Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization

Koopmans' Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules

Density functional theory (DFT) is a significant computational tool that has substantially influenced chemistry, physics, and materials science. DFT necessitates parametrized approximation for determining an expected value. Hence, to predict the properties of a given molecule using DFT, appropriate parameters of the functional should be set for each molecule. Herein, we optimize the parameters of range-separated functionals (LC-BLYP and CAM-B3LYP) via Bayesian optimization (BO) to satisfy Koopmans' theorem. Our results demonstrate the effectiveness of the BO in optimizing functional parameters. Particularly, Koopmans' theorem-compliant LC-BLYP (KTLC-BLYP) shows results comparable to the experimental UV-absorption values. Furthermore, we prepared an optimized parameter dataset of KTLC-BLYP for over 3000 molecules through BO for satisfying Koopmans' theorem. We have developed a machine learning model on this dataset to predict the parameters of the LC-BLYP functional for a given molecule. The prediction model automatically predicts the appropriate parameters for a given molecule and calculates the corresponding values. The approach in this paper would be useful to develop new functionals and to update the previously developed functionals.

Low-Frequency Raman Spectroscopy of Pure and Cocrystallized Mycophenolic Acid

The aqueous solubility of solid-state pharmaceuticals can often be enhanced by cocrystallization with a coformer to create a binary cocrystal with preferred physical properties. Greater understanding of the internal and external forces that dictate molecular structure and intermolecular packing arrangements enables more efficient design of new cocrystals. Low-frequency (sub-200 cm^-1) Raman spectroscopy experiments and solid-state density functional theory simulations have been utilized together to investigate the crystal lattice vibrations of mycophenolic acid, an immunosuppressive drug, in its pure form and as a cocrystal with 2,2'-dipyridylamine. The lattice vibrations primarily consist of large-amplitude translations and rotations of the crystal components, thereby providing insights into the critical intermolecular forces governing cohesion of the molecular solids. The simulations reveal that despite mycophenolic acid having a significantly unfavorable conformation in the cocrystal as compared to the pure solid, the cocrystal exhibits greater thermodynamic stability over a wide temperature range. The energetic penalty due to the conformational strain is more than compensated for by the strong intermolecular forces between the drug and 2,2'-dipyridylamine. Quantifying the balance of internal and external energy factors in cocrystal formation indicates a path forward in the development of future mycophenolic acid cocrystals.

Anion-π Interactions in Monolayers Formed by Amphiphilic Electron-Deficient Aromatic Compounds at Air/Water Interfaces

Biological systems precisely and selectively control ion binding through various chemical reactions, molecular recognition, and transport by virtue of effective molecular interactions with biological membranes and proteins. Because ion binding is inhibited in highly polar media, recognition systems for anions in aqueous media, which are relevant to biological and environmental systems, are still limited. In this study, we explored the anion binding of Langmuir monolayers formed by amphiphilic naphthalenediimide (NDI) derivatives with a series of substituents at air/water interfaces via anion-π interactions. Density functional theory (DFT) simulations revealed that the binding of anions originating from anion-π interactions is related to the electron density of the anions. At the air/water interfaces, amphiphilic NDI derivatives formed Langmuir monolayers, and the addition of anions caused expansion of the Langmuir monolayers. The anions with larger hydration energies related to electron density showed larger binding constants (K_a) for 1:1 stoichiometry with the NDI derivatives. The loosely packed monolayer formed by the amphiphilic NDI derivatives with bromine groups showed a better anion response. In contrast, the binding of NO₃^- was significantly enhanced in the highly packed monolayer. These results indicate that the packing of NDI derivatives with rigid aromatic rings influenced the binding of the anions. These results provide insight into ion binding using the air/water interface as a promising recognition site for mimicking biological membranes. In future, sensing devices can be developed using Langmuir-Blodgett films on electrodes. Furthermore, the capture of anions on electron-deficient aromatic compounds can lead to doping or composition technologies for n-type semiconductors.

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

To obtain observable physical or molecular properties such as ionization potential and fluorescent wavelength with quantum chemical (QC) computation, multi-step computation manipulated by a human is required. Hence, automating the multi-step computational process and making it a black box that can be handled by anybody are important for effective database construction and fast realistic material design through the framework of black-box optimization where machine learning algorithms are introduced as a predictor. Here, we propose a Python library, QCforever, to automate the computation of some molecular properties and chemical phenomena induced by molecules. This tool just requires a molecule file for providing its observable properties, automating the computation process of molecular properties (for ionization potential, fluorescence, etc.) and output analysis for providing their multi-values for evaluating a molecule. Incorporating the tool in black-box optimization, we can explore molecules that have properties we desired within the limitation of QC computation.

De novo creation of a naked eye-detectable fluorescent molecule based on quantum chemical computation and machine learning

Designing fluorescent molecules requires considering multiple interrelated molecular properties, as opposed to properties that straightforwardly correlated with molecular structure, such as light absorption of molecules. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum chemical computation (QC) to develop fluorescent molecules, which are garnering significant attention in various disciplines. Using massive parallel computation (1024 cores, 5 days), the DNMG has produced 3643 candidate molecules. We have selected an unreported molecule and seven reported molecules and synthesized them. Photoluminescence spectrum measurements demonstrated that the DNMG can successfully design fluorescent molecules with 75% accuracy (n = 6/8) and create an unreported molecule that emits fluorescence detectable by the naked eye.