Chemical Problems Research Articles

Uncovering dissipation from coarse observables: A case study of a random walk with unobserved internal states.

Inferring underlying microscopic dynamics from low-dimensional experimental signals is a central problem in physics, chemistry, and biology. As a trade-off between molecular complexity and the low-dimensional nature of experimental data, mesoscopic descriptions such as the Markovian master equation are commonly used. The states in such descriptions usually include multiple microscopic states, and the ensuing coarse-grained dynamics are generally non-Markovian. It is frequently assumed that such dynamics can nevertheless be described as a Markov process because of the timescale separation between slow transitions from one observed coarse state to another and the fast interconversion within such states. Here, we use a simple model of a molecular motor with unobserved internal states to highlight that (1) dissipation estimated from the observed coarse dynamics may significantly underestimate microscopic dissipation even in the presence of timescale separation and even when mesoscopic states do not contain dissipative cycles and (2) timescale separation is not necessarily required for the Markov approximation to give the exact entropy production, provided that certain constraints on the microscopic rates are satisfied. When the Markov approximation is inadequate, we discuss whether including memory effects can improve the estimate. Surprisingly, when we do so in a "model-free" way by computing the Kullback-Leibler divergence between the observed probability distributions of forward trajectories and their time reverses, this leads to poorer estimates of entropy production. Finally, we argue that alternative approaches, such as hidden Markov models, may uncover the dissipative nature of the microscopic dynamics even when the observed coarse trajectories are completely time-reversible.

Antistaphylococcal Triazole-Based Molecular Hybrids: Design, Synthesis and Activity

Background: In the era of resistance, the design and search for new “small” molecules with a narrow spectrum of activity that target a protein or enzyme specific to a certain bacterium with high selectivity and minimal side effects remains an urgent problem of medicinal chemistry. In this regard, we developed and successfully implemented a strategy for the search for new hybrid molecules, namely, the not broadly known [2-(3-R-1H-[1,2,4]-triazol-5-yl)phenyl]amines. They can act as “building blocks” and allow for the introduction of certain structural motifs into the desired final products in order to enhance the antistaphylococcal effect. Methods: The “one-pot” synthesis of the latter is based on the conversion of substituted 4-hydrazinoquinazolines or substituted 2-aminobenzonitriles and carboxylic acid derivatives to the target products. The possible molecular mechanism of the synthesized compounds (DNA gyrase inhibitors) was investigated and discussed using molecular docking, and their further study for antistaphylococcal activity was substantiated. Results: A significant part of the obtained compounds showed high antibacterial activity against Staphylococcus aureus (MIC: 10.1–62.4 µM) and 5-bromo-2-(3-(furan-3-yl)-1H-1,2,4-triazol-5-yl)aniline and 5-fluoro-2-(3-(thiophen-3-yl)-1H-1,2,4-triazol-5-yl)aniline, with MICs of 5.2 and 6.1 µM, respectively, approaching the strength of the effect of the reference drug, “Ciprofloxacin” (MIC: 4.7 µM). The conducted SAR and ADME analyses confirm the prospects of the further structural modification of these compounds. The obtained [2-(3-R-1H-[1,2,4]-triazol-5-yl)phenyl]amines reveal significant antimicrobial activity and deserve further structural modification and detailed study as effective antistaphylococcal agents. The SAR analysis revealed that the presence of a cycloalkyl or electron-rich heterocyclic fragment in the third position of the triazole ring was essential for the antibacterial activity of the obtained compounds. At the same time, the introduction of a methyl group into the aniline moiety led to an enhancement of activity. The introduction of halogen into the aniline fragment has an ambiguous effect on the level of antistaphylococcal activity and depends on the nature of the substituent in the third position. Conclusions: Obtained [2-(3-R-1H-[1,2,4]-triazol-5-yl)phenyl]amines reveal significant antistaphylococcal activity and deserve for further detailed study as effective antibacterial agents.

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science.

Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.

