Chemical Problems Research Articles

Richard Lerner's Bioinspiration: Biomolecular Visualization and Modeling at Scripps Research

AbstractIn thinking back on Richard Lerner's impact on our lab's trajectory at Scripps Research, we see his fingerprints on all the efforts we have undertaken over the last 40 years. Immunology and virology have been a continuing driving focus in our software development and application within the Molecular Graphics Lab (now the Center for Computational Structural Biology – CCSB), spurred on by the marriage of chemistry with biology that Richard enabled at Scripps. Many of our earliest and continuing computational docking targets have been focused on HIV with collaborations with Chi‐Huey Wong and Barry Sharpless. Along with subsequent computational work with Jeff Kelly, Reza Ghadiri, Udi Keinan and Ben Cravatt we have been able to sustain continuous efforts in developing and applying docking and other computational technologies to structural chemistry problems. In this remembrance, we give our reflections of Richard Lerner's character, vision, and his inspiration in our scientific lives and in the environment that he created at Scripps Research.

Solving Graph Problems Using Gaussian Boson Sampling.

Gaussian boson sampling (GBS) is not only a feasible protocol for demonstrating quantum computational advantage, but also mathematically associated with certain graph-related and quantum chemistry problems. In particular, it is proposed that the generated samples from the GBS could be harnessed to enhance the classical stochastic algorithms in searching some graph features. Here, we use Jiǔzhāng, a noisy intermediate-scale quantum computer, to solve graph problems. The samples are generated from a 144-mode fully connected photonic processor, with photon click up to 80 in the quantum computational advantage regime. We investigate the open question of whether the GBS enhancement over the classical stochastic algorithms persists-and how it scales-with an increasing system size on noisy quantum devices in the computationally interesting regime. We experimentally observe the presence of GBS enhancement with a large photon-click number and a robustness of the enhancement under certain noise. Our work is a step toward testing real-world problems using the existing noisy intermediate-scale quantum computers and hopes to stimulate the development of more efficient classical and quantum-inspired algorithms.

Epistemological and Didactic Difficulties of Teaching Chemistry in Moroccan High Schools

This article focuses on the difficulties faced by actors in the process of teaching and learning chemistry, in particular the concepts of redox and acid-base. As to methodology, two tests were developed to conduct two empirical studies. The first test to identify the conceptual difficulties encountered by these actors when teaching the concepts above. The second test examines the skills mobilized by in-service and preservice teachers at the training center to solve daily life situations involving knowledge covered in the high school program such as electronegativity, polarity of a chemical bond, oxidation state of a chemical element, Lewis structure, rules or laws allowing the prediction of a spontaneous transformation, microscopic modeling of a chemical transformation and the classification of the different ox/red pairs, based on standard potentials. The results of these two studies reveal that preservice and in-service teachers have epistemological deficiency related to the knowledge to be taught and are unable to mobilize the appropriate knowledge to solve practical chemistry problems. Consequently, they cannot mentor learners to acquire the skills assigned in the chemistry curriculum: in particular, solving the problems providing from real and daily life. Individual interviews allowed us to better explain the dysfunctions causing these difficulties.
 
 Received: 4 February 2023 / Accepted: 20 April 2023 / Published: 5 May 2023

Advancements in hydrogen energy research with the assistance of computational chemistry

Hydrogen and electricity are the leading energy sources for most applications in the near future. With the rapid development of computers and computational chemistry methods, the research in hydrogen production, infrastructure materials, storage, and utilization more than ever benefits from close interactions with computational chemistry. The present review aims at presenting the modern state of hydrogen energy chemical research in all processes of hydrogen economy. Directions of further research efforts are given in the context of chemometrics and computational chemistry advancements. Problems of computational chemistry that hinder rapid progress are given and their possible solutions are outlined. It is expected that new artificial neural network (ANN) algorithms will play a decisive role in the creation of dependable computational chemistry methods and their application for hydrogen infrastructure, production, storage, and consumption.

