jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Youtian Li | Yangyu Guo | Shiyun Xiong | Hong-Liang Yi
Source: International Journal of Heat and Mass Transfer (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Enhanced heat transport in amorphous silicon via microstructure modulation Materials science ; Amorphous solid ; Annealing (glass) ; Microstructure ; Thermal conductivity ; Amorphous silicon ; Substrate (aquarium) ; Atmospheric temperature range ; Optoelectronics ; Silicon ; Nanotechnology ; Chemical physics ; Composite material ; Crystalline silicon ; Thermodynamics ; Crystallography ; Chemistry Thermal properties of materials ; Phase-change materials and chalcogenides ; Thin-Film Transistor Technologies
Authors: Pedro Mário Cruz e Silva | Guilherme Fidelis Peixer | Anderson Lorenzoni | Yan Azeredo | Rodolfo C.C. Flesch | Jaime A. Lozano | Jader R. Barbosa
Source: Applied Thermal Engineering (article, 2024-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Predicting the dynamic behavior of a magnetocaloric cooling prototype via artificial neural networks Artificial neural network ; Magnetic refrigeration ; Materials science ; Artificial intelligence ; Mechanical engineering ; Computer science ; Control engineering ; Engineering ; Physics Magnetic and transport properties of perovskites and related materials
Authors: Seunghee Han | Byoung Gwan Lee | Dae‐Woon Lim | Jihan Kim
Source: Chemistry of Materials (article, 2024-11-06)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Based Prediction of Proton Conductivity in Metal–Organic Frameworks Recently, metal–organic frameworks (MOFs) have demonstrated their potential as solid-state electrolytes in proton exchanged membrane fuel cells. However, the number of MOFs reported to exhibit proton conductivity remains limited, and the mechanisms underlying this phenomenon have not been fully elucidated, complicating the design of proton-conductive MOFs. In response, we developed a comprehensive database of proton-conductive MOFs and applied machine learning techniques to predict their proton conductivity. Our approach included the construction of both descriptor-based and transformer-based models. Notably, the transformer-based transfer learning (Freeze) model performed the best with a mean absolute error (MAE) of 0.91, suggesting that the proton conductivity of MOFs can be estimated within 1 order of magnitude using this model. Additionally, we employed feature importance and principal component analysis to explore the factors influencing the proton conductivity. The insights gained from our database and machine learning model are expected to facilitate the targeted design of proton-conductive MOFs. Conductivity ; Proton ; Metal-organic framework ; Materials science ; Metal ; Artificial intelligence ; Computer science ; Nanotechnology ; Chemistry ; Physics ; Organic chemistry ; Metallurgy ; Physical chemistry ; Adsorption ; Nuclear physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Electrochemical Analysis and Applications
Authors: Kimia Ghaffari | Salil Bavdekar | Douglas E. Spearot | Ghatu Subhash
Source: Computational Materials Science (preprint, 2024-04-01)
Field: Materials Science — Materials Chemistry

Validation workflow for machine learning interatomic potentials for complex ceramics Workflow ; Ceramic ; Computer science ; Interatomic potential ; Materials science ; Chemistry ; Database ; Molecular dynamics ; Metallurgy ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced X-ray and CT Imaging
Authors: Timo Laakko | Antti Korkealaakso | Burcu Firatligil‐Yildirir | Piotr Batys | Ville Liljeström | Ari Hokkanen | Nonappa Nonappa | Merja Penttilä | Anssi Laukkanen | Ali Miserez | Caj Södergård | Pezhman Mohammadi
Source: Advanced Materials (article, 2024-05-06)
Field: Materials Science — Biomaterials

