jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Caibo Dong | Dazi Li | Jun Liu
Source: Langmuir (article, 2024-08-21)
Field: Materials Science — Materials Chemistry

Glass Transition Temperature Prediction of Polymers via Graph Reinforcement Learning An expansive array of graph-based models has been utilized for accurate prediction of the structure-property relation of polymers. However, these approaches notably underutilize unsupervised structural information. Concentrating on the domain of heterocyclic polymers, particularly polyimides, this study delves into the glass transition temperature (<i>T</i><sub>g</sub>) prediction, aiming to fully exploit the potential within both the global and local structures of molecules. To achieve this, a graph reinforcement learning framework termed Molecular Structural Regularized Graph Convolutional Network with Reinforcement Learning (MSRGCN-RL) is proposed. Experimental results highlight the crucial role of both global and local structural regularization in precise <i>T</i><sub>g</sub> prediction. Concurrently, optimization of MSRGCN training through RL proves essential. This research leads the way in integrating Graph Neural Networks (GNNs) with reinforcement learning methodologies for the property prediction of polymers. Glass transition ; Expansive ; Polymer ; Graph ; Reinforcement learning ; Materials science ; Computer science ; Artificial intelligence ; Composite material ; Theoretical computer science Machine Learning in Materials Science ; Polymer crystallization and properties ; Epoxy Resin Curing Processes
Authors: Xiaofan Huang | Chengzhi Li | Minhui Yuan | Jing Shuai | Xiangguo Li | Yanglong Hou
Source: Materials Today Physics (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Unphysical grain size dependence of lattice thermal conductivity in Mg3(Sb, Bi)2: An atomistic view of concentration dependent segregation effects Materials science ; Thermal conductivity ; Grain boundary ; Grain size ; Thermoelectric effect ; Thermoelectric materials ; Crystallite ; Lattice (music) ; Condensed matter physics ; Thermal ; Thermal conduction ; Thermodynamics ; Microstructure ; Metallurgy ; Composite material ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; nanoparticles nucleation surface interactions
Authors: Jirui Jin | Somayeh Faraji | Bin Liu | Mingjie Liu
Source: The Journal of Physical Chemistry C (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Comparative Analysis of Conventional Machine Learning and Graph Neural Network Models for Perovskite Property Prediction Perovskite materials, renowned for their versatility and remarkable properties, pose challenges in discovering optimal candidates due to the vast compositional space. Data-driven machine learning (ML) offers promise in expediting material discovery; however, the trade-off between accuracy and efficiency across different ML models for predicting perovskite properties is not well-understood. In this study, we conducted a comprehensive assessment of various ML models for predicting the formation energy (Ef) and band gap (Eg) of perovskites. We designed a protocol to extract perovskite structures from three databases based on the stoichiometry, octahedral lattice motif, and alignment with established perovskite prototype structures. Benchmarking conventional ML algorithms (CML) against graph neural network (GNN) models across three data sets, we identified LGBM and GATGNN models as the top performers for CML and GNN, respectively, balancing exceptional prediction accuracy and computational efficiency. We further investigated the impact of the data size on model performance, emphasizing the need for over 1000 data points for optimal prediction accuracy. Additionally, through SHAP analysis, we provided valuable insights into the interpretation of CML models in predicting Ef and Eg. Our study establishes a standardized benchmark for evaluating various ML models across diverse data sets of perovskite materials, facilitating future applications in materials science, particularly in model selection and advancement of perovskite materials. Artificial neural network ; Computer science ; Property (philosophy) ; Artificial intelligence ; Graph ; Machine learning ; Theoretical computer science ; Philosophy Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advancements in Solid Oxide Fuel Cells
Authors: Chang Liu | Hiromasa Tamaki | Tomoyasu Yokoyama | Kensuke Wakasugi | Satoshi Yotsuhashi | Minoru KUSABA | Artem R. Oganov | Ryo Yoshida
Source: npj Computational Materials (article, 2024-12-20)
Field: Materials Science — Materials Chemistry

