jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Satoshi Minamoto | Koyo Daimaru | Masahiko Demura
Source: Science and Technology of Advanced Materials Methods (article, 2023-09-08)
Field: Materials Science — Materials Chemistry

MIOpt: optimization framework for backward problems on the basis of the concept of materials integration In materials design, it is very difficult to accurately design forward problems owing to the variety of scales and phenomena to be considered and the increasing number of input and output variables. In addition, it is necessary to reduce the number of computationally expensive calculations to perform backward problem to obtain the optimal set of parameters for the input to a forward problem. In this paper, we describe the development of the MIOpt (MI Optimizer), a framework for backward problem. The process – structure–property – performance relationship (PSPP relation) of materials is modeled as forward problems in MInt, and constructed in terms of materials engineering concepts. This can be linked to backward analysis via MIOpt, which greatly improves the efficiency of trial – and–error materials design on a computer. In addition, two proprietary optimization algorithms are implemented, allowing the use of an extension of Bayesian optimization and an update algorithm for machine learning models, which reduces the actual time required for optimization. The benchmark problems were the minimum search problem for the analytical function and the heat input condition optimization problem for creep rupture time in welds. Efficient Bayesian optimization method was confirmed to be faster and more stable than the conventional sequential Bayesian optimization method. Bayesian optimization ; Benchmark (surveying) ; Mathematical optimization ; Computer science ; Optimization problem ; Set (abstract data type) ; Basis (linear algebra) ; Extension (predicate logic) ; Process (computing) ; Relation (database) ; Algorithm ; Mathematics ; Data mining Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Advanced Multi-Objective Optimization Algorithms
Authors: Junru Zhou | Jiarui Feng | Xiyuan Wang | Muhan Zhang
Source: arXiv (Cornell University) (preprint, 2023-09-10)
Field: Materials Science — Materials Chemistry

Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power The ability of graph neural networks (GNNs) to count certain graph substructures, especially cycles, is important for the success of GNNs on a wide range of tasks. It has been recently used as a popular metric for evaluating the expressive power of GNNs. Many of the proposed GNN models with provable cycle counting power are based on subgraph GNNs, i.e., extracting a bag of subgraphs from the input graph, generating representations for each subgraph, and using them to augment the representation of the input graph. However, those methods require heavy preprocessing, and suffer from high time and memory costs. In this paper, we overcome the aforementioned limitations of subgraph GNNs by proposing a novel class of GNNs -- $d$-Distance-Restricted FWL(2) GNNs, or $d$-DRFWL(2) GNNs. $d$-DRFWL(2) GNNs use node pairs whose mutual distances are at most $d$ as the units for message passing to balance the expressive power and complexity. By performing message passing among distance-restricted node pairs in the original graph, $d$-DRFWL(2) GNNs avoid the expensive subgraph extraction operations in subgraph GNNs, making both the time and space complexity lower. We theoretically show that the discriminative power of $d$-DRFWL(2) GNNs strictly increases as $d$ increases. More importantly, $d$-DRFWL(2) GNNs have provably strong cycle counting power even with $d=2$: they can count all 3, 4, 5, 6-cycles. Since 6-cycles (e.g., benzene rings) are ubiquitous in organic molecules, being able to detect and count them is crucial for achieving robust and generalizable performance on molecular tasks. Experiments on both synthetic datasets and molecular datasets verify our theory. To the best of our knowledge, our model is the most efficient GNN model to date (both theoretically and empirically) that can count up to 6-cycles. Computer science ; Discriminative model ; Graph factorization ; Subgraph isomorphism problem ; Graph ; Theoretical computer science ; Artificial intelligence ; Line graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Dmitry G. Kvashnin | Viktor S. Baidyshev
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Study of Formation and Properties of F4tncq/Mos2 Heterostructures with Point Defects Driven by Dft and Neural Network Potential Heterojunction ; Artificial neural network ; Point (geometry) ; Materials science ; Biological system ; Computer science ; Neuroscience ; Psychology ; Artificial intelligence ; Mathematics ; Biology ; Optoelectronics ; Geometry Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; 2D Materials and Applications
Authors: Jian Sun
Source:  (preprint, 2023-02-22)
Field: Chemistry — Inorganic Chemistry

