Revealing factors influencing polymer degradation with rank-based machine learning.

The efficient treatment of polymer waste is a major challenge for marine sustainability. It is useful to reveal the factors that dominate the degradability of polymer materials for developing polymer materials in the future. The small number of available datasets on degradability and the diversity of their experimental means and conditions hinder large-scale analysis. In this study, we have developed a platform for evaluating the degradability of polymers that is suitable for such data, using a rank-based machine learning technique based on RankSVM. We then made a ranking model to evaluate the degradability of polymers, integrating three datasets on the degradability of polymers that are measured by different means and conditions. Analysis of this ranking model with a decision tree revealed factors that dominate the degradability of polymers.

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.

Selective Detection of Toxic C1 Chemicals Using a Hydroxylamine-Based Chemiresistive Sensor Array.

Formaldehyde (FA) is a deleterious C1 pollutant commonly found in the interiors of modern buildings. C1 chemicals are generally more toxic than the corresponding C2 chemicals, but the selective discrimination of C1 and C2 chemicals using simple sensory systems is usually challenging. Here, we report the selective detection of FA vapor using a chemiresistive sensor array composed of modified hydroxylamine salts (MHAs, ArCH2ONH2·HCl) and single-walled carbon nanotubes (SWCNT). By screening 32 types of MHAs, we have identified an ideal sensor array that exhibits a characteristic response pattern for FA. Thus, trace FA (0.02-0.05 ppm in air) can be clearly discriminated from the corresponding C2 chemical, acetaldehyde (AA). This system has been extended to discriminate methanol (C1) from ethanol (C2) in combination with the catalytic conversion of these alcohols to their corresponding aldehydes. Our system offers portable and reliable chemical sensors that discriminate the subtle differences between C1 and C2 chemicals, enabling advanced environmental monitoring and healthcare applications.

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 (Ka) 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.

Automatic Differentiation for the Direct Minimization Approach to the Hartree-Fock Method.

Automatic differentiation (AD) has become an important tool for optimization problems in computational science, and it has been applied to the Hartree-Fock method. Although the reverse-mode AD is more efficient than the forward-mode, eigenvalue calculation in the self-consistent field (SCF) method has impeded the use of the reverse-mode AD. Here, we propose a method to directly minimize Hartree-Fock energy under the orthonormality constraint of the molecular orbitals using reverse-mode AD by avoiding eigenvalue calculation. According to our validation, the proposed method was more stable than the conventional SCF method and achieved comparable accuracy.

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.

Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring.

Recently, artificial intelligence (AI)-enabled de novo molecular generators (DNMGs) have automated molecular design based on data-driven or simulation-based property estimates. In some domains like the game of Go where AI surpassed human intelligence, humans are trying to learn from AI about the best strategy of the game. To understand DNMG's strategy of molecule optimization, we propose an algorithm called characteristic functional group monitoring (CFGM). Given a time series of generated molecules, CFGM monitors statistically enriched functional groups in comparison to the training data. In the task of absorption wavelength maximization of pure organic molecules (consisting of H, C, N, and O), we successfully identified a strategic change from diketone and aniline derivatives to quinone derivatives. In addition, CFGM led us to a hypothesis that 1,2-quinone is an unconventional chromophore, which was verified with chemical synthesis. This study shows the possibility that human experts can learn from DNMGs to expand their ability to discover functional molecules.

High reactivity of H2O vapor on GaN surfaces.