Chemistry Learning Media Based on Social Media: Students’ View

Chemistry learning should be able to integrate contextual chemistry problems and present the sophistication of 21st-century learning technology. Social media, as a product of 21st-century technology, has the potential to be used as chemistry learning media. Therefore, this study aims to investigate students’ views toward the potential use of social media as chemistry learning media. The survey method involved 791 students. A total of 40 question items were developed and distributed online using a Google form to respondents. Based on the results of a survey conducted, it is known that respondents are accustomed to using Instagram and TikTok as the most frequently used social media. Other results reveal that most respondents have the same screen time, more than 4 hours daily. This screen time is used by respondents for entertainment and looking for news information on social media. With the length of screen time not used for this learning process, it can be a promising opportunity to utilize social media as a medium for learning chemistry. This study is expected to be a reference for developing social media-based chemistry learning to increase student motivation and achievement.

Phosphorescent O2-Probes Based on Ir(III) Complexes for Bioimaging Applications

The design, synthesis, and investigation of new molecular oxygen probes for bioimaging, based on phosphorescent transition metal complexes are among the topical problems of modern chemistry and advanced bioimaging. Three new iridium [Ir(N^C)2(N^N)]+ complexes with cyclometallating 4-(pyridin-2-yl)-benzoic acid derivatives and different di-imine chelate ligands have been synthesized and characterized by mass spectrometry and NMR spectroscopy. The periphery of these complexes is decorated with three relatively small “double-tail” oligo(ethylene glycol) fragments. All these complexes exhibit phosphorescence; their photophysical properties have been thoroughly studied, and quantum chemical calculations of their photophysical properties were also performed. It turned out that the changes in the nature of the di-imine ligand greatly affected the character of the electronic transitions responsible for their emission. Two complexes in this series show the desired photophysical characteristics; they demonstrate appreciable quantum yield (14–15% in degassed aqueous solutions) and a strong response to the changes in oxygen concentration, ca. three-fold increase in emission intensity, and an excited state lifetime upon deaeration of the aqueous solution. The study of their photophysical properties in model biological systems (buffer solutions containing fetal bovine serum—FBS) and cytotoxicity assays (MTT) showed that these complexes satisfy the requirements for application in bioimaging experiments. It was found that these molecular probes are internalized into cultured cancer cells and localized mainly in mitochondria and lysosomes. Phosphorescent lifetime imaging (PLIM) experiments showed that under hypoxic conditions in cells, a 1.5-fold increase in the excitation state lifetime was observed compared to aerated cells, suggesting the applicability of these complexes for the analysis of hypoxia in biological objects.

Free and Open Source Codes for Computational Chemistry Research Initiation: A Conspectus

Computational chemistry is a field of current active research in science that can supplement and complement the regular experimental methods and validate or propose new theoretical formulations. It has contributed from basic to advanced levels of investigation in a synergistic manner. The mathematical experiments can be pursued with minimal resources and are sustainable making it a good approach for institutions with limited resources. The freely available computer programs that are suitable for solving various types of problems in chemistry and interdisciplinary areas are briefly mentioned. The general features and specificity of some selected codes that are adaptable for undergraduate and graduate-level courses and research in Nepal are highlighted. A recommendation is made for the incorporation of adequate theoretical courses and parallel laboratory sessions in the existing syllabus for obtaining a trained and skilled workforce qualified for national and global careers in computational chemistry and also for research and development in Nepal.

Highly efficient, singularly P-stable, and low-cost phase-fitting two-step method of 14th order for problems in chemistry

Highly efficient, singularly P-stable, and low-cost phase-fitting two-step method of 14th order for problems in chemistry

Deep-neural-network approach to solving the ab initio nuclear structure problem

Predicting the structure of quantum many-body systems from the first principles of quantum mechanics is a common challenge in physics, chemistry, and material science. Deep machine learning has proven to be a powerful tool for solving condensed matter and chemistry problems, while for atomic nuclei it is still quite challenging because of the complicated nucleon-nucleon interactions, which strongly couple the spatial, spin, and isospin degrees of freedom. By combining essential physics of the nuclear wave functions and the strong expressive power of artificial neural networks, we develop FeynmanNet, a deep-learning variational quantum Monte Carlo approach for ab initio nuclear structure. We show that FeynmanNet can provide very accurate solutions of ground-state energies and wave functions for $^{4}\mathrm{He}, ^{6}\mathrm{Li}$, and even up to $^{16}\mathrm{O}$ as emerging from the leading-order and next-to-leading-order Hamiltonians of pionless effective field theory. Compared to the conventional diffusion Monte Carlo approaches, which suffer from the severe inherent fermion-sign problem, FeynmanNet reaches such a high accuracy in a variational way and scales polynomially with the number of nucleons. Therefore, it paves the way to a highly accurate and efficient ab initio method for predicting nuclear properties based on the realistic interactions between nucleons.