Shotgun crystal structure prediction using machine-learned formation energies Abstract Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is less computationally intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures. Crystal structure prediction ; Maxima and minima ; Metastability ; Crystal (programming language) ; Benchmark (surveying) ; Crystal structure ; Computer science ; Symmetry (geometry) ; Artificial intelligence ; Materials science ; Algorithm ; Machine learning ; Crystallography ; Physics ; Chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Emil A. Aydinsoy | Zaur Z. Aghamali̇yev | Durna B. Aghamaliyeva | Vasif B. Abbasov
Source: PPOR (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

A systematic review of corrosion inhibitors in marine environments: insights from the last 5 years Corrosion inhibitors are crucial for protecting metals and alloys in marine conditions, where corrosion poses significant risks to infrastructure and equipment.This systematic review investigates the most recent advancements in the development and application of corrosion inhibitors in several industries, such as oil and gas [1-3], desalination [4][5][6], and shipping [7][8][9].Various types of inhibitors, including organic [10,11], inorganic [12,13], hybrid [14], and green inhibitors [15][16][17], are evaluated for their mechanisms of action [18][19][20], effectiveness [21,22], and environmental impacts [23][24][25].This review assesses the efficacy of several inhibitors on distinct metal substrates [26][27][28] under varying environmental circumstances [29][30][31].The focus is on technological progressions such as self-healing coatings [32,33], intelligent materials [34,35], and nanotechnology-based solutions [36,37].The effectiveness of these inhibitors is assessed using traditional electrochemical methods [38][39][40] as well as state-of-the-art machine learning algorithms [41,42], which offer new insights in predicting corrosion patterns.The article examines the main challenges of transitioning from laboratory research to field implementation [43,44], adhering to regulatory constraints [45,46], and attaining cost-efficiency [47,48].This study aims to rectify the shortcomings in the current understanding by integrating the most recent findings from influential studies [49][50][51].The book proposes potential avenues for future investigation focused on developing corrosion mitigation devices that are both ecologically sustainable and exceptionally efficient, specifically tailored for demanding marine environments Corrosion ; Materials science ; Metallurgy Corrosion Behavior and Inhibition
Authors: Suman Hazra | Urvesh Patil | Stefano Sanvito
Source: Journal of Chemical Theory and Computation (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Predicting the One-Particle Density Matrix with Machine Learning Two of the most widely used electronic-structure theory methods, namely, Hartree-Fock and Kohn-Sham density functional theory, require the iterative solution of a set of Schrödinger-like equations. The speed of convergence of such a process depends on the complexity of the system under investigation, the self-consistent-field algorithm employed, and the initial guess for the density matrix. An initial density matrix close to the ground-state matrix will effectively allow one to cut out many of the self-consistent steps necessary to achieve convergence. Here, we predict the density matrix of Kohn-Sham density functional theory by constructing a neural network that uses only the atomic positions as information. Such a neural network provides an initial guess for the density matrix far superior to that of any other recipes available. Furthermore, the quality of such a neural-network density matrix is good enough for the evaluation of interatomic forces. This allows us to run accelerated ab initio molecular dynamics with little to no self-consistent steps. Particle (ecology) ; Computer science ; Matrix (chemical analysis) ; Artificial intelligence ; Machine learning ; Materials science ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Physical and Chemical Molecular Interactions
Authors: Meicen Fan | Tongqi Wen | Shile Chen | Yanhao Dong | Chang‐An Wang
Source: Advanced Science (article, 2024-04-06)
Field: Materials Science — Ceramics and Composites