Accelerated Engineering of ELP‐Based Materials through Hybrid Biomimetic‐De Novo Predictive Molecular Design Efforts to engineer high-performance protein-based materials inspired by nature have mostly focused on altering naturally occurring sequences to confer the desired functionalities, whereas de novo design lags significantly behind and calls for unconventional innovative approaches. Here, using partially disordered elastin-like polypeptides (ELPs) as initial building blocks this work shows that de novo engineering of protein materials can be accelerated through hybrid biomimetic design, which this work achieves by integrating computational modeling, deep neural network, and recombinant DNA technology. This generalizable approach involves incorporating a series of de novo-designed sequences with α-helical conformation and genetically encoding them into biologically inspired intrinsically disordered repeating motifs. The new ELP variants maintain structural conformation and showed tunable supramolecular self-assembly out of thermal equilibrium with phase behavior in vitro. This work illustrates the effective translation of the predicted molecular designs in structural and functional materials. The proposed methodology can be applied to a broad range of partially disordered biomacromolecules and potentially pave the way toward the discovery of novel structural proteins. Materials science ; Nanotechnology Silk-based biomaterials and applications ; Supramolecular Self-Assembly in Materials ; Corrosion Behavior and Inhibition
Authors: Xujie Gong | Ruize Sun | Ruichao Lei | Xue Jiang | Yanjing Su | Yu Yan
Source: Corrosion Science (article, 2024-03-01)
Field: Materials Science — Metals and Alloys

Iterative multi-objective design of hydrogen embrittlement resistant high-strength steels using Bayesian optimization Hydrogen embrittlement ; Materials science ; Ultimate tensile strength ; Toughness ; Austenite ; Elongation ; Hydrogen ; Bayesian optimization ; Metallurgy ; Embrittlement ; Hardenability ; Composite material ; Corrosion ; Computer science ; Microstructure ; Machine learning Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Welding Techniques and Residual Stresses
Authors: Rafael G. Uceda | Alfonso Gijón | Sandra Míguez‐Lago | Carlos M. Cruz | Víctor Blanco | Fátima Fernández-Álvarez | Luı́s Álvarez de Cienfuegos | Miguel Molina-Solana | Juan Gómez‐Romero | D. Miguel | Antonio J. Mota | Juan M. Cuerva
Source: Angewandte Chemie International Edition (article, 2024-09-27)
Field: Chemistry — Organic Chemistry

Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case The relationship between chemical structure and chiroptical properties is not always clearly understood. Nowadays, efforts to develop new systems with enhanced optical properties follow the trial-error method. A large number of data would allow us to obtain more robust conclusions and guide research toward molecules with practical applications. In this sense, in this work we predict the chiroptical properties of millions of halogenated [6]helicenes in terms of the rotatory strength (R). We have used DFT calculations to randomly create derivatives including from 1 to 16 halogen atoms, that were then used as a data set to train different deep neural network models. These models allow us to i) predict the R<sub>max</sub> for any halogenated [6]helicene with a very low computational cost, and ii) to understand the physical reasons that favour some substitutions over others. Finally, we synthesized derivatives with higher predicted R<sub>max</sub> obtaining excellent correlation among the values obtained experimentally and the predicted ones. Helicene ; Halogen ; Artificial neural network ; Molecule ; Work (physics) ; Computational chemistry ; Computer science ; Chemistry ; Artificial intelligence ; Machine learning ; Physics ; Organic chemistry ; Thermodynamics Synthesis and Properties of Aromatic Compounds ; Molecular spectroscopy and chirality ; Fullerene Chemistry and Applications
Authors: Jian Chang | Shuze Zhu
Source: Advanced Intelligent Systems (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

