jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leon Hostetler | Ryo Sakai | Jin Zhang | Alexei Bazavov | Yannick Meurice
Source: arXiv (Cornell University) (preprint, 2023-12-29)
Field: Chemistry — Spectroscopy

Symmetry Breaking in an Extended O(2) Model Motivated by attempts to quantum simulate lattice models with continuous Abelian symmetries using discrete approximations, we study an extended-O(2) model in two dimensions that differs from the ordinary O(2) model by the addition of an explicit symmetry breaking term $-h_q\cos(qφ)$. Its coupling $h_q$ allows to smoothly interpolate between the O(2) model ($h_q=0$) and a $q$-state clock model ($h_q\rightarrow\infty$). In the latter case, a $q$-state clock model can also be defined for noninteger values of $q$. Thus, such a limit can also be considered as an analytic continuation of an ordinary $q$-state clock model to noninteger $q$. In previous work, we established the phase diagram for noninteger $q$ in the infinite coupling limit ($h_q\rightarrow\infty$). We showed that there is a second-order phase transition at low temperature and a crossover at high temperature. In this work, we seek to establish the phase diagram at finite values of the coupling using Monte Carlo and tensor methods. We show that for noninteger $q$, the second-order phase transition at low temperature and crossover at high temperature persist to finite coupling. For integer $q=2,3,4$, we know there is a second-order phase transition at infinite coupling (i.e. the well-known clock models). At finite coupling, we find that the critical exponents for $q=3,4$ vary with the coupling, and for $q=4$ the transition may turn into a BKT transition at small coupling. We comment on the similarities and differences of the phase diagrams with those of quantum simulators of the Abelian-Higgs model based on ladder-shaped arrays of Rydberg atoms. Symmetry breaking ; Spontaneous symmetry breaking ; Physics ; Explicit symmetry breaking ; Symmetry (geometry) ; Theoretical physics ; Mathematics ; Mathematical physics ; Particle physics ; Geometry Molecular spectroscopy and chirality ; Methane Hydrates and Related Phenomena
Authors: Sangeeta Rajpurohit | Christian Jooß | Simone Techert | Tadashi Ogitsu | Peter E. Blöchl | L. Z. Tan
Source: arXiv (Cornell University) (preprint, 2023-06-15)
Field: Materials Science — Materials Chemistry

Characteristic THz-emissions induced by optically excited collective orbital modes We study the generation of collective orbital modes, their evolution, and the characteristic nonlinear optical response induced by them in a photoinduced orbital-ordered correlated oxide using real-time simulations based on an interacting multiband tight-binding (TB) model. The d-d optical transitions under femtoseconds light-pulse in an orbital-ordered state excite collective orbital modes, also known as "orbitons". Consistently incorporating electronic interactions and the interplay between charge, spin, and lattice degrees of freedom in the TB-model provides a clearer understanding of how these factors influence the generation and evolution of collective orbital modes. The dynamics of Jahn-Teller vibrational modes in the photoinduced state modify the intersite orbital interaction, which further amplifies these orbital modes. In the presence of weak ferroelectricity, the excitation of collective orbital modes induces a strong THz oscillatory photocurrent, which is long-lived. This suggests an alternative way to experimentally detect low-energy collective modes through THz-emission studies in the photoinduced state. Our study also elucidates that quasiparticle dynamics in improper ferroelectric oxides can be exploited to achieve highly interesting and non-trivial optoelectronic properties. Quasiparticle ; Physics ; Excited state ; Femtosecond ; Excitation ; Condensed matter physics ; Terahertz radiation ; Molecular physics ; Atomic physics ; Quantum mechanics ; Laser Solid-state spectroscopy and crystallography ; Acoustic Wave Resonator Technologies ; Crystal Structures and Properties
Authors: Cheong-Eung Ahn | Kyung‐Hwan Jin | Choi, Young-Jae | Jae Whan Park | Han Woong Yeom | Ara Go | Yong Baek Kim | Gil Young Cho
Source: arXiv (Cornell University) (preprint, 2023-09-27)
Field: Materials Science — Materials Chemistry

