jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jun‐ichi Fukuda | Kazuaki Z. Takahashi
Source: Proceedings of the National Academy of Sciences (article, 2024-12-03)
Field: Materials Science — Materials Chemistry

Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics Phase transition between ordered phases has garnered attention from the viewpoint of materials science as well as statistical physics. One interesting example is martensitic transformation and the resulting formation of twin structures, in which atoms or molecules that form one crystalline phase move in a concerted and diffusionless manner toward another crystalline phase. Recently martensitic transformation has been observed experimentally also in various soft materials. However, the complex internal structures involving many molecules have eluded direct investigation of the dynamical processes of martensitic transformation. Here, we carry out a direct simulation of mesoscale structural transition of a liquid crystalline blue phase (BP) of cubic symmetry, known as BP II. The dynamics is simulated by a Langevin-type equation for the orientational order parameter with thermal fluctuations. We demonstrate that machine-learning-aided analysis of local structures successfully unravels the transformation process from a perfect lattice of BP II to a twinned lattice of another BP (BP I). The nucleation of BP I is initiated by the breakup of junctions of line defects (disclinations), followed by the deformation of disclination network. We further show that twinned BP I is reversibly transformed to a perfect lattice of BP II by temperature variation. Order-parameter-based simulations with machine-learning-aided local structure identification provide valuable insights into not only the martensitic transformation of soft materials but also a wider class of complex structural transitions between ordered phases. Diffusionless transformation ; Crystal twinning ; Martensite ; Disclination ; Nucleation ; Phase transition ; Materials science ; Condensed matter physics ; Statistical physics ; Molecular dynamics ; Chemical physics ; Crystallography ; Physics ; Thermodynamics ; Liquid crystal ; Chemistry ; Computational chemistry ; Microstructure Material Dynamics and Properties ; Liquid Crystal Research Advancements ; Block Copolymer Self-Assembly
Authors: Sunin Baek | Woongchul Choi
Source:  (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Performance Comparison of Machine Learning-Based Static Capacity Estimation Technique with Various C-rate Partial Discharges The significant growth of electric vehicles (EVs) has highlighted the need for effective management of retired lithiumion batteries. Accurate static capacity estimation is crucial for assessing these batteries' remaining useful life, particularly for reuse and recycling. Previous research has demonstrated the efficiency of machine learning-based models for rapid static capacity estimation. However, using a high C-rate such as 1C presents challenges, requiring high power and leading to larger equipment. This study addresses these issues by utilizing short partial discharge data at reduced C-rates of 1C, 0.5C, and 0.25C to diagnose static capacity quickly and accurately. The model's performance was evaluated using MAE, MSE, and RMSE metrics. The experimental results showed RMSE errors of 29.716 mAh, 20.942 mAh, and 14.368 mAh for each C-rate, respectively. These metrics showed satisfactory results among all cases confirming that the accurate, fast, and safe static capacity estimation is possible using low current partial discharges. This study indicates that low-power diagnostic equipment can facilitate safer, more cost-effective, and widespread diagnosis of retired EV batteries, supporting the sustainable reuse and recycling industry. Computer science ; Estimation ; Artificial intelligence ; Machine learning ; Engineering High voltage insulation and dielectric phenomena ; ECG Monitoring and Analysis ; Electrostatic Discharge in Electronics
Authors: Yuxuan Chen | Chao Liang | Jian Yuan | Biao Wang | Huashan Li
Source: Physical review. B./Physical review. B (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Unveiling the microscopic dynamics of the charge density wave transition in monolayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi mathvariant="normal">V</mml:mi><mml:msub><mml:mi>X</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> (<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant="normal">S</mml:mi></mml:mrow></mml:math>, Te) Charge density waves (CDWs) in two-dimensional materials have received great attention due to their intriguing properties, yet the microscopic evolution process of CDW transition and its impact on charge transport remain to be fully understood. Herein we employed density-functional theory calculations to ascertain the richness of CDW phases in $\mathrm{V}{X}_{2}$ ($X=\mathrm{S}$, Te) originated from electron-phonon coupling. Reversible transitions between the normal and CDW phases are directly simulated with ab initio molecular dynamics, indicating that the formation of CDW phase is a rapid nucleation process. The corresponding microscopic dynamic processes involve the formation, flipping, translation, and aggregation of characteristic patterns, which are driven by the soft phonon modes. Modifications of electrical conductivity in CDW phase transition are found to stem from the varying orientation and location distributions of relevant wave functions. The revealed dynamic mechanism opens an opportunity for the control of CDW phase transition that is crucial to its applications in logical circuits and neural networks. Physics ; Dynamics (music) ; Algorithm ; Computer science 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Machine Learning in Materials Science
Authors: Cinthia Antunes Corrêa | Jiří Volný | Klára Uhlířová | Tim Verhagen
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Mapping Sliding Ferroelectricity in Bulk (PbS)<sub>1.11</sub>VS<sub>2</sub> Crystals The recent discovery of sliding ferroelectricity opens up the field of 2D ferroelectricity. However, the limitation to switching the polarization due to the topological protection of the domain walls limits the potential of these heterostructures. Here, the switching of sliding ferroelectric bulk misfit layer compound (PbS) 1.11 VS 2 crystals is studied using switching spectroscopy piezoresponse force microscopy. A large number of measured hysteresis loops can be efficiently analyzed using unsupervised machine‐learning tools. Different switching behavior of the domains is observed on different types of domain structures, and it is shown that full domain switching can be observed for the right type of dislocation structure in the PbS layer. Ferroelectricity ; Materials science ; Crystallography ; Condensed matter physics ; Physics ; Optoelectronics ; Chemistry ; Dielectric Solid-state spectroscopy and crystallography ; Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films
Authors: Abdelli Fathi | J. Sahaya Vijay | Mohamad Nazri Husin | Tony Augustine
Source: Malaysian Journal of Fundamental and Applied Sciences (article, 2024-12-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Valency-Based Molecular Descriptor on Structural Property Relationship of Ni Tetrathiafulvalene Tetrathionate Induced by quantitative structural relationships, valency-based topological indices have been investigated for predicting the structural properties of Ni Tetrathiafulvalene tetrathionate (NiTTFtt) like 2D sheet. Through the use of topological indices, the Kubelka-Munk function for bad gap energy, vibrational frequencies of IR spectroscopy, and graph energy of NiTTFtt like 2D sheet are calculated. The structural features studied have applications such as biosensing, drug discovery, chemical graph theory, machine learning, and more. This main study focused on deriving expressions and numerical values of valency-based topological indices for NiTTFtt like 2D sheet. Additionally, we employed technique to calculate the Kubelka-Munk function values linearly increase when obtained directly from numerical values of valency-based topological indices. Finally, for investigating the properties of NiTTFtt like 2D sheet, statistical correlation analysis shows a significant correlation with vibrational frequencies. Valency ; Tetrathionate ; Tetrathiafulvalene ; Property (philosophy) ; Mathematics ; Chemistry ; Materials science ; Molecule ; Organic chemistry ; Sulfur ; Philosophy Organic and Molecular Conductors Research
Authors: Yurong Guan | Mudassir Hussain Tahir | Yasser M. Riyad | A. Badshah | Zeinhom M. El‐Bahy
Source: Dyes and Pigments (article, 2024-08-31)
Field: Materials Science — Materials Chemistry

