jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuan Liu | Yangyang Pu | Xiao Cheng Zeng
Source: Nanoscale (review, 2022-11-28)
Field: Chemistry — Inorganic Chemistry

Nanoporous ices: an emerging class in the water/ice family The history of scientific research on diverse ice structures dates back to more than a century. To date, 20 three-dimensional crystalline ice phases (ice I-ice XX) have been identified in the laboratory, among which ice XVI and ice XVII belong to a class of low-density nanoporous ices. Nanoporous ices can also be viewed as a special class of porous materials or water ice, as they possess a relatively high fraction of nano-cavities and/or nano-channels built into the hydrogen-bonded water framework. As such, like the prototypical class of porous materials (<i>e.g.</i>, MOFs and COFs), nanoporous ices can be named as water oxygen-vertex frameworks (WOFs). Because of their large surface-to-volume ratio, WOFs may be potential media for gas storage, gas purification and separation. They may be applied to the biomedical field owing to their excellent biocompatibility. The field of porous ices is still emerging, as many porous ice structures that are predicted to be stable by computer simulations require future experimental confirmation. For future theoretical/computational studies, as the machine-learning method becomes an increasingly popular research tool in the material science and chemical science fields, more reliable porous ice structures and phase diagrams will be predicted with the development of more accurate machine-learning force fields. Nanoporous ; Class (philosophy) ; Nanotechnology ; Materials science ; Astrobiology ; Computer science ; Physics ; Artificial intelligence Inorganic Fluorides and Related Compounds ; Zeolite Catalysis and Synthesis ; Calcium Carbonate Crystallization and Inhibition
Authors: Ryo Kawada | Katsuhiro Endo | Kenji Yasuoka | Hidekazu Kojima | Nobuyuki Matubayasi
Source: Journal of Chemical Information and Modeling (article, 2022-12-07)
Field: Materials Science — Materials Chemistry

Prediction of Water Diffusion in Wide Varieties of Polymers with All-Atom Molecular Dynamics Simulations and Deep Generative Models Permeation through polymer membranes is an important technology in the chemical industry, and in its design, the self-diffusion coefficient is one of the physical quantities that determine permeability. Since the self-diffusion coefficient sensitively reflects intra- and intermolecular interactions, analysis using an all-atom model is required. However, all-atom simulations are computationally expensive and require long simulation times for the diffusion of small molecules dissolved in polymers. MD-GAN, a machine learning model, is effective in accelerating simulations and reducing computational costs. The target systems for MD-GAN prediction were limited to polyethylene melts in previous studies; therefore, this study extended MD-GAN to systems containing copolymers with branches and successfully predicted water diffusion in various polymers. The correlation coefficient between the predicted self-diffusion coefficient and that of the long-time simulation was 1.00. Additionally, we found that incorporating statistical domain knowledge into MD-GAN improved accuracy, reducing the mean-square displacement prediction outliers from 14.6% to 5.3%. Lastly, the distribution of latent variables with embedded dynamics information within the model was found to be strongly related to accuracy. We believe that these findings can be useful for the practical applications of MD-GAN. Mean squared displacement ; Molecular dynamics ; Polymer ; Materials science ; Diffusion ; Atom (system on chip) ; Permeation ; Correlation coefficient ; Intermolecular force ; Chemical physics ; Membrane ; Statistical physics ; Molecule ; Thermodynamics ; Computer science ; Chemistry ; Computational chemistry ; Physics ; Machine learning ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Polymer crystallization and properties
Authors: Ludwig Schneider | Marcus Schwarting | Joshua A. Mysona | Heyi Liang | Ming Han | Phillip M. Rauscher | Jeffrey Ting | Shruti Venkatram | Richard B. Ross | K. J. Schmidt | Ben Blaiszik | Ian Foster | Juan Pablo
Source: Molecular Systems Design & Engineering (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