Perspectives Toward Damage‐Tolerant Nanostructure Ceramics Advanced ceramic materials and devices call for better reliability and damage tolerance. In addition to their strong bonding nature, there are examples demonstrating superior mechanical properties of nanostructure ceramics, such as damage-tolerant ceramic aerogels that can withstand high deformation without cracking and local plasticity in dense nanocrystalline ceramics. The recent progresses shall be reviewed in this perspective article. Three topics including highly elastic nano-fibrous ceramic aerogels, load-bearing nanoceramics with improved mechanical properties, and implementing machine learning-assisted simulations toolbox in understanding the relationship among structure, deformation mechanisms, and microstructure-properties shall be discussed. It is hoped that the perspectives present here can help the discovery, synthesis, and processing of future structural ceramic materials that are insensitive to processing flaws and local damages in service. Ceramic ; Materials science ; Nanostructure ; Nanocrystalline material ; Damage tolerance ; Microstructure ; Deformation (meteorology) ; Nanotechnology ; Composite material ; Composite number Advanced ceramic materials synthesis ; Advanced materials and composites ; Electronic and Structural Properties of Oxides
Authors: Hyuna Kwon | Tim Hsu | Wenyu Sun | Wonseok Jeong | Fikret Aydin | James Chapman | Xiaohong Chen | Vincenzo Lordi | Matthew R. Carbone | Deyu Lu | Fei Zhou | Tuan Anh Pham
Source: Machine Learning Science and Technology (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Spectroscopy-guided discovery of three-dimensional structures of disordered materials with diffusion models Abstract Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon ( a -C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a -C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted. XANES ; Spectroscopy ; X-ray absorption spectroscopy ; Atomic units ; Materials science ; Computer science ; Absorption spectroscopy ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Makdud Islam | Akhouri Sanjay Kumar Sinha | K. Prasad
Source: Cellulose (article, 2024-09-02)
Field: Materials Science — Biomaterials

Organosolv delignification of rice straw cellulose fiber for functional food packaging Organosolv ; Cellulose ; Straw ; Cellulose fiber ; Fiber ; Rice straw ; Pulp and paper industry ; Lignin ; Materials science ; Food packaging ; Composite material ; Food science ; Chemistry ; Organic chemistry ; Engineering Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Material Properties and Processing
Authors: Daria Maksimovna Vakhrusheva | Jun Xu
Source: Batteries & Supercaps (review, 2024-10-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review Abstract The rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions. Energy density ; Computer science ; Automotive engineering ; Engineering physics ; Engineering Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Recycling and Waste Management Techniques
Authors: Chuqiao Chen | Viviana Palacio‐Betancur | Sepideh Norouzi | Pablo F. Zubieta Rico | Nina Chang | Monirosadat Sadati | Stuart J. Rowan | Juan Pablo
Source: Chemistry of Materials (article, 2024-03-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

LCPOM: Precise Reconstruction of Polarized Optical Microscopy Images of Liquid Crystals When examined with polarized optical microscopy (POM), liquid crystals display interference colors and complex patterns that depend on the material’s microscopic orientation. That orientation can be manipulated by the application of external fields, a feature that provides the basis for applications in optical display and sensing technologies. The color patterns themselves have high information content. Traditionally, however, calculations of the optical appearance of liquid crystals have been performed by assuming that a single-wavelength light source is employed and reported on a monochromatic scale. In this work, the original Jones matrix method is extended to calculate the colored images that arise when a liquid crystal is exposed to a multiwavelength source. By accounting for the material properties, including the local orientation, the visible light spectrum, and the CIE (International Commission on Illumination) color matching functions, we demonstrate that the proposed approach produces colored POM images that are in quantitative agreement with experimental data. Results are presented for a variety of systems, including radial, bipolar, and cholesteric droplets, where results of simulations are compared with experimental images. The effects of the droplet size, topological defect structure, and droplet orientation are examined systematically. The technique introduced here generates images that can be directly compared to experiments, thereby facilitating machine learning efforts aimed at interpreting LC microscopy images and paving the way for the inverse design of materials capable of producing specific internal microstructures in response to external stimuli. Microscopy ; Optical microscope ; Materials science ; Optics ; Polarized light microscopy ; Liquid crystal ; Crystallography ; Optoelectronics ; Chemistry ; Physics ; Scanning electron microscope Liquid Crystal Research Advancements ; Optical Polarization and Ellipsometry ; Spectroscopy and Chemometric Analyses
Authors: Haonan Wang | Peipei Xu | Yi‐Ting Chen | Chuyi Wang | Shurui Chen | Junjie Ren | Yudong Lu | Jingbo Chen | Li Zhang | Yunzhen Liu | Ruiyun You
Source: Biosensors and Bioelectronics (article, 2024-09-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