Unexpected new compounds and their states in the interior of giant planets predicted from first-principles calculations ABSTRACTIn this talk, I will introduce the methods developed in my group, especially the machine learning and graph theory aided crystal structure prediction method (MAGUS) [1]. In addition, I will show some applications of these methods combined with first-principles calculations, for instance, the predictions of new possible compounds (helium-water, helium-ammonia, helium-methane, helium-silica, silica-water, etc) in the interior of giant planets or exoplanets, and their exotic new states under planetary high-pressure and high-temperature conditions (superionic state, plastic state, and their coexistence) [2-6]. These new compounds and their states may have some important implications for giant planets, including demixing, magnetic field, erosion of the rocky core, etc.&amp;#160;REFERENCEKang Xia et al., &amp;#8220;A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search&amp;#8221;, Sci. Bull. 63, 817 (2018). Cong Liu et al., &amp;#8220;Mixed coordination silica at megabar pressure&amp;#8221;, Phys. Rev. Lett. 126, 035701 (2021). Cong Liu et al., &amp;#8220;Multiple superionic states in helium-water compounds&amp;#8221;, Nature Physics 15, 1065 (2019). Cong Liu et al., &amp;#8220;Plastic and Superionic Helium Ammonia Compounds under High Pressure and High Temperature&amp;#8221;, Phys. Rev. X 10, 021007 (2020). Hao Gao et al., &amp;#8220;Coexistence of plastic and partially diffusive phases in a helium-methane compound&amp;#8221;, Natl. Sci. Rev. 7, 1540 (2020). Hao Gao et al., &amp;#8220;Superionic Silica-Water and Silica-Hydrogen Compounds in the Deep Interiors of Uranus and Neptune&amp;#8221;, Phys. Rev. Lett. 128, 035702 (2022). Planet ; Gas giant ; Helium ; Uranus ; Chemistry ; Thermodynamics ; Physics ; Exoplanet ; Atomic physics ; Astrophysics Inorganic Fluorides and Related Compounds ; High-pressure geophysics and materials ; Quantum, superfluid, helium dynamics
Authors: E. V. Fomin
Source: Челябинский физико-математический журнал (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

STUDY OF THE TEMPERATURE DEPENDENCE OF THE SYMMETRICAL GRAIN BOUNDARY ENERGIES ON THE PLANE (110) IN ALUMINUM In this work the energy of symmetric tilt and twist grain boundaries in the range of grain misorientation angles from 0 to 180◦ and temperatures from 100 to 700 K in pure aluminum is investigated. The bicrystal systems with different grain tilt/twist angles are maintained at constant temperatures of 100, 400, or 700 K by molecular dynamic method and the energy of each grain boundary is calculated. The results show that the minimum grain boundary energy decreases as the temperature increases from 100 to 400 K; but the energy may decrease, remain practically unchanged, or even increase with further heating to 700 K. The average grain boundary energy obtained by averaging the energies of the resulting grain boundary structure variations at constant temperature grows with increasing temperature from 100 to 700 K for random boundaries with initially high energies. In the case of special grain boundaries with small Σ values, the average energy will be practically unchanged. To describe the continuous energy dependence of symmetric tilt and twist boundaries on temperature, an approximation by an forward propagation of artificial neural network is proposed. The neural network is trained and tested on atomistic simulation data and shows high predictive ability on test data and to describe the boundary energy in the temperature range from 100 to 700 K. Misorientation ; Grain boundary ; Twist ; Materials science ; Tilt (camera) ; Atmospheric temperature range ; Work (physics) ; Condensed matter physics ; Grain boundary strengthening ; Plane (geometry) ; Range (aeronautics) ; Boundary (topology) ; Aluminium ; Energy (signal processing) ; Thermodynamics ; Geometry ; Metallurgy ; Physics ; Composite material ; Mathematics ; Mathematical analysis ; Microstructure ; Quantum mechanics Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Aluminum Alloy Microstructure Properties
Authors: Vigneswara Reddy K. | Avvaru N. Suhasini | V. V. S. S. S. Balaram
Source: International Journal on Recent and Innovation Trends in Computing and Communication (article, 2023-09-01)
Field: Chemistry — Analytical Chemistry