Emergent Quantum Phenomena of Noncentrosymmetric Charge-Density Wave in 1T-Transition Metal Dichalcogenides 1T-transition metal dichalcogenides (TMD) have been an exciting platform for exploring the intertwinement of charge density waves and strong correlation phenomena. While the David star structure has been conventionally considered as the underlying charge order in the literature, recent scanning tunneling probe experiments on several monolayer 1T-TMD materials have motivated a new, alternative structure, namely the anion-centered David star structure. In this Letter, we show that this novel anion-centered David star structure manifestly breaks inversion symmetry, resulting in flat bands with pronounced Rashba spin-orbit couplings. These distinctive features unlock novel possibilities and functionalities for 1T-TMDs, including the giant spin Hall effect, the emergence of Chern bands, and spin liquid that spontaneously breaks crystalline rotational symmetry. Our findings establish promising avenues for exploring emerging quantum phenomena of monolayer 1T-TMDs with this novel noncentrosymmetric structure. Point reflection ; Monolayer ; Condensed matter physics ; Transition metal ; Charge (physics) ; Charge density wave ; Quantum tunnelling ; Scanning tunneling microscope ; Physics ; Materials science ; Chemical physics ; Chemistry ; Superconductivity ; Nanotechnology ; Quantum mechanics 2D Materials and Applications ; Cold Atom Physics and Bose-Einstein Condensates ; Topological Materials and Phenomena
Authors: Сергій Сергійович Курєннов | Костянтин Барахов | Ганна Барахова
Source: The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Застосування кривих Безьє для опису форми конструкції при оптимізації клейового з’єднання The problem of topological optimization of a symmetrical double-shear adhesive joint has been solved. The suggested mathematical model of a joint with variable thickness generalizes the classic Holland–Reissner model. The shape of the doubler is described by means of the Bezier curve. Seeking parameters in the optimization problem are coordinates of reference points of the Bezier curve. Both joint length and doubler cross-section area can be considered an objective function. The restriction is applied on stress in adhesive film and doubler. The direct problem of finding the joint stress state at given geometric parameters was solved using the finite difference method. The genetic algorithm was used to solve the optimization problem. In order to improve the convergence of the genetic algorithm, the island model of evolution is suggested, which ensures quick evolution selection and stability of obtained results. The model problem is solved. Convergence (economics) ; Bézier curve ; Joint (building) ; Stability (learning theory) ; Genetic algorithm ; Optimization problem ; Stress (linguistics) ; Finite element method Structural mechanics and materials ; Topology Optimization in Engineering ; Elasticity and Wave Propagation
Authors: Trần Minh Tú | Nguyễn Văn Long | Phạm Thúy Hằng | Trần Văn Bình | Tạ Thị Hiền
Source: Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE (article, 2023-11-24)
Field: Materials Science — Materials Chemistry

Phân tích tĩnh tấm FGM có vi bọt rỗng trên nền đàn hồi Kerr theo lý thuyết biến dạng cắt bậc nhất đơn giản Bài báo tiến hành phân tích tĩnh tấm FGM có vi bọt rỗng đặt trên nền đàn hồi Kerr theo lý thuyết biến dạng cắtbậc nhất đơn giản. Không giống như lý thuyết biến dạng cắt bậc nhất Reissner-Mindlin, lý thuyết biến dạng cắt bậc nhất đơn giản quan niệm độ võng gồm hai thành phần: do biến dạng uốn và do biến dạng cắt ngang gây nên, vì thế số thành phần chuyển vị giảm từ năm xuống bốn ẩn số. Ba loại tấm được xét đến gồm tấm hoàn hảo, tấm có vi bọt rỗng phân bố đều và không đều. Dựa trên nguyên lý thế năng cực tiểu, hệ phương trình cân bằng được thiết lập và giải bằng cách sử dụng dạng nghiệm Navier cho tấm chữ nhật liên kết khớp trên chu tuyến. Ảnh hưởng của các tham số vật liệu, kích thước và nền đàn hồi đến độ võng và các thành phần ứng suất của tấm được khảo sát qua các ví dụ số. Stereochemistry ; Chemistry Structural mechanics and materials ; Composite Structure Analysis and Optimization
Authors: Mengke Li | Hongmei Yuan | Yufeng Luo | Xiaoyi Ma | Huijun Liu | Wentao Jiang | Ying Fang