Understanding the process of oxidation on the surface of GaN is important for improving metal-oxide-semiconductor (MOS) devices. Real-time X-ray photoelectron spectroscopy was used to observe the dynamic adsorption behavior of GaN surfaces upon irradiation of H2O, O2, N2O, and NO gases. It was found that H2O vapor has the highest reactivity on the surface despite its lower oxidation power. The adsorption behavior of H2O was explained by the density functional molecular dynamic calculation including the spin state of the surfaces. Two types of adsorbed H2O molecules were present on the (0001) (+c) surface: non-dissociatively adsorbed H2O (physisorption), and dissociatively adsorbed H2O (chemisorption) molecules that were dissociated with OH and H adsorbed on Ga atoms. H2O molecules attacked the back side of three-fold Ga atoms on the (0001̅) (-c) GaN surface, and the bond length between the Ga and N was broken. The chemisorption on the (101̅0) m-plane of GaN, which is the channel of a trench-type GaN MOS power transistor, was dominant, and a stable Ga-O bond was formed due to the elongated bond length of Ga on the surface. In the atomic layer deposition process of the Al2O3 layer using H2O vapor, the reactions caused at the interface were more remarkable for p-GaN. If unintentional oxidation can be resulted in the generation of the defects at the MOS interface, these results suggest that oxidant gases other than H2O and O2 should be used to avoid uncontrollable oxidation on GaN surfaces.

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.

Integrating Incompatible Assay Data Sets with Deep Preference Learning.

A large amount of bioactivity assay data is already accumulated in public databases, but the integration of these data sets for quantitative structure-activity relationship (QSAR) studies is not straightforward due to differences in experimental methods and settings. We present an efficient deep-learning-based approach called Deep Preference Data Integration (DPDI). For integrating outcome variables of different assay types, a surrogate variable is introduced, and a neural network is trained such that the total order induced by the surrogate variable is maximally consistent with given data sets. In a task of predicting efficacy of factor Xa inhibitors, DPDI successfully integrated 2959 molecules distributed in 129 assay data sets. In most of our experiments, data integration improved prediction accuracy strongly in interpolation and extrapolation tasks, indicating that DPDI is an effective tool for QSAR studies.

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

In order to accurately understand and estimate molecular properties, finding energetically favorable molecular conformations is the most fundamental task for atomistic computational research on molecules and materials. Geometry optimization based on quantum chemical calculations has enabled the conformation prediction of arbitrary molecules, including de novo ones. However, it is computationally expensive to perform geometry optimizations for enormous conformers. In this study, we introduce the gray-box optimization (GBO) framework, which enables optimal control over the entire geometry optimization process, among multiple conformers. Algorithms designed for GBO roughly estimate energetically preferable conformers during their geometry optimization iterations. They then preferentially compute promising conformers. To evaluate the performance of the GBO framework, we applied it to a test set consisting of seven dipeptides and mycophenolic acid to determine their stable conformations at the density functional theory level. We thus preferentially obtained energetically favorable conformations. Furthermore, the computational costs required to find the most stable conformation were significantly reduced (approximately 1% on average, compared to the naive approach for the dipeptides).

Unique Photophysical Properties of 1,8-Naphthalimide Derivatives: Generation of Semi-stable Radical Anion Species by Photo-Induced Electron Transfer from a Carboxy Group.

The development of anion sensors for selective detection of a specific anion is a crucial research topic. We previously reported a selective photo-induced colorimetric reaction of 1-methyl-3-(N-(1,8-naphthalimidyl)ethyl)imidazolium (MNEI) having a cationic receptor in the presence of molecules having multiple carboxy groups, such as succinate, citrate, and polyacrylate. However, the mechanism underlying this reaction was not clarified. Here, we investigate the photo-induced colorimetric reaction of N-[2-(trimethylammonium)ethyl]-1,8-naphthalimide (TENI), which has a different cationic receptor from MNEI and undergoes the photo-induced colorimetric reaction, and its analogues to clarify the reaction mechanism. The TENI analogues having substituents on the naphthalene ring provide important evidence, suggesting that the colorimetric chemical species were radical anions generated via photo-induced electron transfer from carboxylate to the naphthalimide derivative. The generation of the naphthalimide-based radical anion is verified by 1H NMR and cyclic voltammetry analyses, and photo-reduction of methylene blue is mediated by TENI. In addition, the role of the cationic receptor for the photo-induced colorimetric reaction is investigated with TENI analogues having different hydrophilic groups instead of the trimethylammonium group. Interestingly, the photo-induced colorimetric reaction is observed in a nonionic analogue having a polyethylene glycol group, indicating that the colorimetric reaction does not require a cationic receptor. On the other hand, we reveal that the trimethylammonium group stabilizes the radical anion species. These generation and stabilization phenomena of naphthalimide-based radical anion species will contribute to the development of sophisticated detection systems specific for carboxylate.

