jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Thomas Bilyk | Alexandra M. Goryaeva | Mihai‐Cosmin Marinica | Camille Flament | C. Sabathier | Éric Leroy | Marie Loyer-Prost | E. Meslin
Source: Scientific Reports (article, 2024-10-24)
Field: Materials Science — Metals and Alloys

Accurate quantification of dislocation loops in complex functional alloys enabled by deep learning image analysis In-depth statistics of individual defects observed during transmission electron microscopy (TEM) experiments are essential for the thorough characterization of materials. In this study, we aim to quantitatively characterize the population of dislocation loops in ion-irradiated CrFeMnNi alloys. To this end, we propose an efficient guideline to prepare TEM micrographs dataset for deep learning analysis, adapted for accurate characterization of microstructures produced by thousands of overlapping defects, a very common situation in TEM images, unfeasible by previous existing methods. To reduce human effort, we annotate only a few images and complement the database through a two-step process: initially, singular value decomposition to normalize image background, followed by a controlled data augmentation. The performed analysis provides precise quantitative information about the number of loops of different types, as well as their spatial distribution, their size, and the inter-object distances. These characteristics provide insights into the nucleation, mobility, and growth of dislocation loops, as well as the elastic anisotropy of the material. Our results emphasize how accurate analysis of complex microstructures can provide insights into the physical properties of materials and open up many perspectives for attaining quantitative information on materials properties based solely on their image analysis. Dislocation ; Characterization (materials science) ; Computer science ; Nucleation ; Materials science ; Artificial intelligence ; Transmission electron microscopy ; Process (computing) ; Population ; Microstructure ; Pattern recognition (psychology) ; Nanotechnology ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Qingchun Yang | Zhou Jianlong | Runjie Bao | Dongwen Rong | Dawei Zhang
Source: Energy (article, 2024-10-19)
Field: Materials Science — Materials Chemistry

Machine learning-based techno-econo-environmental analysis of CO2-to-olefins process for screening the optimal catalyst and hydrogen color Catalysis ; Process (computing) ; Hydrogen ; Process engineering ; Chemistry ; Environmental science ; Biochemical engineering ; Computer science ; Waste management ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Mohammad Hossein Nargesi | Kobra Heidarbeigi | Zahra Moradi | Sahar Abdolahi
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2024-10-10)
Field: Chemistry — Analytical Chemistry

Detection of chlorine in potassium chloride and potassium sulfate using chemical imaging and artificial neural network Chemistry ; Potassium ; Chlorine ; Potassium sulfate ; Sulfate ; Chloride ; Inorganic chemistry ; Environmental chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Jaesun Kim | Jisu Jung | Sookyung Kim | Seungwu Han
Source: Computational Materials Science (article, 2024-01-09)
Field: Materials Science — Materials Chemistry

Predicting melting temperature of inorganic crystals via crystal graph neural network enhanced by transfer learning Unary operation ; Transfer of learning ; Ternary operation ; Materials science ; Artificial neural network ; Binary number ; Computer science ; Crystal (programming language) ; Mean squared error ; Biological system ; Artificial intelligence ; Machine learning ; Mathematics ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Yifeng Zhang | Jie Tian | Hui Huang | Chen Sun | Chengwei Li | Guanyu Li | Dongyang Ji | Zeng Fan | Lujun Pan
Source: Carbon (article, 2024-07-27)
Field: Materials Science — Materials Chemistry