Deep Learning on Atomistic Physical Fields of Graphene for Strain and Defect Engineering Strain and defect engineering have profound applications in two‐dimensional materials, where it is important to determine the equilibrated atomistic structures with defect conditions under mechanical deformations for computational materials design. Nevertheless, how to efficiently predict relaxed atomistic structures and the associated physical fields on each atom or bond under evolving mechanical deformations remains as an essential challenge. To address this issue, a deep neural network architecture is designed to embed the state of applied strains into the defect‐engineered atomistic geometry, so that deformation‐coupled physical fields of interests on atoms or bonds can be predicted or derived over continuous state of deformations. For demonstration, the combination of applied tensile strains and shear strain on monolayer graphene with random distribution of Stone–Wales defects and vacancy defects is considered. The unique advantage of this framework is the development of strain‐embedding concept combined with generative adversarial network, which can be feasibly extended to other material and other conditions. The computational approach sheds light on boosting the efficiency of evaluating physical properties of 2D materials under complex strain and defect states. Materials science ; Strain engineering ; Graphene ; Deformation (meteorology) ; Vacancy defect ; Generative grammar ; Embedding ; Statistical physics ; Nanotechnology ; Artificial intelligence ; Condensed matter physics ; Computer science ; Composite material ; Physics Machine Learning in Materials Science ; Graphene research and applications ; Thermal properties of materials
Authors: Anzhe Wang | Hao He | Wenzheng Xiong | Shuai Wang | Jicheng Liu | Haishen Xie | Yajie Dai
Source: Journal of the European Ceramic Society (article, 2024-09-06)
Field: Materials Science — Ceramics and Composites

Intelligent prediction of Si3N4 ceramic strength considering inherent defect characteristics Materials science ; Ceramic ; Composite material ; Engineering physics ; Engineering Advanced ceramic materials synthesis ; Advanced materials and composites ; Advanced Surface Polishing Techniques
Authors: Hossein Mashhadimoslem | Peyman Karimi | Mohammad Ali Abdol | Kourosh Zanganeh | Ahmed Shafeen | Ali A. AlHammadi | Ali Elkamel
Source: Industrial & Engineering Chemistry Research (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

The Role of Transition Metals on CeO<sub>2</sub>Supported for CO<sub>2</sub>Adsorption by DFT and Machine Learning Analysis Catalyst design is a field where machine learning (ML) algorithms have found many useful applications using atomistic simulation data sets. Atomistic simulation of CO2 adsorption energy using several transition metals on ceria oxide (CeO2) catalysts is the subject of our research. The density functional theory (DFT) calculation was used, and its results were applied as a data set to train several ML algorithms. Gibbs free energy (ΔG) was simulated for all TMs such as Ni, Fe, Cu, Co, Mo, Ru, Rh, Pd, Ag, Pt, Zr, and Ti and used as the goal of CO2 adsorption prediction using ML algorithms. The XGBoost algorithm provided satisfactory predictions of ΔG using different transition metals and bond energy at different adsorption temperatures on the catalysts. The effective role of Cu, Pt, Fe, and Ni in all temperature ranges regarding the increase of CO2 adsorption energy was evident. The best CO2 adsorption efficiency will be achieved by Ni at temperatures below 300 K and Cu at temperatures over 300 K. Evaluation of new catalysts using prediction findings and correlations between complex DFT simulation parameters demonstrates the potential of ML as a powerful tool for industry development. Adsorption ; Catalysis ; Transition metal ; Gibbs free energy ; Density functional theory ; Materials science ; Oxide ; Molecular dynamics ; Thermodynamics ; Physical chemistry ; Chemistry ; Computational chemistry ; Metallurgy ; Physics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Jungi Lee | Myounghwan Kim | Jeongyoon Yoon | Kwangsun Yoo | Seok-Joo Byun
Source: IEEE Access (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

