jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jun Li | Kun Luo | Qi An
Source: Crystal Growth & Design (article, 2024-06-26)
Field: Materials Science — Materials Chemistry

Elucidating Twinning Mechanisms in CdTe Homoepitaxial Film Growth from Machine Learning Force Field Molecular Dynamics Simulations The process of twinning formation during the growth of semiconductor thin films, particularly in CdTe homoepitaxial films, is a complex phenomenon that has eluded comprehensive understanding. Here, this study investigated the growth dynamics of CdTe homoepitaxial films on (111) Cd- and Te-terminated surfaces across a range of substrate temperatures. We employed molecular dynamics and time-stamped force-bias Monte Carlo simulations with a machine-learning force field to explore these growth dynamics. Our findings reveal that CdTe homoepitaxial layers are characterized by lamella twins with (111) twin boundaries parallel to the surface. The development of stacking fault layers is potentially influenced by the presence of residual holes prior to the crystallization of subsequent layers. We also observed that (111) Te homoepitaxial layers exhibit more significant planar defects than (111) Cd homoepitaxial layers, and this disparity is more pronounced at lower substrate temperatures and can be attributed to the more reactive (111) Te-terminated surface. Moreover, there is a notable trend in which the prevalence of planar defects escalates with increasing substrate temperature, arising from the competing formation mechanism of CdTe zinc-blende and wurtzite structures. This work offers detailed atomic-level insights into the growth and twinning mechanisms of CdTe homoepitaxial films. Crystal twinning ; Molecular dynamics ; Cadmium telluride photovoltaics ; Field (mathematics) ; Materials science ; Force field (fiction) ; Chemical physics ; Dynamics (music) ; Nanotechnology ; Chemistry ; Computer science ; Physics ; Artificial intelligence ; Computational chemistry ; Composite material ; Microstructure ; Mathematics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Chalcogenide Semiconductor Thin Films
Authors: Frendy Jaya Kusuma | Eri Widianto | Wahyono Wahyono | Iman Santoso | Sholihun Sholihun | Moh. Adhib Ulil Absor | Setyawan P. Sakti | Kuwat Trıyana
Source: Solar Energy (article, 2024-12-12)
Field: Materials Science — Materials Chemistry

Multi-Properties prediction of perovskite materials using Machine learning and Meta-Heuristic feature selection Feature selection ; Computer science ; Meta heuristic ; Heuristic ; Perovskite (structure) ; Feature (linguistics) ; Artificial intelligence ; Machine learning ; Selection (genetic algorithm) ; Meta learning (computer science) ; Materials science ; Algorithm ; Systems engineering ; Chemical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications ; Machine Learning and ELM
Authors: Jinhan Zhang | Jingtai Yu | Xiaoran Wei | Kun Zhou | Weifei Niu | Yushun Wei | Cong Zhao | Gang Chen | Fengmin Jin | Kai Song
Source: Materials Characterization (article, 2024-11-07)
Field: Materials Science — Surfaces, Coatings and Films

A soft scanning electron microscopy for efficient segmentation of alloy microstructures based on a new self-supervised pre-training deep learning network Materials science ; Scanning electron microscope ; Microstructure ; Alloy ; Segmentation ; Training (meteorology) ; Artificial intelligence ; Electron microscope ; Nanotechnology ; Pattern recognition (psychology) ; Metallurgy ; Composite material ; Computer science ; Optics Electron and X-Ray Spectroscopy Techniques
Authors: Wenhui Li | Yingxin Chen | Xin Li | Yi Zhong | Pei Xu | Yuanjie Teng
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrasensitive SERS quantitative detection of antioxidants via diazo derivatization reaction and deep learning for signal fluctuation mitigation Derivatization ; Diazo ; Chemistry ; BSTFA ; Raman scattering ; Detection limit ; Raman spectroscopy ; Antioxidant ; Colloidal gold ; Nanoparticle ; Chromatography ; Combinatorial chemistry ; Organic chemistry ; High-performance liquid chromatography ; Materials science ; Nanotechnology Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy and Chemometric Analyses ; Dye analysis and toxicity
Authors: Mahmoud Shaban | Fahad N. Alsunaydih | Hanan Kouta | Samar El‐Sanabary | Abdulrahman Alrumayh | A. I. Alateyah | Majed O. Alawad | Waleed H. El-Garaihy | Yasmine El-Taybany
Source: Scientific Reports (article, 2024-04-22)
Field: Materials Science — Materials Chemistry