<i>In silico</i> active learning for small molecule properties Machine learning (ML) has emerged as a promising technology to accelerate materials discovery. In silico ; Active learning (machine learning) ; Molecule ; Computer science ; Computational biology ; Chemistry ; Artificial intelligence ; Biology ; Biochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electronic and Structural Properties of Oxides
Authors: Polina N. Mayer | Victor V. Pogorelko | D.S. Voronin | Alexander E. Mayer
Source: Metals (article, 2022-11-03)
Field: Materials Science — Materials Chemistry

Spall Fracture of Solid and Molten Copper: Molecular Dynamics, Mechanical Model and Strain Rate Dependence In this study, we formulate a mechanical model of spall fracture of copper, which describes both solid and molten states. The model is verified, and its parameters are found based on the data of molecular dynamics simulations of this process under ultrahigh strain rate of tension, leading to the formation of multiple pores within the considered volume element. A machine-learning-type Bayesian algorithm is used to identify the optimal parameters of the model. We also analyze the influence of the initial size distribution of pores or non-wettable inclusions in copper on the strain rate dependence of its spall strength and show that these initial heterogeneities explain the existing experimental data for moderate strain rates. This investigation promotes the development of atomistically-based machine learning approaches to description of the strength properties of metals and deepens the understanding of the spall fracture process. Spall ; Strain rate ; Materials science ; Fracture (geology) ; Copper ; Molecular dynamics ; Tension (geology) ; Mechanics ; Composite material ; Metallurgy ; Ultimate tensile strength ; Chemistry ; Physics ; Computational chemistry Microstructure and mechanical properties ; High-Velocity Impact and Material Behavior ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Grier M. Jones | Brett A. Smith | Justin K. Kirkland | Konstantinos D. Vogiatzis
Source: Inorganic Chemistry Frontiers (article, 2022-11-18)
Field: Materials Science — Materials Chemistry

Data-driven ligand field exploration of Fe(<scp>iv</scp>)–oxo sites for C–H activation We have explored the ligand topology of high-valent Fe( iv )–oxo complexes for screening a large molecular database with machine learning. Ligand (biochemistry) ; Field (mathematics) ; Chemistry ; Computational biology ; Topology (electrical circuits) ; Computer science ; Combinatorial chemistry ; Biology ; Biochemistry ; Mathematics ; Combinatorics ; Receptor ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Jana Rehm | Ta‐Shun Chou | Saud Bin Anooz | Palvan Seyidov | Andreas Fiedler | Zbigniew Galazka | Andreas Popp
Source: Applied Physics Letters (article, 2022-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Perspectives on MOVPE-grown (100) β-Ga2O3 thin films and its Al-alloy for power electronics application Beta gallium oxide (β-Ga2O3) is a promising ultra-wide bandgap semiconductor with attractive physical properties for next-generation high-power devices, radio frequency electronics, and solar-blind ultraviolet radiation detectors. Here, we present an overview and perspective on the development of MOVPE-grown (100) β-Ga2O3 thin films and its role in supplementing high-power electronics. We review the development path of the growth process on (100) β-Ga2O3 thin films with a discussion regarding the solved and remaining challenges. The structural defect formation mechanism, substrate treatment strategies, and different growth windows are analyzed to optimize the grown film to fulfill the requirements for device fabrication. Toward industrial applications, MOVPE-grown β-Ga2O3 thin films are evaluated in two aspects: thick layers with smooth surface roughness and the electrical properties in terms of high carrier mobility and low doping concentration. Based on the reviewed results, we propose strategies in substrate preparation treatments and supportive tools such as the machine learning approaches for future growth process optimization and envision the rising interest of the β-Ga2O3-related alloy, β-(AlxGa1−x)2O3. Materials science ; Metalorganic vapour phase epitaxy ; Thin film ; Optoelectronics ; Substrate (aquarium) ; Nanotechnology ; Electronics ; Doping ; Electron mobility ; Band gap ; Epitaxy ; Engineering physics ; Electrical engineering ; Layer (electronics) Ga2O3 and related materials ; ZnO doping and properties ; Advanced Photocatalysis Techniques
Authors: Natalja Fjodorova | Marjana Novič | Katja Venko | Viktor Drgan | Bakhtiyor Rasulev | Melek Türker Saçan | Safiye Sağ Erdem | Gulcin Tugcu | Alla P. Toropova | Andrey A. Toropov
Source: Computational and Structural Biotechnology Journal (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein-ligand binding in order to identify the key binding characteristics of FDs. Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction. The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles. All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity. Protein Data Bank (RCSB PDB) ; Cheminformatics ; Quantitative structure–activity relationship ; Computational biology ; Protein Data Bank ; Molecular descriptor ; Ranking (information retrieval) ; Chemistry ; Machine learning ; Computer science ; Protein structure ; Biology ; Computational chemistry ; Biochemistry Fullerene Chemistry and Applications ; Cholinesterase and Neurodegenerative Diseases ; Computational Drug Discovery Methods
Authors: Ivan M. Novitskiy | Andrei G. Kutateladze
Source: Natural Product Reports (review, 2022-01-01)
Field: Chemistry — Spectroscopy