New insights into the role of nitrogen doping in microporous carbon on the capacitive charge storage mechanism: From ab initio to machine learning accelerated molecular dynamics Microporous material ; Capacitive sensing ; Mechanism (biology) ; Charge (physics) ; Materials science ; Chemical physics ; Ab initio ; Doping ; Carbon fibers ; Molecular dynamics ; Nitrogen ; Nanotechnology ; Computational chemistry ; Chemistry ; Computer science ; Optoelectronics ; Composite material ; Organic chemistry ; Physics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Memory and Neural Computing
Authors: Jihun Ahn | Gabriella Pasya Irianti | Yeojin Choe | Su‐Mi Hur
Source: npj Computational Materials (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Enhancing deep learning predictive models with HAPPY (Hierarchically Abstracted rePeat unit of PolYmers) representation Abstract We introduce HAPPY (Hierarchically Abstracted rePeat unit of PolYmers), a string representation for polymers, designed to efficiently encapsulate essential polymer structure features for property prediction. HAPPY assigns single constituent elements to groups of sub-structures and employs grammatically complete and independent connectors between chemical linkages. Using a limited number of datapoints, we trained neural networks utilizing both HAPPY and conventional SMILES encoding of repeated unit structures and compared their performance in predicting five polymer properties: dielectric constant, glass transition temperature, thermal conductivity, solubility, and density. The results showed that the HAPPY-based network could achieve higher prediction R-squared score and two-fold faster training times. We further tested the robustness and versatility of HAPPY-based network with an augmented training dataset. Additionally, we present topo-HAPPY (Topological HAPPY), an extension that incorporates topological details of the constituent connectivity, leading to improved solubility and glass transition temperature prediction R-squared score. Representation (politics) ; Unit (ring theory) ; Artificial intelligence ; Polymer ; Computer science ; Deep learning ; Machine learning ; Psychology ; Materials science ; Mathematics education ; Composite material ; Political science Machine Learning in Materials Science ; Advanced Data Processing Techniques ; Software Engineering Research
Authors: Gong Chen | Jian Liu | Siqi Dai | Hua Hao | Hanxing Liu
Source: Computational Materials Science (article, 2024-03-21)
Field: Materials Science — Materials Chemistry

Machine learning assisted prediction of the phonon cutoff frequency of ABO3 perovskite materials Phonon ; Cutoff ; Cutoff frequency ; Perovskite (structure) ; Materials science ; Frequency dependence ; Condensed matter physics ; Statistical physics ; Physics ; Chemistry ; Nuclear magnetic resonance ; Quantum mechanics ; Crystallography ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Machine Learning in Materials Science ; Multiferroics and related materials
Authors: Hasan Tiryaki | Aminu Yusuf | Sedat Ballıkaya
Source: Energy (article, 2024-02-04)
Field: Materials Science — Materials Chemistry

Determination of electrical and thermal conductivities of n- and p-type thermoelectric materials by prediction iteration machine learning method Type (biology) ; Thermal diffusivity ; Thermoelectric effect ; Artificial neural network ; Thermal conductivity ; Thermal ; Materials science ; Thermoelectric materials ; Thermodynamics ; Algorithm ; Mathematics ; Analytical Chemistry (journal) ; Machine learning ; Computer science ; Chemistry ; Physics ; Chromatography Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Sung-Ho Lee | Jing Li | Valério Olevano | B. Sklénard
Source: Physical Review Materials (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Equivariant graph neural network interatomic potential for Green-Kubo thermal conductivity in phase change materials Thermal conductivity is a fundamental material property that plays an essential role in technology, but its accurate evaluation presents a challenge for theory. In this work, we demonstrate the application of $E(3)$-equivariant neutral network interatomic potentials within Green-Kubo formalism to determine the lattice thermal conductivity in amorphous and crystalline materials. We apply this method to study the thermal conductivity of germanium telluride (GeTe) as a prototypical phase change material. A single deep learning interatomic potential is able to describe the phase transitions between the amorphous, rhombohedral, and cubic phases, with critical temperatures in good agreement with experiments. Furthermore, this approach accurately captures the pronounced anharmonicity that is present in GeTe, enabling precise calculations of the thermal conductivity. In contrast, the Boltzmann transport equation including only three-phonon processes tends to overestimate the thermal conductivity by approximately a factor of 2 in the crystalline phases. Thermal conductivity ; Materials science ; Phonon ; Anharmonicity ; Condensed matter physics ; Phase transition ; Interatomic potential ; Amorphous solid ; Molecular dynamics ; Physics ; Quantum mechanics ; Composite material ; Crystallography ; Chemistry Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Han Min | Jun Liang | Biao Jin | Ziwei Wang | Wanlu Wu | Hans Peter H. Arp
Source: iScience (article, 2024-01-25)
Field: Chemistry — Electrochemistry

Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water Various synthetic substances were utilized in large quantities during the recent coronavirus pandemic, COVID-19. Some of these chemicals could potentially enter drinking water sources. Persistent, mobile, and toxic (PMT) substances have been recognized as a threat to drinking water resources. It has not yet been assessed how many COVID-19 related substances could be considered PMT substances. One reason is the lack of high-quality experimental data for the identification of PMT substances. To solve this problem, we applied a machine learning model to identify the PMT substances among COVID-19 related chemicals. The optimal model achieved an accuracy of 90.6% based on external test data. The model interpretation and causal inference indicated that our approach understood causation between PMT properties and molecular descriptors. Notably, the screening results showed that over 60% of the COVID-19 chemicals considered are candidate PMT substances, which should be prioritized to prevent undue pollution of water resources. Coronavirus disease 2019 (COVID-19) ; 2019-20 coronavirus outbreak ; Causal inference ; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ; Inference ; Coronavirus Infections ; Artificial intelligence ; Psychology ; Machine learning ; Data science ; Computer science ; Biology ; Medicine ; Virology ; Infectious disease (medical specialty) ; Mathematics ; Econometrics ; Internal medicine Electrochemical Analysis and Applications ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Kirill P. Larionov | Vasilii Yu. Evtushok
Source: Chemistry of Materials (article, 2024-04-15)
Field: Chemistry — Inorganic Chemistry

From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach This study delves into understanding the relationship between synthesis conditions and the resulting properties of the Zr-based metal–organic framework (MOF) UiO-66, with an emphasis on machine learning (ML) in making quantitative predictions. Utilizing a comprehensive, manually curated data set, three ML models are trained to predict UiO-66 properties, including specific surface area, defect concentration, and particle size, based on synthesis parameters. A solution to the inverse problem, which involves finding optimal synthesis conditions for given properties using the method of differential evolution, has been implemented in the software. Experimental validation of models through synthesis and detailed characterization of UiO-66 samples and comparison with the predicted properties show a high accuracy, confirming their reliability. Interpretation of the ML models using Shapley additive explanation values and two-dimensional (2D) partial dependence plots reveals both previously known patterns, validating the adequacy of the models, and new, previously unexplored patterns in the relationships between the synthesis conditions and UiO-66 properties. The developed models can be used as a basis for further research on MOF synthesis. This approach can be applied to the rational design of UiO-66 for various applications. Computer science ; Reliability (semiconductor) ; Characterization (materials science) ; Set (abstract data type) ; Experimental data ; Biological system ; Algorithm ; Materials science ; Mathematics ; Nanotechnology ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Meng Yang | Qiang Xu | Guangqing Chen | Jianjun Liu | Shuoye Zhou | Yanling Zhang | Aiguo Wang | Jianwei Wang | Ding Yan | Xianjie Cai | Junying Li | Xuchu Chen | Qiuying Li | Qiang Zeng | Weimin Guo | Yuanhui Wang
Source: Scientific Reports (article, 2024-11-07)
Field: Chemistry — Analytical Chemistry