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE-GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70-99.7%, respectively. Response surface methodology ; Materials science ; Design of experiments ; Alloy ; Pressing ; Die (integrated circuit) ; Coefficient of friction ; Friction coefficient ; Grain size ; Standard deviation ; Correlation coefficient ; Genetic algorithm ; Computer science ; Mathematics ; Metallurgy ; Composite material ; Statistics ; Machine learning Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Metal and Thin Film Mechanics
Authors: Anastasiia Tukmakova | Patrizio Graziosi
Source: ACS Applied Energy Materials (article, 2024-11-12)
Field: Materials Science — Materials Chemistry

Machine Learning Unveils the Physical Properties of Materials Driving Thermoelectric Generator Efficiency: The Case of Half-Heuslers We report a machine learning (ML)-based approach allowing thermoelectric generator (TEG) efficiency evaluation directly from five parameters: two physical properties─carrier density and energy gap, and three engineering parameters─external load resistance, TEG hot side temperature, and leg height. Then, we use a genetic algorithm to optimize these parameters to maximize TEG efficiency. To prepare data, physical properties of n- and p-type materials were computed by coupling Density Functional Theory with Boltzmann Transport and were then used for Finite Elements simulations. TEG efficiency was evaluated using a finite element model that considered design, radiative heat loss, contacts, external load resistance, and combinations of n- and p-type materials, resulting in 5300 different scenarios with corresponding efficiency values. For the ML model, physical properties and engineering parameters were used as input features, excluding thermoelectric coefficients, with TEG efficiency as the target. The model was based on the gradient boosting algorithm, and its performance was evaluated using the coefficient of determination, which reached a value of 0.98 on the test dataset. Feature importance analysis revealed the most crucial features for half-Heusler-based TEG efficiency: carriers density or Fermi level position, indicating the predominant role of the balance between electrical conductivity and the electronic component of thermal conductivity in driving overall TEG module efficiency. Features that were less important but contributed to model performance included energy gap, lattice thermal conductivity, charge carrier relaxation time, and carrier conductivity effective mass. Features with no impact included density of states effective mass, heat capacity, density, relative permittivity, and leg width. The proposed approach can be applied to identify the most important physical properties and their optimal values, as well as to optimize the TEG design and operating conditions to maximize TEG efficiency. Thermoelectric effect ; Thermoelectric generator ; Generator (circuit theory) ; Materials science ; Thermoelectric materials ; Automotive engineering ; Computer science ; Mechanical engineering ; Engineering ; Composite material ; Physics ; Thermal conductivity ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Yanming Feng | Song Yue | Ran Wang | Yu Hou | Shunshuo Cai | Zihuang Wang | Mei Xue | Kunpeng Zhang | Zichen Zhang
Source: Optics Express (article, 2024-10-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-color long-wave infrared perfect absorber based on a heavily doped semiconductor that is inverse-designed via machine learning Metamaterial perfect absorbers (MPAs) with high absorption, thin thickness, and custom-tailorable spectrum are in great demand in many applications, especially in photoelectric detectors. Presently, infrared (IR) focal plane array detectors based on type-II superlattice (T2SL) still face the challenge of a low absorption coefficient. Moreover, it is still difficult to integrate conventional metal-insulator-metal (MIM) MPA with a T2SL infrared detector, due to the incompatibility of fabrication processes. In addition, the need to achieve custom-tailorable multi-peak absorption in the long-wave infrared band is high, and the design process of an MPA with a complicated geometric shape is time-consuming. To tackle these problems, in this work, we replace the ground metal layer in a conventional MIM MPA with a heavily doped semiconductor (n<sup>++</sup>), whose growth process is compatible with the fabrication process of T2SL infrared detectors and thus can be integrated with them. Moreover, we set up a deep neural network (DNN) to associate the spectral response of the device with the corresponding structural parameters. In this way, we can quickly inverse design the infrared perfect absorber with multiple absorption peaks using a trained DNN. The designed devices can achieve three perfect absorption peaks in the wavelength range of interest (8 ∼ 13 µm), and the peak absorptivity generally reaches over 90%. Our work provides an effective method for the inverse design of n<sup>++</sup>IM MPA based on DNN, which is of significant guidance for the study of infrared MPA. Additionally, our work anticipates enhancing the detection performance of infrared detectors through absorption enhancement, indicating substantial application potential in the field of optically modulated infrared detectors. Optics ; Infrared ; Semiconductor ; Doping ; Materials science ; Inverse problem ; Optoelectronics ; Inverse scattering problem ; Inverse ; Physics ; Scattering ; Mathematics Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Infrared Target Detection Methodologies
Authors: Sasaank Bandi | Chao Jiang | Chris A. Marianetti
Source: Machine Learning Science and Technology (article, 2024-07-25)
Field: Materials Science — Materials Chemistry