Comparative Analysis of Fruit Disease Identification Methods: A Comprehensive Study The need for accurate and efficient technologies for recognising and controlling fruit diseases has increased due to the rising global demand for high-quality agricultural products. This study focuses on the advantages, disadvantages, and potential practical applications of a range of methods for identifying fecundities. Thanks to developments like improved imaging, machine learning, and data analysis tools, old methods of disease diagnosis have altered as technology has developed. The study compares older methods like visual observation, manual symptom correlation, spectroscopy, and chemical procedures with more contemporary methods like computer vision, autonomous learning algorithms, and sensor-based technologies. Precision, efficiency, cost, scalability, and ease of use are used to describe each method's effectiveness. The article reviews the research examples and practical applications of fruit endocrine disease detection in different cultivars and areas to provide a thorough comparison. This comparison focuses on the variations in disease prevalence and the ways that alternative treatments can be customised to certain situations.It is for this reason that this study offers useful information on how the methods for detecting fruit rot have evolved through time. It emphasises the significance of utilising technological advances to enhance the accuracy, effectiveness, and long-term sustainability of the management of agricultural diseases. Based on the unique requirements of their various agricultural systems, this analysis can assist researchers, practitioners, and policymakers in selecting the most effective methods for identifying fruit diseases. Computer science ; Identification (biology) ; Scalability ; Data science ; Sustainability ; Risk analysis (engineering) ; Agriculture ; Management science ; Machine learning ; Artificial intelligence ; Medicine ; Engineering ; Database Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Pengchao Zhang | Axel Tosello Gardini | Xuefei Xu | Michele Parrinello
Source: arXiv (Cornell University) (preprint, 2023-11-10)
Field: Chemistry — Spectroscopy

Intramolecular and water mediated tautomerism of solvated glycine The understanding of prototropic tautomerism in water and the characterization of solvent effects on protomeric equilibrium pose significant challenges. Using molecular dynamics simulations based on state-of-the-art deep learning potential and enhanced sampling methods, we provide a comprehensive description of all configurational transformations in glycine solvated in water and determine accurate free energy profiles of these processes. We observe that the tautomerism between the neutral and zwitterionic forms of solvated glycine can occur by both intramolecular proton transfer in glycine and intermolecular proton transfer in the contact ion pair (anionic glycine and hydronium ion) or the separated ion pair (cationic glycine and hydroxide ion). Tautomer ; Intramolecular force ; Chemistry ; Glycine ; Proton ; Intermolecular force ; Cationic polymerization ; Ion ; Hydroxide ; Hydrogen bond ; Umbrella sampling ; Solvation ; Hydronium ; Solvent ; Computational chemistry ; Photochemistry ; Molecular dynamics ; Molecule ; Inorganic chemistry ; Stereochemistry ; Organic chemistry ; Amino acid Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Crystallography and molecular interactions
Authors: Wei Lü | Nic A. Lee | Markus J. Buehler
Source: arXiv (Cornell University) (preprint, 2023-04-11)
Field: Materials Science — Biomaterials

Modeling and design of heterogeneous hierarchical bioinspired spider web structures using generative deep learning and additive manufacturing Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here we provide a detailed analysis of the heterogenous graph structures of spider webs, and use deep learning as a way to model and then synthesize artificial, bio-inspired 3D web structures. The generative AI models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation, 2) a discrete diffusion model with full neighbor representation, and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bio-inspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles towards integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties. Computer science ; Generative model ; Scalability ; Artificial intelligence ; Representation (politics) ; Generative grammar ; Theoretical computer science ; Database Silk-based biomaterials and applications ; Insect and Arachnid Ecology and Behavior ; Paleontology and Evolutionary Biology
Authors: Haihui Zhang | Biao Wu | Cheng-Hui Xia | Lanting Zhang | Hong Wang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Efficient Thermodynamic Model Optimization and Uncertainty Quantification Via Integration of Combinatorial Materials Chip and Bayesian Approach Bayesian optimization ; Bayesian probability ; Chip ; Bayesian inference ; Uncertainty quantification ; Computer science ; Biological system ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Engineering ; Biology Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Laurie Winkless
Source: Materials Today (article, 2023-05-29)
Field: Materials Science — Materials Chemistry

