Incorporating solvent effects in DFT: insights from cation exchange in faujasites.

Zeolites are versatile materials renowned for their extra-framework cation exchange capabilities, with applications spanning diverse fields, including nuclear waste treatment. While detailed experimental characterization offers valuable insight, density functional theory (DFT) proves particularly adept at investigating ion exchange in zeolites, owing to its atomic and electronic resolution. However, the prevalent occurrence of zeolitic ion exchange in aqueous environments poses a challenge to conventional DFT modeling, traditionally conducted in a vacuum. This study seeks to enhance zeolite modeling by systematically evaluating predictive differences across varying degrees of aqueous solvent inclusion. Specifically focusing on monovalent cation exchange in Na-X zeolites, we explore diverse modeling approaches. These range from simple dehydrated systems (representing bare reference states in vacuum) to more sophisticated models that incorporate aqueous solvent effects through explicit water molecules and/or a dielectric medium. Through comparative analysis of DFT and semi-empirical DFT approaches, along with their validation against experimental results, our findings underscore the necessity to concurrently consider explicit and implicit solvent effects for accurate prediction of zeolitic ionic exchange.

Computational Design of Low Melting Eutectics of Molten Salts: A Combined Machine Learning and Thermodynamic Modeling Approach.

We develop a computational framework combining thermodynamic and machine learning models to predict the melting temperatures of molten salt eutectic mixtures (Teut). The model shows an accuracy of ∼6% (mean absolute percentage error) over the entire data set. Using this approach, we screen millions of combinatorial eutectics ranging from binary to hexanary, predict new mixtures, and propose design rules that lead to low Teut. We show that heterogeneity in molecular sizes, quantified by the molecular volume of the components, and mixture configurational entropy, quantified by the number of mixture components, are important factors that can be exploited to design low Teut mixtures. While predicting eutectic composition with existing techniques had proved challenging, we provide some preliminary models for estimating the compositions. The high-throughput screening technique presented here is essential to design novel mixtures for target applications and efficiently navigate the vast design space of the eutectic mixtures.

Ligand Optimization of Exchange Interaction in Co(II) Dimer Single Molecule Magnet by Machine Learning.

Designing single-molecule magnets (SMMs) for potential applications in quantum computing and high-density data storage requires tuning their magnetic properties, especially the strength of the magnetic interaction. These properties can be characterized by first-principles calculations based on density functional theory (DFT). In this work, we study the experimentally synthesized Co(II) dimer (Co2(C5NH5)4(µ-PO2(CH2C6H5)2)3) SMM with the goal to control the exchange energy, ΔEJ, between the Co atoms through tuning of the capping ligands. The experimentally synthesized Co(II) dimer molecule has a very small ΔEJ < 1 meV. We assemble a DFT data set of 1081 ligand substitutions for the Co(II) dimer. The ligand exchange provides a broad range of exchange energies, ΔEJ, from +50 to -200 meV, with 80% of the ligands yielding a small ΔEJ < 10 meV. We identify descriptors for the classification and regression of ΔEJ using gradient boosting machine learning models. We compare one-hot encoded, structure-based, and chemical descriptors consisting of the HOMO/LUMO energies of the individual ligands and the maximum electronegativity difference and bond order for the ligand atom connecting to Co. We observe a similar overall performance with the chemical descriptors outperforming the other descriptors. We show that the exchange coupling, ΔEJ, is correlated to the difference in the average bridging angle between the ferromagnetic and antiferromagnetic states, similar to the Goodenough-Kanamori rules.