Benchmarking machine learning interatomic potentials via phonon anharmonicity Abstract Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIPs have been extensively tested across various classes of materials and molecules, a clear characterization of the anharmonic terms encoded in the MLIPs is lacking. Here, we benchmark popular MLIPs using the anharmonic vibrational Hamiltonian of ThO 2 in the fluorite crystal structure, which was constructed from density functional theory (DFT) using our highly accurate and efficient irreducible derivative methods. The anharmonic Hamiltonian was used to generate molecular dynamics (MD) trajectories, which were used to train three classes of MLIPs: Gaussian approximation potentials, artificial neural networks (ANN), and graph neural networks (GNN). The results were assessed by directly comparing phonons and their interactions, as well as phonon linewidths, phonon lineshifts, and thermal conductivity. The models were also trained on a DFT MD dataset, demonstrating good agreement up to fifth-order for the ANN and GNN. Our analysis demonstrates that MLIPs have great potential for accurately characterizing anharmonicity in materials systems at a fraction of the cost of conventional first principles-based approaches. Anharmonicity ; Phonon ; Hamiltonian (control theory) ; Statistical physics ; Artificial neural network ; Molecular dynamics ; Gaussian ; Machine learning ; Interatomic potential ; Computer science ; Thermal conductivity ; Artificial intelligence ; Benchmark (surveying) ; Physics ; Materials science ; Condensed matter physics ; Quantum mechanics ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Hengnian Qi | Jiahao Luo | Gang Chen | Jianyi Zhang | Fengnong Chen | Hongyang Li | Cong Shen | Chu Zhang
Source: Journal of the Science of Food and Agriculture (article, 2024-02-02)
Field: Chemistry — Analytical Chemistry

Detection of peach soluble solids based on near‐infrared spectroscopy with High Order Spatial Interaction network The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry. Artificial intelligence ; Computer science ; Subspace topology ; Deep learning ; Machine learning ; Permutation (music) ; Feature (linguistics) ; Feature selection ; Quality (philosophy) ; Scalability ; Pattern recognition (psychology) ; Algorithm Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Jui‐Han Fu | Dongyue Chen | Yen‐Ju Wu | Vincent Tung
Source: Precision Chemistry (review, 2024-12-04)
Field: Materials Science — Materials Chemistry

Constructing Two-Dimensional, Ordered Networks of Carbon–Carbon Bonds with Precision Organic semiconducting nanomembranes (OSNMs), particularly carbon-based ones, are at the forefront of next-generation two-dimensional (2D) semiconductor research. These materials offer remarkable promise due to their diverse chemical properties and unique functionalities, paving the way for innovative applications across advanced semiconductor material sectors. Graphene stands out for its extraordinary mechanical strength, thermal conductivity, and superior charge transport capabilities, inspiring extensive research into other 2D carbon allotropes like graphyne and graphdiyne. With its high electron mobility and tunable bandgap, graphdiyne is particularly attractive for power-efficient electronic devices. However, synthesizing graphdiyne presents significant challenges, primarily due to the difficulty in achieving precise and deterministic control over the coupling of its monomers. This precision is crucial for determining the material's porosity, periodicity, and overall functionality. Innovative approaches have been developed to address these challenges, such as the strategic assembly of molecular building blocks at heterogeneous interfaces. Furthermore, data-driven techniques, such as machine learning and artificial intelligence (AI), are proving invaluable in this field, assisting in screening precursors, optimizing structural configurations, and predicting novel properties of these materials. These advancements are essential for producing durable monolayer sheets that can be integrated into existing electronic components. Despite these advancements, the integration of graphdiyne into semiconductor technology remains complex. Achieving long-range coherence in bonding configurations and enhancing charge transport characteristics are significant hurdles. Continued research into robust and controllable synthesis techniques is essential for unlocking the full potential of graphdiyne and other 2D materials, leading to more efficient, faster, and mechanically robust electronics. Carbon fibers ; Bond ; Reinforced carbon–carbon ; Materials science ; Business ; Composite material Graphene research and applications ; Graph theory and applications ; Machine Learning in Materials Science
Authors: Muhammad Saqib | Muhammad Sagir | Munawar Lal Joshi | Shahida Bashir | Mohamed Ibrahim Halawa | Saman Ali | Hosam O. Elansary | Ghulam Mustafa Kamal
Source: Materials Today Communications (article, 2024-01-08)
Field: Materials Science — Polymers and Plastics