Brief overview of recently reported misassigned natural products and their <i>in silico</i> revisions enabled by DU8ML, a machine learning-augmented DFT computational NMR method Mostly covering 2018 to 2022This <i>Highlight</i> article describes a personal selection of recent misassigned structures of natural products and their revision with the aid of DU8ML, a machine learning-augmented DFT computational method for fast and accurate calculations of solution NMR chemical shifts and spin-spin coupling constants. In silico ; Computer science ; Coupling constant ; Selection (genetic algorithm) ; Artificial intelligence ; Coupling (piping) ; Computational chemistry ; Machine learning ; Chemistry ; Materials science ; Physics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Xien Yin Yap | Kim Seng Chia | Nur Aisyah Syafinaz Suarin
Source: Chemometrics and Intelligent Laboratory Systems (article, 2022-09-22)
Field: Chemistry — Analytical Chemistry

Adaptive Artificial Neural Network in near infrared spectroscopy for standard-free calibration transfer Calibration ; Artificial neural network ; Computer science ; Transfer of learning ; Artificial intelligence ; Principal component analysis ; Bayesian probability ; Standardization ; Machine learning ; Pattern recognition (psychology) ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Bachir Aoun
Source: Journal of Applied Crystallography (article, 2022-10-14)
Field: Materials Science — Materials Chemistry

Stochastic atomic modeling and optimization with <i>fullrmc</i> Understanding materials' atomic structure with a high level of confidence and certainty is often regarded as a very arduous and sometimes impossible task, especially for newer, emerging technology materials exhibiting limited long-range order. Nevertheless, information about atomic structural properties is very valuable for materials science and synthesis. For non-crystalline amorphous and nanoscale materials, using conventional structural determination methods is impossible. Reverse Monte Carlo (RMC) modeling is commonly used to derive models of materials from experimental diffraction data. Here, the latest developments in the fullrmc software package are discussed. Despite its name, fullrmc provides a very flexible modeling framework for solving atomic structures with many methods beyond RMC. The stochastic nature of fullrmc allows it to explore all possible dimensions and degrees of freedom for atomic modeling and create statistical solutions to match measurements. Differing versions of fullrmc are provided as open source or for cloud computing access. The latter includes a modern web-based graphical user interface that incorporates advanced computing and structure-building modules and machine-learning-based components. The main features of fullrmc are presented, including constraint types, boundary conditions, density shape functions and the two running modes: stochastic using a Monte Carlo algorithm and optimization using a genetic algorithm. Capabilities include tools for statistical, mesoscopic and nanoscopic approaches, atomic or coarse-grained models, and smart artificial-intelligence-ready loss functions. Computer science ; Mesoscopic physics ; Reverse Monte Carlo ; Monte Carlo method ; Software ; Computational science ; Statistical physics ; Algorithm ; Theoretical computer science ; Diffraction ; Physics ; Mathematics X-ray Diffraction in Crystallography ; High-pressure geophysics and materials ; Machine Learning in Materials Science
Authors: V. Sathana | M. Mathumathi | K. Makanyadevi
Source: Materials Today Proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Prediction of material property using optimized augmented graph-attention layer in GNN Computer science ; Graph ; Property (philosophy) ; Artificial neural network ; Heuristic ; Focus (optics) ; Data mining ; Algorithm ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Chun‐Yan Qin | Zhilian Yue | Gordon G. Wallace | Jun Chen
Source: ACS Applied Bio Materials (review, 2022-10-19)
Field: Materials Science — Polymers and Plastics