Source: Advanced Energy Materials (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Advanced Thermoelectric Materials via Transfer Learning (Adv. Energy Mater. 23/2023) Thermoelectrics In article 2300049, Huijun Liu, Ying Fang, and co-workers leverage the knowledge from a pre-training on electronic bandgaps to propose a transfer learning strategy that allows ready and accurate predictions of the ZT values of any candidate materials at arbitrary temperature, which is very beneficial for the accelerated discovery of advanced thermoelectric materials. Materials science ; Leverage (statistics) ; Thermoelectric materials ; Thermoelectric effect ; Energy transfer ; Engineering physics ; Nanotechnology ; Computer science ; Composite material ; Machine learning ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Jian‐Min Wang
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 1; peer review: 3 approved with reservations] Affinities ; Chemical space ; Transformer ; Generative grammar ; Reinforcement learning Machine Learning in Materials Science ; Micro and Nano Robotics ; Nanopore and Nanochannel Transport Studies
Authors: Qifeng Bai
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 1; peer review: 4 approved with reservations] Affinities ; Chemical space ; Transformer ; Generative grammar ; Reinforcement learning Machine Learning in Materials Science ; Micro and Nano Robotics ; Nanopore and Nanochannel Transport Studies
Authors: Ka‐Chun Wong
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 1; peer review: 1 approved with reservations] Affinities ; Chemical space ; Transformer ; Generative grammar ; Reinforcement learning Machine Learning in Materials Science ; Micro and Nano Robotics ; Nanopore and Nanochannel Transport Studies
Authors: Aslıhan Aycan Tanrıverdi
Source: Faculty of 1000 Research Ltd (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Referee report. For: Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning [version 1; peer review: 5 approved with reservations] Affinities ; Chemical space ; Transformer ; Generative grammar ; Reinforcement learning Machine Learning in Materials Science ; Micro and Nano Robotics ; Nanopore and Nanochannel Transport Studies
Authors: Mikhail Lifar | Andrei A. Tereshchenko | Aleksei N. Bulgakov | Alexander A. Guda | С. А. Гуда | А. В. Солдатов
Source: Journal of Surface Investigation X-ray Synchrotron and Neutron Techniques (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Carbon Monoxide Oxidation on the Surface of Palladium Nanoparticles Optimized by Reinforcement Learning The yield of reaction products depends on the interaction between processes on the catalyst surface: adsorption, activation, reaction, desorption, and others. These processes, in turn, depend on the magnitude of the flows of reaction mixtures, temperature, and pressure. Under stationary conditions, active sites on the surface can be poisoned by reaction by-products or blocked by an excess of adsorbed reactant molecules. Dynamic control of reaction parameters takes into account changes in surface properties and adjusts the temperature, flow rates, and other parameters accordingly. A reinforcement learning algorithm was applied to control the oxidation reaction of carbon monoxide CO on the surface of palladium nanoparticles. The algorithm was trained to maximize the rate of carbon dioxide production based on information about the magnitude of CO, O2, and CO2 flows at each time step. A gradient policy algorithm with a continuous action space was chosen, and observations of the flow rates were extended over several successive time steps, which made it possible to obtain a set of non-stationary solutions. The maximum yield of the product is achieved with a periodic change in gas flows, which ensures a balance between the available adsorption sites and the concentration of activated intermediates. This methodology opens up prospects for optimizing catalytic reactions under nonstationary conditions. Carbon monoxide ; Adsorption ; Catalysis ; Desorption ; Yield (engineering) ; Reaction rate ; Chemical engineering ; Chemistry ; Space velocity ; Palladium ; Materials science ; Composite material ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Mikhail Lifar | Andrei A. Tereshchenko | Aleksei N. Bulgakov | Alexander A. Guda | С. А. Гуда | А. В. Солдатов