Development of innovative electronic student worksheet (E-LKPD) based on somatic auditory visual intellectual (SAVI) on salt hydrolysis topic

A low understanding of salt hydrolysis and a lack of mastery of mathematics in the calculation section is a problem in learning chemistry. Therefore, this research aim is to produce a good electronic student worksheet (E-LKPD) based on SAVI learning model by determining the quality of LKPD in terms of (1) feasibility and (2) student response. This research and development (R&D) used a 4D development model that was modified to 3D. The research instruments used were interview and validation sheets, test items in the E-LKPD, and student response questionnaires. The research sample consisted of 10 students of class XI MIPA. The results showed that (1) the developed E-LKPD has good quality because it met valid criteria with a percentage of 90% in terms of material and 87% in terms of media. The average score of E-LKPD effectiveness is 78 (high criteria), and practically average score of E-LKPD based on students' response questionnaire is 94.4%; (2) students said that E-LKPD can help them learn and the material is exposed clearly (92.5%), can be used for independent study (97.5%), and the design of the E-LKPD is attractive (95%). Based on the research results, SAVI-based innovative E-LKPD can be used in chemistry learning on salt hydrolysis topics.

Graphene-Assisted Electron-Based Imaging of Individual Organic and Biological Macromolecules: Structure and Transient Dynamics.

Characterizing the structures, interactions, and dynamics of molecules in their native liquid state is a long-existing challenge in chemistry, molecular science, and biophysics with profound scientific significance. Advanced transmission electron microscopy (TEM)-based imaging techniques with the use of graphene emerged as promising tools, mainly due to their performance on spatial and temporal resolution. This review focuses on the various approaches to achieving high-resolution imaging of individual molecules and their transient interactions. We highlight the crucial role of graphene grids in cryogenic electron microscopy for achieving Ångstrom-level resolution for resolving molecular structures and the importance of graphene liquid cells in liquid-phase TEM for directly observing dynamics with subnanometer resolution at a frame rate of several frames per second, as well as the cross-talks of the two imaging modes. To understand the chemistry and physics encoded in these molecular movies, incorporating machine learning algorithms for image analysis provides a promising approach that further bolsters the resolution adventure. Besides reviewing the recent advances and methodologies in TEM imaging of individual molecules using graphene, this review also outlines future directions to improve these techniques and envision problems in molecular science, chemistry, and biology that could benefit from these experiments.

DEVELOPMENT AND IMPLEMENTATION OF PRODUCTION TECH-NOLOGIES FOR PRODUCING COMPOSITE MATERIALS AND TIRES BASED ON POLYURETHANE SYSTEMS FEATURES OF POLYURE-THANE RHEOLOGY

The objects of the study were thermal polyurethane thermoplastic elastomers (PUTP), obtained by the reaction of 4,4-diphenylmethane diisocyanate, hydroxide-containing polyester with a molecu-lar weight of 2000 (polyethylene glycol adipate) and 1,4-butadiol (industrial name VITUR). -T-1413) It was found that the dependence of the logarithm of the effective viscosity (ηeff) of the PUTEP melt on shear stress at various temperatures in the studied range of shear rates (from 1 to 100 s-1) represents parallel straight lines. This makes it possible to use the analytical dependence of the ef-fective viscosity on temperature to describe the rheological behavior of PUTEP melts, which also takes into account the influence of shear stress on the viscosity of the melt carbon-chain and heterochain polymers are determined by many factors, such as the thermody-namic properties of the components, their molecular weight, the degree of phase dispersion, the ability of the components to crystallize, etc. Composite materials, which are reinforced or filled polymers, play an important role in technology. The development of these materials and improvement of their properties is a complex problem, in which a significant place belongs to the physical chemistry of surface phenomena in polymers. Therefore, the problems of physical chemistry of filled polymers are problems of physical chemis-try of surface phenomena in polymers. Keywords: development, rheology, modification, polyurethane, technology, tires, vulcanization, extruder.

Entanglement and Mutual Information in Molecules: Comparing Localized and Delocalized Orbitals.

The use of the mutual information (MI) as a measure of the entanglement in quantum systems has gained a consensus in recent years, even if there is an ongoing effort to distinguish the classical and quantum contributions contained therein. This quantity has been first introduced in condensed matter physics, in particular, in studies based on the density matrix renormalization group method. This method has been successfully adapted to quantum chemistry problems, opening the way to compute MI also in molecular systems. A key aspect of this quantity is its dependence on the one-electron (orbital) basis set, even for wave functions that are invariant under unitary transformation of the orbitals. In this work, we investigate the role of the orbital basis set (delocalized or localized, following different strategies) for wave functions expressed as linear combinations of Slater determinants and we give the analytic expression for the MI for a few special cases. This study aims to improve the knowledge of the relationship between the characteristics of the chemical bond (considering a few paradigmatic molecules, H2, F2, N2, and short linear polyenes) and the properties of interest in the field of quantum information theory.