PA2E: Real-Time Anomaly Detection With Hyperspectral Imaging for Food Safety Inspection Hyperspectral imaging captures material-specific spectral data, making it effective for detecting contaminants in food that are challenging to identify using conventional methods. In the food industry, the occurrence of unknown contaminants is problematic due to the difficulty in obtaining training data, highlighting the need for anomaly detection algorithms that can identify previously unseen contaminants by learning from normal data. Deep learning techniques for anomaly detection have been extended to hyperspectral imaging. However, existing methods often use fully connected layers that do not leverage local spectral similarities or convolutional layers that overlook positional importance due to transform invariance, leading to performance limitations. Furthermore, practical hyperspectral contaminant detection in food requires significant real-time processing capabilities, hindered by the computational inefficiency of convolution operations and the limitations of existing deep neural network architectures. To address these issues, we propose a novel model called Partial and Aggregate Autoencoder (PA2E), which incorporates local information without transform invariance and utilizes masked fully connected layers to achieve high inference speed. Additionally, optimizations such as ReLU culling and layer fusion were applied to the fully connected architecture, dramatically reducing inference time without compromising accuracy. Validation on three hyperspectral datasets demonstrated that PA2E achieved an average detection capability of 29, outperforming the current state-of-the-art methods (average of 23 out of 42) with 2.6 times faster inference speed. PA2E leverages the unique characteristics of hyperspectral data and offers a highly optimized layer structure, making it a promising model for application in the hyperspectral food industry and other hyperspectral domains. Hyperspectral imaging ; Anomaly detection ; Computer science ; Food safety ; Artificial intelligence ; Computer vision ; Remote sensing ; Pattern recognition (psychology) ; Geology ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Ling Liu | Shaofang Zhang | Xinzhu Chen | Guo Li | Yadong Zhe | Yonghui Li | Xiaodong Zhang
Source: Chemistry of Materials (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Spin-Cooperated Catalytic Activities in MnN<sub>4</sub>-Based Single-Atom Nanozyme: Mechanisms and a Brief Charge-Spin Machine Learning Model The applications of natural enzymes are severely hindered by their limited sources and great tendency to denature. Therefore, developing artificial enzymes with comparable or even superior catalytic performance to that of natural enzymes and understanding multienzyme-mimicking catalytic mechanisms in depth are extremely desirable. Herein, single-atom nanozyme (SAzyme) MnN4 with multiple enzyme-like activities is reported, and density functional theory (DFT) calculations combined with machine learning (ML) are employed to rationalize the origin of the performance of multienzyme-mimicking activities. Compared to its peroxidase- and catalase-like activities, the MnN4 SAzyme exhibits outstanding superoxide dismutase (SOD)-like performance in “one-side adsorption” catalytic mechanisms due to a very low barrier of 0.077 eV in the desorption of the O2 molecule. Such SOD-like activity can be linked to the “spin flip lock”, which appears in the detachment of the O2 moiety: the O2 moiety is locked to the MnN4 SAzyme until the charge is transferred from the superoxide anion to the catalyst. Such a spin-related mechanism suggests a broad correspondence between electronic distribution and spin. With DFT-simulated charge-spin characteristics, a ML model is built based on the natural population analysis data set using the principal component analysis (PCA) and support vector machine algorithms. It displays a w-shaped boundary that separates the high spin from the low spin with respect to PCA-converted charge features. This work not only provides essential guidance for future synthesis of high-performance artificial SAzymes but also proposes rational mechanisms that are responsible for the multienzyme-mimicking catalytic activities of the MnN4 SAzyme. Chemistry ; Moiety ; Density functional theory ; Population ; Spin (aerodynamics) ; Computational chemistry ; Chemical physics ; Stereochemistry ; Physics ; Thermodynamics Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Electrochemical sensors and biosensors
Authors: J. P. Lu | Feiyang Xu | Yulu Zhou | Xiaoming Mo | Yifang Ouyang | Xiaoma Tao
Source: ACS Applied Materials & Interfaces (article, 2024-05-07)
Field: Materials Science — Materials Chemistry