Regression prediction of tobacco chemical components during curing based on color quantification and machine learning Color is one of the most important indicators to characteristic the quality of tobacco, which is strongly related to the variations of chemical components. In order to clarify the relationship between the changes of tobacco color and chemical components, here we established several prediction models of chemical components with the color values of tobacco based on machine learning algorithms. The results of correlation analysis showed that tobacco moisture content was highly significantly correlated with the parameters such as a<sup>*</sup>, H<sup>*</sup> and H°, the reducing sugar and total sugar content of tobacco was significantly correlated with the color values, and the starch content was highly significantly correlated with the color values except for b<sup>*</sup> and C<sup>*</sup>. The random forest models performed best in predicting tobacco moisture, reducing sugar, total sugar and starch constructed with the R<sup>2</sup> of the model validation set was higher than 0.90, and the RPD value was greater than 2.0. The consistent between the predictions and measurements verified the availability and feasibility using color values to predict some chemical components of the tobacco leaves with high accuracy, and which has distinct advantages and potential application to realize the real-time monitoring of some chemical components in the tobacco curing process. Regression ; Regression analysis ; Computer science ; Artificial intelligence ; Curing (chemistry) ; Machine learning ; Statistics ; Chemistry ; Mathematics Spectroscopy and Chemometric Analyses ; Food Quality and Safety Studies ; Advanced Chemical Sensor Technologies
Authors: W. Chen | Wei Gou | Yageng Li | Xiangmin Li | Meng Li | Jianxin Hou | Xiaotong Zhang | Zhang‐Zhi Shi | Lu‐Ning Wang
Source: International Journal of Minerals Metallurgy and Materials (article, 2024-11-09)
Field: Materials Science — Materials Chemistry

Machine learning design of 400 MPa grade biodegradable Zn-Mn based alloys with appropriate corrosion rates Corrosion ; Metallurgy ; Materials science Corrosion Behavior and Inhibition ; Advanced materials and composites ; Magnesium Alloys: Properties and Applications
Authors: Yuqi Liu | Haikuan Dong | Ying Ren | Weigang Zhang | Wei Chen
Source: Computational Materials Science (article, 2024-12-27)
Field: Materials Science — Materials Chemistry

Crystallization of h-BN by molecular dynamics simulation using a machine learning interatomic potential Crystallization ; Molecular dynamics ; Interatomic potential ; Materials science ; Chemical physics ; Statistical physics ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Liang Sun | Mohan Chen
Source: Physical review. B./Physical review. B (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Machine learning based nonlocal kinetic energy density functional for simple metals and alloys Developing an accurate kinetic energy density functional (KEDF) remains a major hurdle in orbital-free density functional theory. We propose a machine-learning-based physical-constrained nonlocal (MPN) KEDF and implement it with the usage of the bulk-derived local pseudopotentials and plane wave basis sets in the abacus package. The MPN KEDF is designed to satisfy three exact physical constraints: the scaling law of electron kinetic energy, the free electron gas limit, and the non-negativity of Pauli energy density. The MPN KEDF is systematically tested for simple metals, including Li, Mg, Al, and 59 alloys. We conclude that incorporating nonlocal information for designing new KEDFs and obeying exact physical constraints are essential to improve the accuracy, transferability, and stability of ML-based KEDF. These results shed new light on the construction of ML-based functionals. Simple (philosophy) ; Kinetic energy ; Energy (signal processing) ; Materials science ; Density functional theory ; Statistical physics ; Physics ; Classical mechanics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Pengyu Liu | Zhenming Cui | Yan Sun | Wenpei Yuan | Lin Qu | Xiaoming Wang | Yanlan Zhang | Yongzhen Wang
Source: Ceramics International (article, 2024-09-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on high-entropy spinel microwave absorption materials: Exploration of machine learning and experimental integration Materials science ; Spinel ; Microwave ; Absorption (acoustics) ; Entropy (arrow of time) ; Engineering physics ; Composite material ; Metallurgy ; Thermodynamics ; Computer science ; Engineering ; Telecommunications Electromagnetic wave absorption materials ; Thermal Expansion and Ionic Conductivity ; Transition Metal Oxide Nanomaterials
Authors: Atsushi Ishikawa
Source: Journal of Computational Chemistry (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