Finding Singular Points of the Titration Curve by Solving the Equation of State of the Electrolyte Solution

Electrolyte solutions consisting of acids, bases, and their salts have one or more singular points. These special points include the equivalence points of the titration curves and the coordinates (composition) of the maximum buffer capacity of the buffer solution. Knowledge of these points is necessary for solving problems of analytical and preparative chemistry. This paper describes a method for finding singular points of an electrolyte solution by solving the general equation of state derived in our previous work.

Predicting Kinetics and Dynamics of Spin-Dependent Processes

ConspectusPredicting mechanisms and rates of nonadiabatic spin-dependent processes including photoinduced intersystem crossings, thermally activated spin-forbidden reactions, and spin crossovers in metal centers is a very active field of research. These processes play critical roles in transition-metal-based and metalloenzymatic catalysis, molecular magnets, light-harvesting materials, organic light-emitting diodes, photosensitizers for photodynamic therapy, and many other applications. Therefore, accurate modeling of spin-dependent processes in complex systems and on different time scales is important for many problems in chemistry, biochemistry, and materials sciences.Nonadiabatic statistical theory (NAST) and nonadiabatic molecular dynamics (NAMD) are two complementary approaches to modeling the kinetics and dynamics of spin-dependent processes. NAST predicts the probabilities and rate constants of nonradiative transitions between electronic states with different spin multiplicities using molecular properties at only few critical points on the potential energy surfaces (PESs), including the reactant minimum and the minimum energy crossing point (MECP) between two spin states. This makes it possible to obtain molecular properties for NAST calculations using accurate but often computationally expensive electronic structure methods, which is critical for predicting the rate constants of spin-dependent processes. Alternatively, NAST can be used to study spin-dependent processes in very large complex molecular systems using less computationally expensive electronic structure methods. The nuclear quantum effects, such as zero-point vibrational energy, tunneling, and interference between reaction paths can be easily incorporated. However, the statistical and local nature of NAST makes it more suitable for large systems and slow kinetics. In contrast, NAMD explores entire PESs of interacting electronic states, making it ideal for modeling fast barrierless spin-dependent processes. Because the knowledge of large portions of PESs is often needed, the simulations require a very large number of electronic structure calculations, which limits the NAMD applicability to relatively small molecular systems and ultrafast kinetics.In this Account, we discuss our contribution to the development of the NAST and NAMD approaches for predicting the rates and mechanism of spin-dependent processes. First, we briefly describe our NAST and NAMD implementations. The NAST implementation is an extension of the transition state theory to the processes involving two crossing potential energy surfaces of different spin multiplicities. The NAMD approach includes the trajectory surface hopping (TSH) and ab initio multiple spawning (AIMS) methods. Second, we discuss several applications of NAST and NAMD to model spin-dependent processes in different systems. The NAST applicability to large complex systems is demonstrated by the studies of the spin-forbidden isomerization of the active sites of metal-sulfur proteins. Our implementation of the MECP search algorithm within the fully ab initio fragment molecular orbital method allows applying NAST to systems with thousands of atoms, such as the solvated protein rubredoxin. Applications of NAMD to ultrafast spin-dependent processes are represented by the generalized AIMS simulations utilizing the fast GPU-based TeraChem electronic structure program to gain insight into the complex photoexcited state relaxation in 2-cyclopentenone.

Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on 13C NMR Spectra and Prior Knowledge.