Source: Поверхность Рентгеновские синхротронные и нейтронные исследования (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Reaction of CO Oxidation on the Surface of Pd Nanoparticles: Optimization by Reinforcement Learning The yield of reaction products depends on the interaction between processes on the catalyst surface: adsorption, activation, reaction, desorption, and others. These processes, in turn, depend on the magnitude of the flows of reaction mixtures, temperature, and pressure. Under stationary conditions, active sites on the surface can be poisoned by reaction by-products or blocked by an excess of adsorbed reactant molecules. Dynamic control of reaction parameters takes into account changes in surface properties and adjusts temperature, flow rates and other parameters accordingly. A reinforcement learning algorithm was applied to control the oxidation reaction of carbon monoxide CO on the surface of palladium nanoparticles. The algorithm was trained to maximize the rate of carbon dioxide production based on information about the magnitude of CO, O2 and CO2 fluxes at each time step. A gradient policy algorithm with a continuous action space was chosen, and observations of the flow rates were extended over several successive time steps, which made it possible to obtain a set of non-stationary solutions. The maximum yield of the product is achieved with a periodic change in gas flows, which ensures a balance between the available adsorption sites and the concentration of activated intermediates. This methodology opens up prospects for optimizing catalytic reactions under nonstationary conditions. Reinforcement ; Reinforcement learning ; Nanoparticle ; Chemical engineering ; Materials science ; Chemistry ; Nanotechnology ; Computer science ; Composite material ; Engineering ; Artificial intelligence Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Ikuo Kurisaki | Madoka Suzuki
Source: arXiv (Cornell University) (preprint, 2023-02-24)
Field: Chemistry — Physical and Theoretical Chemistry

Simulation toolkits at the molecular scale for trans-scale thermal signaling Thermogenesis is a physiological activity of releasing heat that originates from intracellular biochemical reactions. Recent experimental studies discovered that externally applied heat changes intracellular signaling locally, resulting in global changes in cell morphology and signaling. Therefore, we hypothesize an inevitable contribution of thermogenesis in modulating biological system functions throughout the spatial scales from molecules to individual organisms. One key issue examining the hypothesis, namely, the "trans-scale thermal signaling," resides at the molecular scale on the amount of heat released via individual reactions and by which mechanism the heat is employed for cellular function operations. This review introduces atomistic simulation tool kits for studying the mechanisms of thermal signaling processes at the molecular scale that even state-of-the-art experimental methodologies of today are hardly accessible. We consider biological processes and biomolecules as potential heat sources in cells, such as ATP/GTP hydrolysis and multiple biopolymer complex formation and disassembly. Microscopic heat release could be related to mesoscopic processes via thermal conductivity and thermal conductance. Additionally, theoretical simulations to estimate these thermal properties in biological membranes and proteins are introduced. Finally, we envisage the future direction of this research field. Mesoscopic physics ; Thermal conductivity ; Biomolecule ; Scale (ratio) ; Mechanism (biology) ; Intracellular ; Nanotechnology ; Cell signaling ; Thermal ; Biophysics ; Biological system ; Chemistry ; Biochemical engineering ; Materials science ; Signal transduction ; Biology ; Physics ; Biochemistry ; Thermodynamics ; Engineering thermodynamics and calorimetric analyses ; Advanced Thermodynamics and Statistical Mechanics ; Protein Structure and Dynamics
Authors: Gaurav Tamrakar | Shailesh Singh Thakur
Source: MIGRATION LETTERS (article, 2023-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanocomposites Utilizing Carbon Quantum Dots for Lithium Energy Storage and Transformation In the dynamic realm of energy use, the pursuit of practical and environmentally friendly energy storage technologies is paramount. The growing need for portable electronic devices, electric cars, and the integration of renewable energy into the power grid drives the urgency. Lithium-based energy storage systems have emerged as leading contenders among the vast array of available options due to their high energy density and comparatively extended cycle life. Nevertheless, these systems are full of obstacles, including issues related to safety and