Generation of chemical library of near-IR dyes for photovoltaics applications Photovoltaics ; Materials science ; Chemistry ; Nanotechnology ; Engineering physics ; Photovoltaic system ; Physics ; Electrical engineering ; Engineering Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; TiO2 Photocatalysis and Solar Cells
Authors: Ruan Bernardy | Lázaro da Costa Corrêa Cañizares | Sílvia Letícia Rivero Meza | Larissa Alves Rodrigues | Silvia Naiane Jappe | Maurício de Oliveira
Source: Ciência e Agrotecnologia (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Identifying soybean genotypes with artificial intelligence and near infrared reflectance ABSTRACT With the increasing soybean production in Brazil, and the demand for soybeans with high protein and oil content, it is essential to conduct an in-depth study of the constituents of this grain, which can vary according to genotypes and growing conditions. Therefore, the objective of this study was to classify soybean genotypes, cultivated in different environments and sowing seasons, according to their chemical composition and the spectrum generated by near-infrared spectroscopy (NIRS). For this purpose, artificial intelligence and its machine learning technique were employed. 10 soybean genotypes were used, sown in two sowing seasons and cultivated 7 cities in Rio Grande do Sul. The chemical composition of the samples was analyzed using the FOSS NIRS DS2500 equipment, selecting the band between 807 and 817 nm. The applied algorithms were J48, Random Forest, CVR, lBk, MLP, using the Resample filter. The Weka software, version 3.8.6, was employed for data mining. The IBk algorithm achieved the best performance, reaching 89% correct classification of attributes. From the Confusion Matrix, it was observed that all genotypes obtained results above 60/70 for correctly predicted values, highlighting the algorithms’ good performance. In the metrics, IBk achieved 0.89 Precision, Recall, and F-Measure, and 0.94 ROC Area. Thus, it was possible to classify the genotypes according to their chemical composition related to the data obtained in the spectral curve, sowing season, and environment, using artificial intelligence and machine learning. Confusion matrix ; Sowing ; Mathematics ; C4.5 algorithm ; Artificial intelligence ; Machine learning ; Agronomy ; Computer science ; Biology ; Support vector machine Spectroscopy and Chemometric Analyses ; Soybean genetics and cultivation ; Genetics and Plant Breeding
Authors: Livia Stoica | Vasile Radu | Denisa Toma | Alexandra Jinga
Source: Journal of Science and Arts (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