Black-Box Optimization for Automated Discovery.

In chemistry and materials science, researchers and engineers discover, design, and optimize chemical compounds or materials with their professional knowledge and techniques. At the highest level of abstraction, this process is formulated as black-box optimization. For instance, the trial-and-error process of synthesizing various molecules for better material properties can be regarded as optimizing a black-box function describing the relation between a chemical formula and its properties. Various black-box optimization algorithms have been developed in the machine learning and statistics communities. Recently, a number of researchers have reported successful applications of such algorithms to chemistry. They include the design of photofunctional molecules and medical drugs, optimization of thermal emission materials and high Li-ion conductive solid electrolytes, and discovery of a new phase in inorganic thin films for solar cells.There are a wide variety of algorithms available for black-box optimization, such as Bayesian optimization, reinforcement learning, and active learning. Practitioners need to select an appropriate algorithm or, in some cases, develop novel algorithms to meet their demands. It is also necessary to determine how to best combine machine learning techniques with quantum mechanics- and molecular mechanics-based simulations, and experiments. In this Account, we give an overview of recent studies regarding automated discovery, design, and optimization based on black-box optimization. The Account covers the following algorithms: Bayesian optimization to optimize the chemical or physical properties, an optimization method using a quantum annealer, best-arm identification, gray-box optimization, and reinforcement learning. In addition, we introduce active learning and boundless objective-free exploration, which may not fall into the category of black-box optimization.Data quality and quantity are key for the success of these automated discovery techniques. As laboratory automation and robotics are put forward, automated discovery algorithms would be able to match human performance at least in some domains in the near future.

NMR-TS: de novo molecule identification from NMR spectra.

Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS.

Enantiomeric Excess Dependent Splitting of NMR Signal through Dynamic Chiral Inversion and Coligand Exchange in a Coordination Complex.

Nuclear magnetic resonance (NMR) spectroscopy cannot be used to discriminate enantiomers, and NMR resonances of enantiomeric mixtures are generally not affected by enantiomeric excess (ee). Here, we report that a coordination complex (L·2Zn·3C), where L is a salen-like prochiral ligand and C is an exchangeable acetate coligand, exhibits symmetrical splitting of one of the 1H NMR resonances of L with the degree of splitting linearly proportional to ee of the chiral guest coligand C, 2-phenoxypropionic acid. Despite the well-defined chirality in the crystal structure of L·2Zn·3C, concurrent fast chiral inversion and coligand exchange in solution renders L·2Zn·3C the primary example of prochiral solvating agent (pro-CSA) based on a coordination complex. Notably, the NMR resonances remain split even in dilute solution due to the lack of chiral guest dissociation in the coligand exchange system. This work provides new insights into chiral transfer events in metal-ligand complexes.

Pushing property limits in materials discovery via boundless objective-free exploration.

Materials chemists develop chemical compounds to meet often conflicting demands of industrial applications. This process may not be properly modeled by black-box optimization because the target property is not well defined in some cases. Herein, we propose a new algorithm for automated materials discovery called BoundLess Objective-free eXploration (BLOX) that uses a novel criterion based on kernel-based Stein discrepancy in the property space. Unlike other objective-free exploration methods, a boundary for the materials properties is not needed; hence, BLOX is suitable for open-ended scientific endeavors. We demonstrate the effectiveness of BLOX by finding light-absorbing molecules from a drug database. Our goal is to minimize the number of density functional theory calculations required to discover out-of-trend compounds in the intensity-wavelength property space. Using absorption spectroscopy, we experimentally verified that eight compounds identified as outstanding exhibit the expected optical properties. Our results show that BLOX is useful for chemical repurposing, and we expect this search method to have numerous applications in various scientific disciplines.

Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies.

This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using a molecule generator and a DFT simulator, and attempted to generate novel photofunctional molecules whose lowest excited states lie at desired energetic levels. A 10 day run on the 12-core server discovered 86 potential photofunctional molecules around target lowest excitation levels, designated as 200, 300, 400, 500, and 600 nm. Among the molecules discovered, six were synthesized, and five were confirmed to reproduce DFT predictions in ultraviolet visible absorption measurements. This result shows the potential of AI-assisted chemistry to discover ready-to-synthesize novel molecules with modest computational resources.

Theoretical study on photo-induced processes of 1-methyl-3-(N-(1,8-naphthalimidyl)ethyl)imidazolium halide species: an application of constrained density functional theory.

1-Methyl-3-(N-(1,8-naphthalimidyl)ethyl)imidazolium (MNEI) has potential as a versatile sensor that can measure the electronegativity of anions based on the fluorescence intensity upon irradiation. To clarify the factors that determine the fluorescence intensity, constrained density functional theory (CDFT) was applied to explore the electron transfer (ET) states of MNEI halide species (MNEI-X; X = F, Cl, Br, I). According to the CDFT potential energy surface, intra-molecular ET (SM1) states on MNEI are responsible for the intensity of absorption and fluorescence spectra. However, inter-molecular ET (SET) states between MNEI and X are certainly responsible for fluorescence quenching. Hence, the energetic difference between the SM1 state and the SET state (ΔEM1_ET) is a crucial factor that determines the fluorescence intensity in the spectra of MNEI-X complexes. ΔEM1_ET decreases as the electronegativity of X decreases (i.e., F > Cl > Br > I). This explains the fluorescence intensity of MNEI-X.

Manipulation of fullerene superstructures by complexing with polycyclic aromatic compounds.

Polycyclic aromatic compounds (naphthalene, anthracene and pyrene) have been intercalated into the superstructures of fullerene nanowhiskers, using a facile liquid-liquid interfacial precipitation (LLIP) method. Due to the interaction between polycyclic molecules and fullerene, the growth of fullerene crystals was interfered in comparison to the fullerene crystal growth without the polycyclic molecules, resulting in the formation of fullerene superstructures with various nanofeatures. Moreover, the fluorescence emissions of the fullerene superstructures were significantly changed due to the intercalation of the polycyclic molecules, implying the influence of molecular packing on the electron transfer within the nanostructures. These results may bring new insights on the control of fullerene nanostructures and to manipulate their optical properties in optoelectronic devices.

Wood-mimetic skins prepared using horseradish peroxidase catalysis to induce surface wrinkling of chitosan film upon drying.

We previously developed bio-based wrinkled surfaces induced by wood-mimetic skins upon drying in which microscopic wrinkles were fabricated on a chitosan (CS) film by immersing it in a phenolic acid solution, followed by horseradish peroxidase (HRP)-catalyzed surface reaction and drying. However, the detailed structure of the resulting wood-mimetic skins, including crosslinking mode and thickness, has not been clarified due to the difficulty of the analysis. Here, we prepare wrinkled films using ferulic acid (FE), vanillic acid (VA), and homovanillic acid (HO) and characterize their structures to clarify the unknown characteristics of wood-mimetic skin. Chemical and structural analyses of wood-mimetic skins prepared using VA and HO indicate that the crosslinking structure in the skin is composed of ionic bonds between CS and an oligophenolic residue generated by the HRP-catalyzed reaction on the CS surface. Moreover, the quantity of these ionic bonds is related to the skin hardness and wrinkle size. Finally, SEM and TOF-SIMS analyses indicate that the skin thickness is on the submicron order (<200nm).