Bipolar Electrochemical Stimulation Using Conducting Polymers for Wireless Electroceuticals and Future Directions Electrochemistry has become a powerful strategy to modulate cellular behavior and biological activity by manipulating electrical signals. Subsequent electrical stimulus-responsive conducting polymers (CPs) have advanced traditional wired electrochemical stimulation (ES) systems and developed wireless cell stimulation systems due to their electroconductivity, biocompatibility, stability, and flexibility. Bipolar electrochemistry (BPE), i.e., wireless electrochemistry, offers an effective pathway to modify wired ES systems into a desirable contactless mode, turning out a potential technique to offer fundamental insights into neural cell stimulation and neural network formation. This review commences with a brief discussion of the BPE technique and also the advantages of a bipolar electrochemical stimulation (BPES) system compared to traditional wired ES systems and other wireless ES systems. Then, the BPES system is elucidated through four aspects: the benefits of BPES, the key factors to establish BPES platforms for cell stimulation, the limits/barriers to overcome for current rigid materials in particular metals-based systems, and a brief overview of the concept proved by CPs-based systems. Furthermore, how to refine the existing BPES system from materials/devices modification that combine CP compositions with 3D fabrication/bioprinting technologies is elaborately discussed as well. Finally, the review ends together with future research directions, picturing the potential of BPES system in biomedical applications. Wireless ; Computer science ; Flexibility (engineering) ; Stimulation ; Biocompatibility ; Electrochemistry ; Electrical engineering ; Nanotechnology ; Neuroscience ; Materials science ; Engineering ; Telecommunications ; Chemistry ; Psychology Conducting polymers and applications ; Neuroscience and Neural Engineering ; Advanced Sensor and Energy Harvesting Materials
Authors: Robert U. Stelzer | Yuji Ikeda | Prashanth Srinivasan | Tanja S. Lehmann | Blazej Grabowski | Rainer Niewa
Source: Journal of the American Chemical Society (article, 2022-04-13)
Field: Materials Science — General Materials Science

Li<sub>5</sub>Sn, the Most Lithium-Rich Binary Stannide: A Combined Experimental and Computational Study From reaction of excess lithium with tin, we isolate well-crystallized Li<sub>5</sub>Sn and solve the crystal structure from single-crystal X-ray diffraction data. The orthorhombic structure (space group <i>Cmcm</i>) features the same coordination polyhedra around tin and lithium as previously predicted by electronic structure calculations for this composition, however differently arranged. An extensive <i>ab initio</i> analysis, including thermodynamic integration using Langevin dynamics in combination with a machine-learning potential (moment tensor potential), is conducted to understand the thermodynamic stability of this <i>Cmcm</i> Li<sub>5</sub>Sn structure observed in our experiments. Among the 108 Li<sub>5</sub>Sn structures systematically derived using the structure enumeration algorithm, including the experimental <i>Cmcm</i> structure and those obtained in previous <i>ab initio</i> studies, another new structure with the space group <i>Immm</i> is found to be energetically most stable at 0 K. This computationally discovered <i>Immm</i> structure is also found to be thermodynamically more stable than the <i>Cmcm</i> structure at finite temperatures, indicating that the <i>Cmcm</i> Li<sub>5</sub>Sn structure observed in our experiments is favored likely due to kinetic reasons rather than thermodynamics. Chemistry ; Lithium (medication) ; Binary number ; Computational chemistry ; Arithmetic ; Internal medicine Metallurgical and Alloy Processes ; Inorganic Chemistry and Materials ; Intermetallics and Advanced Alloy Properties
Authors: Xiaohua Tian | Zhou Li-wen | Kun Zhang | Qiu Zhao | Hongxing Li | Dingding Shi | Tianyou Ma | Cheng Wang | Qinlong Wen | Changlong Tan
Source: Computational Materials Science (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