ASSESSMENT OF DELAYED HYDRIDE CRACKING IN CANDU PRESSURE TUBE USING THE PROCESS ZONE WITH CREEP EQUATION FROM ARTIFICIAL NEURAL NETWORK MODELLING The pressure tubes of the CANDU 600 nuclear power plant at CNE Cernavoda Romania are made of Zr-2.5%Nb alloy, which is susceptible to hydrogen accumulation during normal operation. As part of the work, structural integrity analyses will be performed regarding the initiation of the Delayed Hydride Cracking (DHC) phenomenon at the complex flaws in the pressure tubes, which can be detected by the periodic inspections performed on the fuel channels. These flaws are described by the Canadian standard CAN/CSA N285.8 as a combination of a Bearing Pad Fretting Flaw (BPFF) with a Debris Fretting Flaw (DFF). The analysis of the mechanical stresses and strains field is obtained by finite element analysis (FEA) in the process zone of the flaws, that are located on the inner surface of the CANDU pressure tube. The work develops a method based on FEA, regarding the evaluation of the phenomenon of mechanical stress relaxation by creep in the process zone of flaws for the time interval between two periodic inspections of the CANDU fuel channels. This method allows obtaining the relaxation of mechanical stresses, by inserting the explicit function of the radial strain rate of the CANDU pressure tube (Zr-2.5%Nb alloy) into the algorithm for obtaining iterative numerical solutions in creep. The explicit function was obtained by the Multilayer Feedforward Neural Network (MFNN) method under the conditions of irradiation in-service specific to the CANDU fuel channels. The results of the work are used in the assessment of structural integrity by analysing the prevention of DHC initiation in the pressure tubes of a CANDU plant. Creep ; Cracking ; Artificial neural network ; Hydride ; Tube (container) ; Process (computing) ; Materials science ; Metallurgy ; Composite material ; Computer science ; Artificial intelligence Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Cuimei Liu | X. D. Liu | Yu Du | Zhendong Hua
Source: Forensic Science International (article, 2024-03-02)
Field: Chemistry — Spectroscopy

Discrimination of opium from Afghanistan and Myanmar by infrared spectroscopy coupled with machine learning methods Opium ; Random forest ; Support vector machine ; Linear discriminant analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Partial least squares regression ; Profiling (computer programming) ; Computer science ; Mathematics ; Machine learning ; Geography Analytical Chemistry and Chromatography ; Plant-based Medicinal Research ; Chromatography in Natural Products
Authors: Ivan Dudakov | С. В. Савельев | Iurii M. Nevolin | Artem Mitrofanov | Vadim Korolev | Yulia G. Gorbunova
Source: ChemRxiv (preprint, 2024-11-08)
Field: Chemistry — Inorganic Chemistry

Examining proton conductivity of metal-organic frameworks by means of machine learning The tunable structure of metal–organic frameworks (MOFs) is an ideal platform to achieve contradictory requirements for proton exchange membranes, a key component of fuel cells. However, the rational design of proton-conducting MOFs remains a challenge owing to the intricate structure-property relationships that govern the target performance. In the present study, the modeling of quantities available for hundreds of MOFs is scaled up to many thousands of entities using supervised machine learning. The experimental dataset was curated to train multimodal transformer-based networks, which integrated crystal graph, energy-grid, and global-state embeddings. Uncertainty-aware models reveal superprotonic conductors among synthesized MOFs that have not been previously investigated for the considered application, highlighting magnesium-containing frameworks with aliphatic linkers as high-confidence candidates for experimental validation. Furthermore, classifiers trained on the activation energy threshold effectively discriminated between well-known proton-conduction mechanisms, providing physical insights beyond black-box routine. Our findings have proved a great potential for the data-driven materials design, which becomes a valuable addition to experimental studies devoted to proton-conducting MOFs. Conductivity ; Proton ; Metal-organic framework ; Metal ; Artificial intelligence ; Materials science ; Computer science ; Chemistry ; Physics ; Metallurgy ; Organic chemistry ; Physical chemistry ; Nuclear physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies
Authors: Yangshuai Wang | James R. Kermode | Christoph Ortner | Lei Zhang
Source: Computer Methods in Applied Mechanics and Engineering (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

A posteriori error estimate and adaptivity for QM/MM models of crystalline defects Estimator ; Robustness (evolution) ; Residual ; Computer science ; QM/MM ; A priori and a posteriori ; Algorithm ; Consistency (knowledge bases) ; Isotropy ; Molecular dynamics ; Applied mathematics ; Mathematical optimization ; Mathematics ; Physics ; Artificial intelligence ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Yifan Zhang | Rajni Chahal | M. Mustafa Azeem | Stephen Lam | Karl Ludwig | Uday B. Pal | Michael C. Gao | Adam Powell | Zhong Yu
Source: Computational Materials Science (article, 2024-08-24)
Field: Chemistry — Inorganic Chemistry