Structure elucidation of unknown compounds based on nuclear magnetic resonance (NMR) remains a challenging problem in both synthetic organic and natural product chemistry. Library matching has been an efficient method to assist structure elucidation. However, it is limited by the coverage of libraries. In addition, prior knowledge such as molecular fragments is neglected. To solve the problem, we propose a conditional molecular generation net (CMGNet) to allow input of multiple sources of information. CMGNet not only uses 13C NMR spectrum data as input but molecular formulas and fragments of molecules are also employed as input conditions. Our model applies large-scale pretraining for molecular understanding and fine-tuning on two NMR spectral data sets of different granularity levels to accommodate structure elucidation tasks. CMGNet generates structures based on 13C NMR data, molecular formula, and fragment information, with a recovery rate of 94.17% in the top 10 recommendations. In addition, the generative model performed well in the generation of various classes of compounds and in the structural revision task. CMGNet has a deep understanding of molecular connectivities from 13C NMR, molecular formula, and fragments, paving the way for a new paradigm of deep learning-assisted inverse problem-solving.

Phase-fitting, singularly P-stable, cost-effective two-step approach to solving problems in quantum chemistry with vanishing phase-lag derivatives up to order 6

Phase-fitting, singularly P-stable, cost-effective two-step approach to solving problems in quantum chemistry with vanishing phase-lag derivatives up to order 6

A Real Neural Network State for Quantum Chemistry

The restricted Boltzmann machine (RBM) has recently been demonstrated as a useful tool to solve the quantum many-body problems. In this work we propose tanh-FCN, which is a single-layer fully connected neural network adapted from RBM, to study ab initio quantum chemistry problems. Our contribution is two-fold: (1) our neural network only uses real numbers to represent the real electronic wave function, while we obtain comparable precision to RBM for various prototypical molecules; (2) we show that the knowledge of the Hartree-Fock reference state can be used to systematically accelerate the convergence of the variational Monte Carlo algorithm as well as to increase the precision of the final energy.

Selecting High-Dimensional Representations of Physical Systems by Reweighted Diffusion Maps.

Constructing reduced representations of high-dimensional systems is a fundamental problem in physical chemistry. Many unsupervised machine learning methods can automatically find such low-dimensional representations. However, an often overlooked problem is what high-dimensional representation should be used to describe systems before dimensionality reduction. Here, we address this issue using a recently developed method called the reweighted diffusion map [J. Chem. Theory Comput. 2022, 18, 7179-7192]. We show how high-dimensional representations can be quantitatively selected by exploring the spectral decomposition of Markov transition matrices built from data obtained from standard or enhanced sampling atomistic simulations. We demonstrate the performance of the method in several high-dimensional examples.

Performance comparison of optimization methods on variational quantum algorithms

Variational quantum algorithms (VQAs) offer a promising path toward using near-term quantum hardware for applications in academic and industrial research. These algorithms aim to find approximate solutions to quantum problems by optimizing a parametrized quantum circuit using a classical optimization algorithm. A successful VQA requires fast and reliable classical optimization algorithms. Understanding and optimizing how off-the-shelf optimization methods perform in this context is important for the future of the field. In this work, we study the performance of four commonly used gradient-free optimization methods: SLSQP, COBYLA, CMA-ES, and SPSA, at finding ground-state energies of a range of small chemistry and material science problems. We test a telescoping sampling scheme (where the accuracy of the cost-function estimate provided to the optimizer is increased as the optimization converges) on all methods, demonstrating mixed results across our range of optimizers and problems chosen. We further hyperparameter tune two of the four optimizers (CMA-ES and SPSA) across a large range of models and demonstrate that with appropriate hyperparameter tuning, CMA-ES is competitive with and sometimes outperforms SPSA (which is not observed in the absence of hyperparameter tuning). Finally, we investigate the ability of an optimizer to beat the `sampling noise floor' given by the sampling noise on each cost-function estimate provided to the optimizer. Our results demonstrate the necessity for tailoring and hyperparameter-tuning known optimization techniques for inherently-noisy variational quantum algorithms and that the variational landscape that one finds in a VQA is highly problem- and system-dependent. This provides guidance for future implementations of these algorithms in the experiment.