Unleashed from constrained optimization: quantum computing for quantum chemistry employing generator coordinate inspired method

Hybrid quantum-classical approaches offer potential solutions to quantum chemistry problems, yet they often manifest as constrained optimization problems. Here, we explore the interconnection between constrained optimization and generalized eigenvalue problems through the Unitary Coupled Cluster (UCC) excitation generators. Inspired by the generator coordinate method, we employ these UCC excitation generators to construct non-orthogonal, overcomplete many-body bases, projecting the system Hamiltonian into an effective Hamiltonian, which bypasses issues such as barren plateaus that heuristic numerical minimizers often encountered in standard variational quantum eigensolver (VQE). Diverging from conventional quantum subspace expansion methods, we introduce an adaptive scheme that robustly constructs the many-body basis sets from a pool of the UCC excitation generators. This scheme supports the development of a hierarchical ADAPT quantum-classical strategy, enabling a balanced interplay between subspace expansion and ansatz optimization to address complex, strongly correlated quantum chemical systems cost-effectively, setting the stage for more advanced quantum simulations in chemistry.

Stereospecific syn-dichlorination of allylic amines enabled by identification of a superior stereo-directing group.

Alteration of a well-established reaction mechanism for access to different molecular structures is an inherently intriguing research subject. In that context, syn-stereospecific alkene dihalogenation draws attention as a long-standing problem in synthetic organic chemistry. The simplest approach would be the incorporation of an additional stereo-inverting step within the traditional anti-dihalogenation process. Surprisingly, this seemingly trivial idea turned out challenging, and no suitable stereo-directing group was known before our work. Herein, we describe a highly efficient syn-dichlorination of N-protected allylic amines through the anchimeric assistant phenomenon that has been inapplicable to alkene dihalogenation. Upon rational identification of a superior stereo-director, 1,8-naphthalimide, our practical reaction conditions with mild and convenient dichlorinating reagents can accommodate the formerly unemployable aryl alkenes in excellent yields (>95%) and stereospecificity (>50:1). DFT calculation suggests a concerted internal trapping mechanism without a discrete carbocationic species, which accounts for the conservation of the stereochemical integrity.

From sight to insight – reflection processes in an eye-gaze-augmented retrospective

ABSTRACT Various one-size-fits-all methods have been developed to improve students’ use of representations for problem-solving. However, as the interplay of processing representations and problem-solving might be highly individual, a differentiated approach may be needed to foster students’ problem-solving with representations. A retrospective enhanced with eye-gaze replays and reflection prompts (eye-gaze-augmented retrospective) may stimulate students’ reflection on their problem-solving process by providing normally inaccessible information, allowing the students to look back and draw tailored consequences for future problem-solving situations. Therefore, an exploratory study was conducted where students solved chemistry problems and subsequently reflected on their problem-solving while watching their eye-gaze replays in three distinct reflection phases. The study aimed to investigate to what extent different phases of an eye-gaze-augmented retrospective stimulate students’ reflection on their problem-solving by characterizing the structural complexity of the reflection processes and considering the accuracy of students’ problem-solving. The results showed that reflection processes with varying structural complexity emerged. Higher complexity reflections emerged primarily in the phase in which the task solution was presented accompanied by reflection prompts, especially for inaccurately solved problems. The findings suggest that an eye-gaze-augmented retrospective could stimulate reflection on problem-solving with representations, but optimization is necessary to enhance each student’s reflection process effectively.

Lineup polytopes of products of simplices

Consider a real point configuration \bm{A} of size n and an integer r \leq n . The vertices of the r -lineup polytope of \bm{A} correspond to the possible orderings of the top r points of the configuration obtained by maximizing a linear functional. The motivation behind the study of lineup polytopes comes from the representability problem in quantum chemistry. In that context, the relevant point configurations are the vertices of hypersimplices and the integer points contained in an inflated regular simplex. The central problem consists in providing an inequality representation of lineup polytopes as efficiently as possible. In this article, we adapt the developed techniques to the quantum information theory setup. The appropriate point configurations become the vertices of products of simplices. A particular case is that of lineup polytopes of cubes, which form a type B analog of hypersimplices, where the symmetric group of type A naturally acts. To obtain the inequalities, we center our attention on the combinatorics and the symmetry of products of simplices to obtain an algorithmic solution. Along the way, we establish relationships between lineup polytopes of products of simplices with the Gale order, standard Young tableaux, and the resonance arrangement.

KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search

Quantum architecture Search (QAS) is a promising direction for optimization and automated design of quantum circuits towards quantum advantage. Recent techniques in QAS emphasize Multi-Layer Perceptron (MLP)-based deep Q-networks. However, their interpretability remains challenging due to the large number of learnable parameters and the complexities involved in selecting appropriate activation functions. In this work, to overcome these challenges, we utilize the Kolmogorov-Arnold Network (KAN) in the QAS algorithm, analyzing their efficiency in the task of quantum state preparation and quantum chemistry. In quantum state preparation, our results show that in a noiseless scenario, the probability of success is 2× to 5× higher than MLPs. In noisy environments, KAN outperforms MLPs in fidelity when approximating these states, showcasing its robustness against noise. In tackling quantum chemistry problems, we enhance the recently proposed QAS algorithm by integrating curriculum reinforcement learning with a KAN structure. This facilitates a more efficient design of parameterized quantum circuits by reducing the number of required 2-qubit gates and circuit depth. Further investigation reveals that KAN requires a significantly smaller number of learnable parameters compared to MLPs; however, the average time of executing each episode for KAN is higher.

Ultrastrong coupling limit to quantum mean force Gibbs state for anharmonic environment.

The equilibrium state of a quantum system can deviate from the Gibbs state if the system-environment (SE) coupling is not weak. An analytical expression for this mean force Gibbs state (MFGS) is known in the ultrastrong coupling (USC) regime for the Caldeira-Leggett (CL) model that assumes a harmonic environment. Here, we derive analytical expressions for the MFGS in the USC regime for more general SEmodels. For all the generalized models considered here, we find the USC state to be diagonal in the basis set by the SEinteraction, just like in the CL case. While for the generic model considered, the corresponding USC-MFGS is found to alter from the CL result, we do identify a class of models more general than the CL model for which the CL-USC result remains unchanged. We also provide numerical verification for our results. These results provide key tools for the study of strong coupling quantum thermodynamics and several quantum chemistry and biology problems under more realistic SEmodels, going beyond the CL model.

Arithmetic Proficiency of Pre-Service Science Teachers: An Empirical Study

Mastery of arithmetic operations is fundamental for students pursuing science education, as these skills are essential in solving complex problems in physics, chemistry, and biology. However, gaps in students' arithmetic proficiency can hinder their academic and professional development. This study aims to examine the arithmetic skills of pre-service science teachers in solving mathematical problems across six domains: addition, subtraction, multiplication, division, exponentiation, and mixed operations. A total of 37 short-answer questions were administered, and the results were analyzed by domain. The findings indicate that students demonstrate proficiency in basic operations involving whole numbers, particularly in addition and subtraction. However, challenges persist in the areas of fractions, decimals, and mixed operations, where accuracy rates were notably lower. These gaps in understanding may affect students' ability to apply mathematical concepts in their science courses and future teaching roles. The study's limitations include a focus on quantitative results without exploring the cognitive processes behind student errors. Future research should investigate intervention strategies to address these weaknesses, potentially through targeted instructional approaches or the use of technology to enhance learning outcomes

Qudit-based variational quantum eigensolver using photonic orbital angular momentum states.

Solving the electronic structure problem is a notorious challenge in quantum chemistry and material science. Variational quantum eigensolver (VQE) is a promising hybrid classical-quantum algorithm for finding the lowest-energy configuration of a molecular system. However, it typically requires many qubits and quantum gates with substantial quantum circuit depth to accurately represent the electronic wave function of complex structures. Here, we propose an alternative approach to solve the electronic structure problem using VQE with a single qudit. Our approach exploits a high-dimensional orbital angular momentum state of a heralded single photon and notably reduces the required quantum resources compared to conventional multi-qubit-based VQE. We experimentally demonstrate that our single-qudit-based VQE can efficiently estimate the ground state energy of hydrogen (H2) and lithium hydride (LiH) molecular systems corresponding to two- and four-qubit systems, respectively. We believe that our scheme opens a pathway to perform a large-scale quantum simulation for solving more complex problems in quantum chemistry and material science.

A circuit-generated quantum subspace algorithm for the variational quantum eigensolver.

Recent research has shown that wavefunction evolution in real and imaginary time can generate quantum subspaces with significant utility for obtaining accurate ground state energies. Inspired by these methods, we propose combining quantum subspace techniques with the variational quantum eigensolver (VQE). In our approach, the parameterized quantum circuit is divided into a series of smaller subcircuits. The sequential application of these subcircuits to an initial state generates a set of wavefunctions that we use as a quantum subspace to obtain high-accuracy groundstate energies. We call this technique the circuit subspace variational quantum eigensolver (CSVQE) algorithm. By benchmarking CSVQE on a range of quantum chemistry problems, we show that it can achieve significant error reduction in the best case compared to conventional VQE, particularly for poorly optimized circuits, greatly improving convergence rates. Furthermore, we demonstrate that when applied to circuits trapped at local minima, CSVQE can produce energies close to the global minimum of the energy landscape, making it a potentially powerful tool for diagnosing local minima.