Screening for shape memory alloys with narrow thermal hysteresis using combined XGBoost and DFT calculation Shape-memory alloy ; Hysteresis ; Transformation (genetics) ; Materials science ; Eigenvalues and eigenvectors ; Thermal hysteresis ; Principal component analysis ; Process (computing) ; Doping ; Artificial intelligence ; Computer science ; Thermodynamics ; Chemistry ; Physics ; Condensed matter physics ; Optoelectronics Shape Memory Alloy Transformations ; Topology Optimization in Engineering
Authors: Weitian Wang | Ning Zhu | Zhenhai Wang | Chuxuan Zhao | Zihao Song | Xiaoliang Chen | Xing Chao
Source: Sensors and Actuators B Chemical (article, 2022-07-11)
Field: Chemistry — Spectroscopy

Efficient terahertz absorption gas sensor with Gaussian process regression in time- and frequency-domain Terahertz radiation ; Kriging ; Frequency domain ; Femtosecond ; Absorption (acoustics) ; Time domain ; Gaussian process ; Calibration ; Terahertz time-domain spectroscopy ; VNIR ; Materials science ; Optics ; Computer science ; Gaussian ; Laser ; Optoelectronics ; Physics ; Terahertz spectroscopy and technology Spectroscopy and Laser Applications ; Terahertz technology and applications ; Atmospheric Ozone and Climate
Authors: Zhiqiang Quan | Yuanjian Wan | Xiaoxiao Ma | Jian Wang
Source: Optics Express (article, 2022-09-01)
Field: Materials Science — Materials Chemistry

Nonvolatile multi-level adjustable optical switch based on the phase change material For the advantages of the faster computation speed and lower energy consumption, all-optical computation has attracted great attention compared with the traditional electric computation method. Optical switches are the critical elementary units of optical computation devices. However, the traditional optical switches have two shortcomings, expending the outside energy to keep the switch state and the weak multi-level adjustable ability, which greatly restrict the realization of the large-scale photonic integrated circuits and optical spiking neural networks. In this paper, we use a subwavelength grating slot-ridge (SWGSR) waveguides on the silicon platform to design a nonvolatile multi-level adjustable optical switch based on the phase change material Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST). Changing the phase state of GST can modulate the transmission of the optical switch, and the change of the optical transmittance of the optical switch is about 70%, which is much higher than that of previous optical switches. As no static power is required to maintain the phase state, it can find promising applications in optical switch matrices and reconfigurable optical spiking neural networks. Optical switch ; Optical transistor ; Optical computing ; Optical burst switching ; Photonics ; Transmittance ; Optical cross-connect ; Computer science ; Optical performance monitoring ; Computation ; Optics ; Optoelectronics ; Materials science ; Optical fiber ; Telecommunications ; Wavelength-division multiplexing ; Transistor ; Physics ; Voltage Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; Photonic and Optical Devices
Authors: Hongwei Niu | Yuhang Jing | Yi Sun | Licheng Guo | N. R. Aluru | Weiqi Li | Jianqun Yang | Xingji Li
Source: Solid State Ionics (article, 2022-01-29)
Field: Materials Science — Materials Chemistry

On the anomalous diffusion of proton in Y-doped BaZrO3 perovskite oxide Proton ; Perovskite (structure) ; Diffusion ; Molecular dynamics ; Oxide ; Ion ; Chemistry ; Chemical physics ; Doping ; Proton transport ; Hydroxide ; Materials science ; Physical chemistry ; Inorganic chemistry ; Crystallography ; Condensed matter physics ; Computational chemistry ; Thermodynamics ; Physics ; Nuclear physics Solid-state spectroscopy and crystallography ; Advancements in Solid Oxide Fuel Cells ; Perovskite Materials and Applications