the constrained availability of resources. To tackle these concerns and advance the field of energy storage, incorporating nanomaterials has attracted considerable interest. This research explores the domain of energy storage, with a particular focus on the significance of Lithium Energy Storage Systems (LESS). This study examines the potential of nanoscale components and materials in changing energy storage, namely Carbon Quantum Dots (CQDs). Due to their remarkable characteristics, CQDs provide a potential pathway for improving energy storage devices. However, current energy storage techniques need help regarding energy density, cycle life, and sustainability. This research presents a unique strategy using Carbon Quantum Dots for a Lithium Energy Storage System (CQD-LESS). This novel approach leverages the unique characteristics of CQDs to address these obstacles directly. CQD-LESS has many advantages, including increased energy density, extended cycle life, and greater sustainability. The experimental results yielded average capacity retention of 158.35%, a charge transfer resistance of 4.21 ohms, a cyclic stability of 94.06%, an energy density of 123.30 Wh/kg, a power density of 157.30 W/kg, an Electrochemical Impedance Spectroscopy (EIS) frequency of 1233.00 Hz, a specific capacity of 337.50 mAh/g, and an average operating temperature of 28.10°C. Energy storage ; Nanotechnology ; Renewable energy ; Materials science ; Lithium (medication) ; Grid energy storage ; Life-cycle assessment ; Process engineering ; Computer science ; Environmental science ; Power (physics) ; Electrical engineering ; Distributed generation ; Engineering Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Racheal John | Rakesh Bhardwaj | Christine Jeyaseelan | Haritha Bollinedi | Rakesh Singh | Gurbachan Singh
Source: Research Square (preprint, 2023-10-09)
Field: Chemistry — Analytical Chemistry

Comparative Assessment of WINISI and The Unscrambler for Near Infrared Reflectance Spectroscopy (NIRS) Modelling of Phytate and Antioxidants in Brown Rice Brown rice ; Phytic acid ; Principal component analysis ; Mathematics ; Reflectivity ; Chemistry ; Food science ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Heavy Metals in Plants ; Food composition and properties
Authors: Racheal John | Rakesh Bhardwaj | Christine Jeyaseelan | Haritha Bollinedi | Rakesh Singh | Gurbachan Singh
Source: Research Square (preprint, 2023-11-10)
Field: Chemistry — Analytical Chemistry

Comparative Assessment of WINISI and Unscrambler for NIRS Modelling of Phytate and Antioxidants in Brown Rice Abstract Brown rice has been known to be a better alternative to polished rice due to greater amounts of nutrients including antioxidants. Phytic acid and phenols in brown rice are also known to impart antioxidant capacity. Near infrared reflectance spectroscopy is renowned to estimate the nutritional composition of brown rice. The present study was conducted to develop the prediction models of total phenolic content (TPC), phytic acid (TPA) and antioxidant capacity (TAC) of brown rice. While both WIN ISI and The Unscrambler® software produce crisp models, certain limitations such as principle component analysis (PCA) descriptives in WIN ISI and inverse multiple scatter correction (iMSC) in The Unscrambler® lead to uncertainty of the results. Hence, the models in this study were generated using PCA and partial least square (PLS) regression, which were compared on both WIN ISI and The Unscrambler® separately. The reference data of 226 rice landraces was subjected to both the softwares and optimal models were obtained from standard normal variate (SNV-DT) for TPA and TPC over The Unscrambler®, while multiplicative scatter correction (MSC) was found better for TPC model development on WIN ISI. According to regression analysis the best prediction model was obtained for TPC employing MSC with RSQ = 0.925 and RPD = 3.11 in WIN ISI, while the rest were better validated in The Unscrambler® using SNV-DT with RSQ = 0.888, 0.958 RPD = 2.97, 2.93 for TPA and TAC respectively. The results are indicative of the NIRS ability and the effect of different scatter corrections to rapidly predict antioxidant content in brown rice. Phytic acid ; Brown rice ; Mathematics ; Antioxidant capacity ; Chemistry ; Food science ; Antioxidant ; Biochemistry Spectroscopy and Chemometric Analyses ; Phytase and its Applications ; Heavy Metals in Plants