Screening and designing of a large chemical space of organic semiconductors for photodetectors Cluster analysis ; Photodetector ; Chemical space ; Materials science ; Materials informatics ; Computer science ; Artificial intelligence ; Semiconductor ; Machine learning ; Quantum machine learning ; Organic semiconductor ; Optoelectronics ; Quantum ; Quantum computer ; Physics ; Drug discovery Conducting polymers and applications ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: Janosh Riebesell | T. Wesley Surta | Rhys E. A. Goodall | Michael W. Gaultois | Alpha A. Lee
Source: Cell Reports Physical Science (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

Discovery of high-performance dielectric materials with machine-learning-guided search Computer science ; Dielectric ; Materials science ; Machine learning ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis
Authors: Wanying Zhang | Yibo Ai | Weidong Zhang
Source: Materials Today Communications (article, 2024-04-06)
Field: Materials Science — Metals and Alloys

Analysis of tensile damage of titanium alloy in seawater environment based on deep learning Materials science ; Acoustic emission ; Corrosion ; Reliability (semiconductor) ; Titanium alloy ; Seawater ; Spline interpolation ; Ultimate tensile strength ; Alloy ; Consistency (knowledge bases) ; Metallurgy ; Composite material ; Computer science ; Artificial intelligence Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Corrosion Behavior and Inhibition
Authors: Felipe Perez | Azeezat Ali | Jianxin Wang | Joseph E. Patterson | Ramesh A. Kini | Alberto Striolo
Source: Energy & Fuels (article, 2024-02-16)
Field: Chemistry — Analytical Chemistry

Machine Learning Suggests Possible Bias in Molecular Simulations for Asphaltene Systems Asphaltenes are defined as a solubility class rather than a singular family of molecular compounds; therefore, there is no unique molecular structure that corresponds to asphaltene. Perhaps as a consequence of this definition, direct probing of the physical and chemical properties of asphaltene molecules has been historically challenging in the laboratory. It was only in the past few years that experimental imaging of asphaltene structures resolved representative molecular geometries. Could the identification deriving from these experiments be useful for moving forward our collective understanding of the properties of asphaltenes? Is the experimental identification of numerous asphaltene molecules implying that simulations should consider all of these compounds for providing useful insights? Is it possible that the community has been considering a limited sample of asphaltene molecules when attempting to shed light on asphaltene behavior? This review attempts to address these questions with the help of unsupervised clustering. The results demonstrate that the experimental asphaltene structures can be clustered into four groups that resolve different physical properties of asphaltenes. Then, we probe the existing literature to assess whether compounds representative of the molecules experimentally identified as “asphaltenes” using non-contact atomic force microscopy (nc-AFM) [Schuler, B.; Meyer, G.; Peña, D.; Mullins, O. C.; Gross, L. Unraveling the molecular structures of asphaltenes by atomic force microscopy. J. Am. Chem. Soc. 2015, 137, 9870−9876, DOI: 10.1021/jacs.5b04056 and Schuler, B.; Fatayer, S.; Meyer, G.; Rogel, E.; Moir, M.; Zhang, Y.; Harper, M. R.; Pomerantz, A. E.; Bake, K. D.; Witt, M.; Peña, D.; Kushnerick, J. D.; Mullins, O. C.; Ovalles, C.; van den Berg, F. G. A.; Gross, L. Heavy oil based mixtures of different origins and treatments studied by atomic force microscopy. Energy Fuels 2017, 31, 6856–6861, DOI: 10.1021/acs.energyfuels.7b00805] have been investigated via simulations. We compare structures used in computational studies found in the literature against the experimental geometries and summarize their context and findings. The exercise reveals that only a few asphaltene molecular structures have been studied, while several asphaltene structures have been overlooked in published simulation works. While this is due to our collective efforts to study a “representative” structure for asphaltenes, our analysis reveals a bias in the systems that have been simulated, which leads to suggesting possible directions for future studies that might shed light on the mechanisms responsible for asphaltene precipitation. Asphaltene ; Computer science ; Chemistry ; Organic chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Jingxuan Zhang | Xiaoli Li | Yong Yan | Shixin Cen | Wen Song | An Jun | Yang Yu | Zheng Li
Source: Microchemical Journal (article, 2024-10-18)
Field: Chemistry — Analytical Chemistry

Recognition of Radix Bupleuri origin using laser-induced breakdown spectroscopy (LIBS) combined with deep learning and machine learning algorithms Laser-induced breakdown spectroscopy ; Radix (gastropod) ; Artificial intelligence ; Spectroscopy ; Algorithm ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Materials science ; Physics ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Laser-induced spectroscopy and plasma