Computational insights into the structural, thermodynamic and transport properties of CaF2-MgF2 binary fluoride system at high temperatures Binary number ; Fluoride ; Materials science ; Thermodynamics ; Chemistry ; Inorganic chemistry ; Physics Inorganic Fluorides and Related Compounds ; Spacecraft and Cryogenic Technologies ; High-pressure geophysics and materials
Authors: Fahreza Haqqi | Muhammad Rivai | Totok Mujiono
Source:  (article, 2024-07-10)
Field: Chemistry — Organic Chemistry

Agarwood Classification using the Electronic Nose Method Agarwood is an important commodity in the perfume industry, and its price depends on its aroma. The resin content is the main factor in the appearance of the distinctive aroma. The differences in agarwood aroma are difficult for human senses to distinguish. In this study, we developed an innovative agarwood aroma classification system using the electronic nose method. This system used a semiconductor gas sensor array to detect and measure the gas emitted by agarwood and a machine learning algorithm for automatic classification purposes. The results of the experiments show that the gas sensor array can provide specific patterns for each type of agarwood, namely Merauke, Bintuni, Sulawesi, and Borneo. The support vector machine (SVM) algorithm can differentiate each type of agarwood with an accuracy of 93.7%. Agarwood ; Electronic nose ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Medicine Wood and Agarwood Research ; Identification and Quantification in Food ; Forensic Fingerprint Detection Methods
Authors: Xiaojian Liu | Zhifeng Li | Shaoheng Song | Hang Li | Haodong Suo | Weihua Liu | Dongliang Liu | Yaqin Song
Source: Engineering Research Express (article, 2024-12-01)
Field: Materials Science — Materials Chemistry

An insulating composite material defects detection CNN model using knowledge-based 2D structured ultrasonic signals Abstract Defects detection of insulators is crucial for the safe operation of power grid. A strategy of domain knowledge-assisted convolutional neural network is implemented for evaluating various depths and sizes of internal defects in insulating composite materials. A novel periodic-based 2D structuring method for ultrasonic signals is used to assist the CNN feature extraction process, leveraging the invariance of defect types with respect to the ultrasound sampling window and real background noise levels for data augmentation to enhance signal fidelity. Two supervised learning-based CNN models are trained to demonstrate the effectiveness of the proposed method. It is observed that the periodic-based 2D representation of ultrasonic signals facilitated superior performance of the 2DCNN compared to the 1DCNN using one-dimensional signals. In our strategy, the 2D ultrasonic signal can be interpreted as a feature map depicting the dependencies among different reflected echoes, as well as the intra- and inter-periodic variations of individual echoes. This domain-knowledge-compliant representation enhances the interpretability of the convolutional neural network. The results show that the trained 2DCNN achieved a defect recognition accuracy of 98.3% on unseen test sets and provided a relatively conservative accuracy estimate of 90% for defect-free samples, fully meeting the real industrial detection requirements to avoid misjudgment and missed judgment. Utilizing domain knowledge to assist neural networks effectively improves the quality of models required for industrial inspection. Ultrasonic sensor ; Composite number ; Materials science ; Acoustics ; Computer science ; Composite material ; Physics High voltage insulation and dielectric phenomena ; Power Line Inspection Robots
Authors: Sabrina Wahler
Source: Propellants Explosives Pyrotechnics (article, 2024-10-24)
Field: Materials Science — Materials Chemistry

Reparametrizing Joback's Group Additivity Theory – Thermochemical Building Blocks for Targeted Design of New Compounds Abstract This study aims to investigate if complex machine‐learning models are required to predict certain properties of materials, or if simple linear regression models also give sufficiently accurate predictions of thermochemical properties and density. An in‐depth analysis of Joback's method in combination with statistical models, and the density predictions by Holden, Keshavarz, and Bondarchuk are made. Updated Group increment tables for Joback's method are provided. The ‘P arametrization of E mpirical A pproximations using S tatistical Methods’ ( PEAS ) is intended to evaluate if it could be better to resort to simpler methods in certain cases. Additionally, a data set analysis tool named “Gr oup E xtraction A nalysis T ool for D eviations in a pproximating n umerical modeling e rrors” ( Great Dane© ) is presented. Additive function ; Group (periodic table) ; Standard enthalpy of formation ; Thermochemistry ; Chemistry ; Computational chemistry ; Mathematics ; Combinatorial chemistry ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Physics ; Mathematical analysis Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure ; Energetic Materials and Combustion