Four-Phonon Enhanced the Thermoelectric Properties of ScSX (X = Cl, Br, and I) Monolayers Recently, the FeOCl-type two-dimensional materials have attracted significant attention owing to their versatile applications in fields such as thermoelectricity and photocatalysis. This study aims to systematically investigate the thermoelectric properties of ScSX (X = Cl, Br, and I) monolayers by a combination of the first-principles calculations and the machine-learning interatomic potential approach. These monolayers are indirect semiconductors with band gaps of 3.22 (ScSCl), 3.27 (ScSBr), and 2.87 eV (ScSI), respectively. The lattice thermal conductivity is decreased by 25.72% (20.90%), 44.05% (40.00%), and 30.96% (34.76%) for ScSCl, ScSBr, and ScSI along the <i>x</i>-axis (<i>y</i>-axis) when the four-phonon scattering is introduced, indicating its important role in phonon transport. Anharmonic phonon scattering yields high Grüneisen parameter and scattering rate values, hence causing these low lattice thermal conductivities. Additionally, the large Seebeck coefficients and electrical conductivities of n-type doped ScSX monolayers contribute to their excellent power factors (24.69, 25.66, and 24.99 mW/K<sup>2</sup>·m for ScSCl, ScSBr and ScSI at 300 K, respectively). Based on the excellent power factor and low thermal conductivity, the maximum values of the figure of merit are calculated to be 2.68, 3.39, and 3.21 for ScSCl, ScSBr, and ScSI monolayers at 700 K, respectively. Our research provides valuable insights into the phonon thermal transport of ScSX monolayers and suggests a promising approach to address high-order anharmonicity. Materials science ; Monolayer ; Phonon ; Thermoelectric effect ; Anharmonicity ; Phonon scattering ; Thermoelectric materials ; Condensed matter physics ; Thermal conductivity ; Semiconductor ; Scattering ; Seebeck coefficient ; Doping ; Figure of merit ; Optoelectronics ; Nanotechnology ; Thermodynamics ; Optics ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; MXene and MAX Phase Materials ; 2D Materials and Applications
Authors: Bo Zhang | Xueqing Li | Xinxin Xu | Jingguo Cao | Ming Zeng | Wu Zhang
Source: Polymer (article, 2024-09-07)
Field: Materials Science — Polymers and Plastics

Multi-property prediction and high-throughput screening of polyimides: An application case for interpretable machine learning Throughput ; Property (philosophy) ; Artificial intelligence ; High-throughput screening ; Computer science ; Machine learning ; Materials science ; Chemistry ; Philosophy Synthesis and properties of polymers ; Formal Methods in Verification ; Fuel Cells and Related Materials
Authors: Qi Qi | Zhuang Wang
Source: Environmental Science and Pollution Research (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Machine learning-based models to predict aquatic ecological risk for engineered nanoparticles: using hazard concentration for 5% of species as an endpoint Ecotoxicity ; Random forest ; Machine learning ; Artificial intelligence ; Artificial neural network ; Decision tree ; Naive Bayes classifier ; Environmental science ; Support vector machine ; Ecology ; Computer science ; Chemistry ; Biology Nanoparticles: synthesis and applications
Authors: Xiaonan Lu | Zachary D. Weller | Vivianaluxa Gervasio | John D. Vienna
Source: Journal of Non-Crystalline Solids (article, 2024-03-21)
Field: Materials Science — Ceramics and Composites

Glass design using machine learning property models with prediction uncertainties: Nuclear waste glass formulation Property (philosophy) ; Radioactive waste ; Computer science ; Machine learning ; Materials science ; Engineering ; Waste management Glass properties and applications ; Advanced X-ray and CT Imaging ; 3D Surveying and Cultural Heritage
Authors: Binbin Wang | De-xin Zhu | Chaolei Zhang | Xiaoye Zhou | Hong‐Hui Wu | Shuize Wang | Guilin Wu | Junheng Gao | Haitao Zhao | Xinping Mao
Source: Journal of Iron and Steel Research International (review, 2024-08-29)
Field: Materials Science — Materials Chemistry

Influence of typical elements and heat treatment parameters on hardenability in steel: a review Hardenability ; Metallurgy ; Materials science ; Heat treating ; Environmental science Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Advanced materials and composites
Authors: Yan Hu | Huahao Yu | Xinbei Song | Wei Chen | Lejia Ding | Jiayi Chen | Zhiyuan Liu | Yihang Guo | Dongyun Xu | Xuesong Zhu | Chuangchuang Zhou | Jingfei Zhang | Binhui Liao | Jihong Zhou | Xiaoli Li | Yuefei Wang | Yong He
Source: Food Research International (article, 2024-09-21)
Field: Chemistry — Analytical Chemistry

Comprehensive assessment of matcha qualities and visualization of constituents using hyperspectral imaging technology Hyperspectral imaging ; Visualization ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Remote sensing ; Geology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Meat and Animal Product Quality