Machine‐learning descriptor search on the density of states profile of bimetallic alloy systems and comparison with the d‐band center theory Abstract In this study, the electronic density of states (DOSs) calculated with density functional theory (DFT) were analyzed by the machine‐learning techniques. More than 400 pure metal and bimetallic alloy systems were calculated with DFT, and obtained the surface DOSs and the CH 3 adsorption energy ( E ad ). By fitting the Gaussian functions to the DOS, multiple descriptors, such as the Gaussian peak positions, heights, and widths were extracted. Several regression methods, such as the least absolute shrinkage of selection operator (LASSO), random‐forest, gradient‐boosting, and extra‐tree were used to find the relationship between these descriptors and the E ad . The results show that the energy position of the peaks in the d‐projected DOS is the most important descriptor, in agreement with the previously known d‐band center theory. It was also shown that the peak position in d‐projected DOS improves the regression model in addition to the d‐band center, since it reduces the regression error. Bimetallic strip ; Center (category theory) ; Alloy ; Density functional theory ; Materials science ; Computer science ; Computational chemistry ; Chemistry ; Crystallography ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Jingli Ren | Xiao Lu
Source: Scripta Materialia (article, 2024-08-08)
Field: Materials Science — Metals and Alloys

Physics-guided neural network for fatigue life prediction of FCC-based multi-principal element alloys Artificial neural network ; Ductility (Earth science) ; Generalization ; Univariate ; Principal (computer security) ; Goodness of fit ; Principal component analysis ; Materials science ; Experimental data ; Artificial intelligence ; Structural engineering ; Computer science ; Machine learning ; Mathematics ; Statistics ; Engineering ; Metallurgy ; Mathematical analysis Hydrogen embrittlement and corrosion behaviors in metals ; High Entropy Alloys Studies ; Microstructure and Mechanical Properties of Steels
Authors: Changzhi Ai | Shuang Han | Xin Yang | Tejs Vegge | Heine Anton Hansen
Source: ACS Applied Materials & Interfaces (article, 2024-03-04)
Field: Materials Science — Materials Chemistry

Graph Neural Network-Accelerated Multitasking Genetic Algorithm for Optimizing Pd <sub> <i>x</i> </sub> Ti <sub> 1– <i>x</i> </sub> H <sub> <i>y</i> </sub> Surfaces under Various CO <sub>2</sub> Reduction Reaction Conditions Palladium (Pd) hydride-based catalysts have been reported to have excellent performance in the CO<sub>2</sub> reduction reaction (CO<sub>2</sub>RR) and hydrogen evolution reaction (HER). Our previous work on doped PdH and Pd alloy hydrides showed that Ti-doped and Ti-alloyed Pd hydrides could improve the performance of the CO<sub>2</sub> reduction reaction compared with pure Pd hydride. Compositions and chemical orderings of the surfaces with only one adsorbate under certain reaction conditions are linked to their stability, activity, and selectivity toward the CO<sub>2</sub>RR and HER, as shown in our previous work. In fact, various coverages, types, and mixtures of the adsorbates, as well as state variables such as temperature, pressure, applied potential, and chemical potential, could impact their stability, activity, and selectivity. However, these factors are usually fixed at common values to reduce the complexity of the structures and the complexity of the reaction conditions in most theoretical work. To address the complexities above and the huge search space, we apply a deep learning-assisted multitasking genetic algorithm to screen for Pd<sub><i>x</i></sub>Ti<sub>1-<i>x</i></sub>H<sub><i>y</i></sub> surfaces containing multiple adsorbates for CO<sub>2</sub>RR under different reaction conditions. The ensemble deep learning model can greatly speed up the structure relaxations and retain a high accuracy and low uncertainty of the energy and forces. The multitasking genetic algorithm simultaneously finds globally stable surface structures under each reaction condition. Finally, 23 stable structures are screened out under different reaction conditions. Among these, Pd<sub>0.56</sub>Ti<sub>0.44</sub>H<sub>1.06</sub> + 25%CO, Pd<sub>0.31</sub>Ti<sub>0.69</sub>H<sub>1.25</sub> + 50%CO, Pd<sub>0.31</sub>Ti<sub>0.69</sub>H<sub>1.25</sub> + 25%CO, and Pd<sub>0.88</sub>Ti<sub>0.12</sub>H<sub>1.06</sub> + 25%CO are found to be very active for CO<sub>2</sub>RR and suitable to generate syngas consisting of CO and H<sub>2</sub>. Materials science ; Artificial neural network ; Human multitasking ; Genetic algorithm ; Graph ; Algorithm ; Computer science ; Theoretical computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalytic Processes in Materials Science