Variational quantum imaginary time evolution for matrix product state Ansatz with tests on transcorrelated Hamiltonians.

The matrix product state (MPS) Ansatz offers a promising approach for finding the ground state of molecular Hamiltonians and solving quantum chemistry problems. Building on this concept, the proposed technique of quantum circuit MPS (QCMPS) enables the simulation of chemical systems using a relatively small number of qubits. In this study, we enhance the optimization performance of the QCMPS Ansatz by employing the variational quantum imaginary time evolution (VarQITE) approach. Guided by McLachlan's variational principle, the VarQITE method provides analytical metrics and gradients, resulting in improved convergence efficiency and robustness of the QCMPS. We validate these improvements numerically through simulations of H2, H4, and LiH molecules. In addition, given that VarQITE is applicable to non-Hermitian Hamiltonians, we evaluate its effectiveness in preparing the ground state of transcorrelated Hamiltonians. This approach yields energy estimates comparable to the complete basis set (CBS) limit while using even fewer qubits. In particular, we perform simulations of the beryllium atom and LiH molecule using only three qubits, maintaining high fidelity with the CBS ground state energy of these systems. This qubit reduction is achieved through the combined advantages of both the QCMPS Ansatz and transcorrelation. Our findings demonstrate the potential practicality of this quantum chemistry algorithm on near-term quantum devices.

Parallel Implementation of the Density Matrix Renormalization Group Method Achieving a Quarter petaFLOPS Performance on a Single DGX-H100 GPU Node.

We report cutting edge performance results on a single node hybrid CPU-multi-GPU implementation of the spin adapted ab initio Density Matrix Renormalization Group (DMRG) method on current state-of-the-art NVIDIA DGX-H100 architectures. We evaluate the performance of the DMRG electronic structure calculations for the active compounds of the FeMoco, the primary cofactor of nitrogenase, and cytochrome P450 (CYP) enzymes with complete active space (CAS) sizes of up to 113 electrons in 76 orbitals [CAS(113, 76)] and 63 electrons in 58 orbitals [CAS(63, 58)], respectively. We achieve 246 teraFLOPS of sustained performance, an improvement of more than 2.5× compared to the performance achieved on the DGX-A100 architectures and an 80× acceleration compared to an OpenMP parallelized implementation on a 128-core CPU architecture. Our work highlights the ability of tensor network algorithms to efficiently utilize high-performance multi-GPU hardware and shows that the combination of tensor networks with modern large-scale GPU accelerators can pave the way toward solving some of the most challenging problems in quantum chemistry and beyond.

Spectral Map for Slow Collective Variables, Markovian Dynamics, and Transition State Ensembles.

Understanding the behavior of complex molecular systems is a fundamental problem in physical chemistry. To describe the long-time dynamics of such systems, which is responsible for their most informative characteristics, we can identify a few slow collective variables (CVs) while treating the remaining fast variables as thermal noise. This enables us to simplify the dynamics and treat it as diffusion in a free-energy landscape spanned by slow CVs, effectively rendering the dynamics Markovian. Our recent statistical learning technique, spectral map [Rydzewski, J. J. Phys. Chem. Lett. 2023, 14(22), 5216-5220], explores this strategy to learn slow CVs by maximizing a spectral gap of a transition matrix. In this work, we introduce several advancements into our framework, using a high-dimensional reversible folding process of a protein as an example. We implement an algorithm for coarse-graining Markov transition matrices to partition the reduced space of slow CVs kinetically and use it to define a transition state ensemble. We show that slow CVs learned by spectral map closely approach the Markovian limit for an overdamped diffusion. We demonstrate that coordinate-dependent diffusion coefficients only slightly affect the constructed free-energy landscapes. Finally, we present how spectral maps can be used to quantify the importance of features and compare slow CVs with structural descriptors commonly used in protein folding. Overall, we demonstrate that a single slow CV learned by spectral map can be used as a physical reaction coordinate to capture essential characteristics of protein folding.

Solving quantum chemistry problems on quantum computers

One of the earliest applications that the new era of computing may be used for is the simulation of the quantum effects that drive chemical reactions.