SYNTHESIS AND PROPERTIES OF POLYMER SUSPENSIONS OBTAINED BY HETEROPHASE POLYMERIZATION IN THE PRESENCE OF α,ω BIS[3 GLYCIDOXYPROPYL]POLYDIMETHYLSILOXANE

Polymeric microspheres with glycidoxy groups on the surface can be used as bioligand carriers in diagnostic test systems. However, the possibility of synthesizing aggregatively stable functional polymeric microspheres in a wide range of diameters is an urgent problem of modern polymer chemistry. This article is devoted to a systematic study of the kinetics of styrene and methyl methacrylate polymerization in the presence of an organosiloxane surfactant with terminal glycidoxy groups – α,ω-bis[3-glycidoxyproopyl]-polydimethylsiloxane, PDMS(CHOCH2). The article shows the influence of phases volume ratio, initiator and surfactant concentration, synthesis temperature and pH of initial medium on the properties of polymer suspensions (their aggregative stability, monodispersity, particle diameter). The formation of polymer-monomer particles during polymerization has been studied and it has been shown that polymer suspensions retain high aggregation stability from early period of monomer conversion, and the particle diameter changes untill 30% of the monomer conversion. Due to the high surface activity of the organosiloxane surfactant used and its incompatibility with the resulting polymer, aggregatively stable functional polymer suspensions with diameters from 0.09 to 1.1 μm were obtained by a single-stage synthesis. Studies of the hydration degree of polymer microspheres have shown that the presence of functional glycidoxy groups on their surface ensures the formation of a hydration shell, which increases the stability of polymer particles, and also makes it possible to use the resulting microspheres as carriers of bioligands. Thus, the use of water-insoluble organosiloxane surfactants as stabilizers for the heterophase polymerization of vinyl monomers makes it possible to synthesize aggregatively stable functional polymer suspensions in a wide range of diameters. The use of organosiloxane surfactants with various functional groups expands the range of functional polymer microspheres used in various fields of application.

Bio-Inspired Iron Pentadentate Complexes as Dioxygen Activators in the Oxidation of Cyclohexene and Limonene.

The use of dioxygen as an oxidant in fine chemicals production is an emerging problem in chemistry for environmental and economical reasons. In acetonitrile, the [(N4Py)FeII]2+ complex, [N4Py-N,N-bis(2-pyridylmethyl)-N-(bis-2-pyridylmethyl)amine] in the presence of the substrate activates dioxygen for the oxygenation of cyclohexene and limonene. Cyclohexane is oxidized mainly to 2-cyclohexen-1-one, and 2-cyclohexen-1-ol, cyclohexene oxide is formed in much smaller amounts. Limonene gives as the main products limonene oxide, carvone, and carveol. Perillaldehyde and perillyl alcohol are also present in the products but to a lesser extent. The investigated system is twice as efficient as the [(bpy)2FeII]2+/O2/cyclohexene system and comparable to the [(bpy)2MnII]2+/O2/limonene system. Using cyclic voltammetry, it has been shown that, when the catalyst, dioxgen, and substrate are present simultaneously in the reaction mixture, the iron(IV) oxo adduct [(N4Py)FeIV=O]2+ is formed, which is the oxidative species. This observation is supported by DFT calculations.

Экологическая химия и токсикология окружающей среды

This publication is a response to a book on the problems of environmental chemistry and toxicology of the environment, including the aquatic environment of ecotoxicology, which are relevant, and their relevance increases over time.

Molecular Energy Landscapes of Hardware-Efficient Ansätze in Quantum Computing.

Rapid advances in quantum computing have opened up new opportunities for solving the central electronic structure problem in computational chemistry. In the noisy intermediate-scale quantum (NISQ) era, where qubit coherence times are limited, it is essential to exploit quantum algorithms with sufficiently short quantum circuits to maximize qubit efficiency. The procedural construction of hardware-efficient ansätze provides one approach to design such circuits. However, refining the accuracy of the global minimum by increasing circuit depth may lead to a proliferation of local minima that hinders global optimization. To investigate this phenomenon, we explore the energy landscapes of hardware-efficient circuits to identify ground-state energies of the hydrogen, lithium hydride, and beryllium hydride molecules. We also propose a simple dimensionality reduction procedure that reduces quantum gate depth while retaining high accuracy for the global minimum, simplifying the energy landscape, and hence speeding up optimization from both software and hardware perspectives.