The role of water in making rubber Computer science ; Nonlinear system ; Hierarchy ; Deformation (meteorology) ; Throughput ; Inverse ; Materials science ; Mathematics ; Physics ; Geometry Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Phase Equilibria and Thermodynamics
Authors: Andrews Nirmala Grace | Mohammed Shariq | Sathish Marimuthu | Amit Rai Dixit | Somnath Chattopadhyaya | Saravanan Pandiaraj | Muthumareeswaran MR | Abdullah N. Alodhayb | Mohammad Khaja Nazeeruddin
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning Models for Prediction of Electrochemical Properties in Supercapacitor Electrodes Using Mxene and Graphene Nanoplatelets Supercapacitor ; Graphene ; Materials science ; Electrochemistry ; Electrode ; Nanotechnology ; Chemistry Supercapacitor Materials and Fabrication
Authors: yisonglv yisonglv | yisong lv | Shujie Liu | Jinping Tian | Chongrong Mou
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Neural Network-Enabled Bifunctional Terahertz Metasurface Design for Absorption and Polarization Conversion Terahertz radiation ; Bifunctional ; Polarization (electrochemistry) ; Materials science ; Absorption (acoustics) ; Artificial neural network ; Optoelectronics ; Computer science ; Optics ; Physics ; Artificial intelligence ; Chemistry Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Millimeter-Wave Propagation and Modeling
Authors: A. V. Krautsou | О. Н. Шорникова | В. В. Авдеев
Source: Журнал физической химии (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Using a Neural Network to Study the Effect of the Means of Synthesizing Exfoliated Graphite on Its Macropore Structure Graphite intercalated compounds (GICs) with different stage numbers are prepared chemically from highly oriented pyrolytic graphite (HOPG), natural flaked graphite (FG) and nitric acid. Exfoliated graphite samples (EG-T) are synthesized from GICs via water treatment followed by thermal shock. The aim of this work is to investigate the dependence of the inner EG-T pore structure on the extent of oxidation and type of graphite by processing scanning electron microscopy (SEM) micrographs of EG-T cross sections. A procedure is developed on the basis of a deep convolutional neural network that speeds up image processing with no appreciable loss of accuracy. A strong correlation is found between EG-T pore structure parameters, the depth of oxidation, and the type of graphite. Macropore ; Graphite ; Artificial neural network ; Materials science ; Chemical engineering ; Artificial intelligence ; Computer science ; Chemistry ; Composite material ; Mesoporous material ; Engineering ; Organic chemistry Graphite, nuclear technology, radiation studies ; Fiber-reinforced polymer composites ; Graphene research and applications
Authors: Pezhman Abbasi | Ehsan Bahrami Moghadam
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Electrochemistry

Electrochemical Decomposition of Ciprofloxacin Via Boron-Doped Diamond (Bdd) Anode, and Performance Prediction Using Artificial Neural Network (Ann) and Least Squares Support Vector Machine (Lssvm) Artificial neural network ; Anode ; Least squares support vector machine ; Diamond ; Support vector machine ; Electrochemistry ; Least-squares function approximation ; Materials science ; Decomposition ; Artificial intelligence ; Computer science ; Biological system ; Pattern recognition (psychology) ; Mathematics ; Metallurgy ; Statistics ; Chemistry ; Electrode ; Biology ; Physical chemistry Electrochemical Analysis and Applications ; Membrane-based Ion Separation Techniques ; Covalent Organic Framework Applications
Authors: Rui Zhang | Jiawang Zhang | Yuexing Han | Qiaochuan Chen
Source:  (article, 2023-06-23)
Field: Materials Science — Materials Chemistry

Material Property Prediction by Integrating Literature-mining and Crossed Feature Selection The data-driven development of materials is currently one of the research hotspots and is considered the fourth paradigm in material research, enabling rapid material development. As a data-driven approach, machine learning relies heavily on feature selection, which can construct the most relevant feature set from a large number of features, providing more accurate models for research. Scientific literature, as an important way to showcase research achievements, contains valuable data. In this paper, a property prediction method that integrates literature mining and crossed feature selection is proposed, which can fully utilize the information in literature and address the issues of limited data and redundant features in data-driven material development. Named entity recognition and image processing techniques are used to mine material composition and tensile strength from literature text and tables, and the crossed feature compression and selection method is used to screen the expanded material composition features in the XenonPy materials informatics library. Finally, using the Extreme Gradient Boosting, a tensile strength prediction model is trained on the selected features and tensile strength data. The highest R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for experiments with data from the National Institute for Materials Science is 0.6671, indicating an improvement of 11.42%. This demonstrates that the proposed method can significantly improve model prediction performance and literature mining can provide strong support for material development. Feature selection ; Computer science ; Data mining ; Property (philosophy) ; Artificial intelligence ; Feature engineering ; Feature (linguistics) ; Feature extraction ; Machine learning ; Materials informatics ; Selection (genetic algorithm) ; Predictive modelling ; Information retrieval ; Deep learning ; Health informatics ; Engineering informatics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Handwritten Text Recognition Techniques