“Partner” cellulose gel with “dialysis” function: Achieve the integration of filtration-enrichment-SERS detection Aerogel ; Adsorption ; Materials science ; Substrate (aquarium) ; Nanotechnology ; Nanoporous ; Molecule ; Nanoparticle ; Biomolecule ; Macromolecule ; Chemical engineering ; Chemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Quantum Dots Synthesis And Properties
Authors: Tiantian Cheng | Weizhi Meng | Man Luo | Jiachi Xian | Wenjin Luo | Weijun Wang | Fangyu Yue | Johnny C. Ho | Chenhui Yu | Junhao Chu
Source: Small (review, 2024-07-19)
Field: Materials Science — Materials Chemistry

Advancements and Challenges in the Integration of Indium Arsenide and Van der Waals Heterostructures The strategic integration of low-dimensional InAs-based materials and emerging van der Waals systems is advancing in various scientific fields, including electronics, optics, and magnetics. With their unique properties, these InAs-based van der Waals materials and devices promise further miniaturization of semiconductor devices in line with Moore's Law. However, progress in this area lags behind other 2D materials like graphene and boron nitride. Challenges include synthesizing pure crystalline phase InAs nanostructures and single-atomic-layer 2D InAs films, both vital for advanced van der Waals heterostructures. Also, diverse surface state effects on InAs-based van der Waals devices complicate their performance evaluation. This review discusses the experimental advances in the van der Waals epitaxy of InAs-based materials and the working principles of InAs-based van der Waals devices. Theoretical achievements in understanding and guiding the design of InAs-based van der Waals systems are highlighted. Focusing on advancing novel selective area growth and remote epitaxy, exploring multi-functional applications, and incorporating deep learning into first-principles calculations are proposed. These initiatives aim to overcome existing bottlenecks and accelerate transformative advancements in integrating InAs and van der Waals heterostructures. van der Waals force ; Nanotechnology ; Heterojunction ; Materials science ; Graphene ; Engineering physics ; Optoelectronics ; Physics ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Lijun Pan | Jesús Carrete | Zhao Wang | Georg K. H. Madsen
Source: Physical review. B./Physical review. B (article, 2024-01-16)
Field: Materials Science — Materials Chemistry

Machine learning boosted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>a</mml:mi><mml:mi>b</mml:mi></mml:mrow></mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi></mml:mrow></mml:math> study of the thermal conductivity of Janus PtSTe van der Waals heterostructures Calculating the thermal conductivity of heterostructures with multiple layers presents a significant challenge for state-of-the-art ab initio methods. In this study we introduce an efficient neural-network force field (NNFF) to explore the thermal transport characteristics of van der Waals heterostructures based on PtSTe, using both the phonon Boltzmann transport equation and molecular dynamics (MD) simulations. Besides demonstrating a remarkable level of agreement with both theoretical and experimental data, our predictions reveal that heterogeneous combinations like $\mathrm{PtSTe}\text{\ensuremath{-}}{\mathrm{PtTe}}_{2}$ display a notable reduction in thermal conductivity at room temperature, primarily due to broken out-of-plane symmetries and the presence of weak van der Waals interactions. Furthermore, our study highlights the superiority of MD simulations with NNFFs in capturing higher-order anharmonic phonon properties. This is demonstrated through the analysis of the temperature-dependent thermal conductivity curves of PtSTe-based van der Waals heterostructures and advances our understanding of phonon transport in those materials. van der Waals force ; Thermal conductivity ; Boltzmann equation ; Phonon ; Physics ; Machine learning ; Ab initio ; Condensed matter physics ; Thermodynamics ; Quantum mechanics ; Computer science ; Molecule Thermal properties of materials ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Mohamed H. Saad | Sherief Hashima | Ahmed I. Omar | Mostafa M. Fouda | Abdelrahman Said
Source: Electrical Engineering (article, 2024-07-16)
Field: Materials Science — Materials Chemistry

Deep learning approach for cable partial discharge pattern identification Partial discharge ; Identification (biology) ; Computer science ; Electrical engineering ; Engineering ; Electronic engineering ; Artificial intelligence ; Voltage High voltage insulation and dielectric phenomena ; Advanced Fiber Optic Sensors ; Electrostatic Discharge in Electronics