in the atmosphere. Previous studies have examined ion-molecule rate constants for sulfuric acid and nitrate ions but not for other atmospherically relevant ions like acetate. We report the relative rate constants of ion-molecule reactions between nitrate and acetate ions reacting with sulfuric acid. The ion-molecule rate constant for acetate and sulfuric acid is estimated to be a factor of 1.9-2.4 times higher than that of the known rate constant for nitrate and sulfuric acid. Using quantum chemistry, we find that acetate has a higher dipole moment and polarizability than nitrate. This may contribute to an increase in the collision cross-sectional area between acetate and sulfuric acid and lead to a greater reaction rate constant than nitrate. The ion-molecule rate constant for acetate with sulfuric acid will help quantify the contribution of acetate ions to atmospheric ion-induced new particle formation.

Charge transfer from Eu2+ to trivalent lanthanide co-dopants: Systematic behavior across the series.

Electron transfer processes between lanthanide activators are crucial for the functional behavior and performance of luminescent materials. Here, a multiconfigurational ab initio study reveals how direct metal-to-metal charge transfer (MMCT) between the Eu2+ luminescence activator and a Ln3+ co-dopant (Ln3+ = Ce3+, Pr3+, Nd3+, Pm3+, Sm3+, Eu3+, Gd3+, Tb3+, Dy3+, Ho3+, Er3+, Tm3+, and Yb3+) systematically dictates the luminescence and optical properties of CaF2. The combination of the structures and energies of the electronic manifolds, the vibrational force constants, and the structural properties of the donor and acceptor in the host determines the predictions of five different behaviors of CaF2:Eu2+, Ln3+ co-doped materials after MMCT absorption: formation of stable traps, MMCT emission, emission quenching, Ln3+ emission, and Eu2+ emission.