jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ye Sheng | Tingting Deng | Pengfei Qiu | Xun Shi | Jinyang Xi | Yuexing Han | Jiong Yang
Source: Chemistry of Materials (article, 2021-08-25)
Field: Materials Science — Materials Chemistry

Accelerating the Discovery of Cu–Sn–S Thermoelectric Compounds via High-Throughput Synthesis, Characterization, and Machine Learning-Assisted Image Analysis High-throughput (HTP) methods have become a powerful method for accelerating the research and development of materials. In this work, we present an example of the whole HTP chain for seeking novel Cu–Sn–S ternary thermoelectric materials including three parts: HTP synthesis, HTP characterization, and HTP analysis. First, the modified diffusion-couple HTP synthesis method is utilized to obtain a bulk sample with nine different raw material (CuS and SnS) ratios. Then, each segment with a fixed raw material ratio is characterized by scanning electron microscopy, and 11 backscattered electron images are taken (99 in total). Finally, we propose two different strategies to classify the different areas in the 99 backscattered electron images and further identify the different phases. The first strategy applies an active learning loop with a fully connected neural network aiming at fast and automated image segmentation. The second strategy employs an unsupervised clustering method to find potentially overlooked compounds without prior knowledge. Two interesting phases Cu7Sn3S10 and Cu1.6S are found and further analyzed, and the former is characterized to be a potential thermoelectric compound with a zT of over 0.6. Throughput ; Thermoelectric effect ; Characterization (materials science) ; Materials science ; Scanning electron microscope ; Sample (material) ; Cluster analysis ; Ternary operation ; Segmentation ; Artificial neural network ; Computer science ; Raw material ; Image (mathematics) ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Nanotechnology ; Computational science ; Physics ; Composite material ; Chemistry ; Thermodynamics Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: Satyajit Panigrahy | Subrata Karmakar | Rakesh Sahoo
Source: 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) (article, 2021-07-09)
Field: Materials Science — Materials Chemistry

Condition Assessment of High Voltage Insulator using Convolutional Neural Network An insulator can be characterized as a device used for insulating and holding conductors or equipment which are subject to the difference in electric potential as stated in IEC 60050-471-01-10. Airborne contamination and weathering are the major factors that influence the operating condition and service life of the power line insulator. Due to outdoor harsh environment conditions and aging power line insulators deteriorate rapidly and fails before the estimated life. Various defects intended to the severity of outdoor insulation is essential for the prediction and life expectancy of in-service insulators. The application of Deep Learning (DL) which is a subgroup of Machine Learning (ML) helps to predict the power line insulator conditions such as defected insulator, surface corrosion, material degradation, and hydrophobicity loss state of the polymeric insulators etc. This work primarily focused on the image classification (binary and multi-class) of the insulator dataset with the help of Convolutional Neural Network (CNN) by varying different optimizers like Adagrad, Adadelta, Adam, Adamax, Nadam, RMSprop, and Ftrl to find out with which combination of neural network and optimizer will classify more accurately. In this work for the binary image classification case, CNN with Adamax optimizer provided overall accuracy of 95.27% and for the multi-class image classification case, CNN with the same Adamax optimizer gave overall accuracy of 98.20%. Insulator (electricity) ; Convolutional neural network ; Computer science ; Artificial intelligence ; Service life ; Artificial neural network ; Binary classification ; Materials science ; Reliability engineering ; Engineering ; Support vector machine ; Composite material High voltage insulation and dielectric phenomena ; Advanced Neural Network Applications ; Infrastructure Maintenance and Monitoring
Authors: Ruru Gao | Xiu-Shen Wei | Wei Zhao | Aming Xie | Wei Dong
Source: Analytica Chimica Acta (article, 2021-12-02)
Field: Materials Science — Materials Chemistry

Machine learning-assisted array from fluorescent conjugated microporous polymers for multiple explosives recognition Explosive material ; Microporous material ; Chemistry ; Conjugated microporous polymer ; Ranging ; Linear discriminant analysis ; Fluorescence ; Comonomer ; Chromophore ; Conjugated system ; Explosive detection ; Biological system ; Analytical Chemistry (journal) ; Polymer ; Artificial intelligence ; Chromatography ; Organic chemistry ; Computer science ; Optics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Advanced Chemical Sensor Technologies
Authors: Baishakhi Mazumder | Jith Sarker
Source: Journal of materials research/Pratt's guide to venture capital sources (review, 2021-01-15)
Field: Materials Science — Materials Chemistry

Probing structural and chemical evolution in (AlxGa1−x)2O3 using atom probe tomography: A review Abstract (Al x Ga 1− x ) 2 O 3 is a novel ultra‐wide bandgap semiconductor with the potential to dominate future power electronics industries. High‐performance devices demand high Al content in (Al x Ga 1− x ) 2 O 3 but are limited by crystallinity degradation resulting from phase separation. Additionally, the solubility limit of Al is still under debate, and conclusive research is in progress. (Al x Ga 1− x ) 2 O 3 is also limited in high‐frequency applications owing to low carrier mobility and requires n‐type doping. For commercializing this material, the major obstacle is understanding dopant's behavior in the host (Al x Ga 1− x ) 2 O 3 . To investigate these issues, an advanced characterization technique, atom probe tomography (APT), was employed to analyze the structural‐chemical evolution of (Al x Ga 1− x ) 2 O 3 . In this review, we summarized our recent works on the structure‐chemistry investigation of (Al x Ga 1− x ) 2 O 3 with alloy composition and doping interaction. We introduced machine learning algorithms on APT data to reveal unrivaled knowledge, previously not achievable with conventional methodologies. The outstanding capabilities of APT to study (Al x Ga 1− x ) 2 O 3 with Al composition and doping will be considered significant for the wide bandgap semiconductors community. Materials science ; Doping ; Dopant ; Atom probe ; Crystallinity ; Semiconductor ; Atom (system on chip) ; Band gap ; Alloy ; Characterization (materials science) ; Nanotechnology ; Optoelectronics ; Metallurgy ; Computer science Electronic and Structural Properties of Oxides ; Ga2O3 and related materials ; Semiconductor materials and devices
Authors: Gyuyeong Hwang | Taehun Kim | Juyong Shin | Naechul Shin | Sungwon Hwang
Source: Journal of Industrial and Engineering Chemistry (article, 2021-05-28)
Field: Materials Science — Materials Chemistry

Machine learnings for CVD graphene analysis: From measurement to simulation of SEM images Graphene ; Chemical vapor deposition ; Convolutional neural network ; Materials science ; Domain (mathematical analysis) ; Support vector machine ; Artificial neural network ; Artificial intelligence ; Computer science ; Substrate (aquarium) ; Nanotechnology ; Machine learning ; Biological system ; Mathematics Machine Learning in Materials Science ; Graphene research and applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Qian Qiao | Hongtu He | Jiaxin Yu | Yafeng Zhang | Huimin Qi
Source: Wear (article, 2021-03-05)
Field: Materials Science — Ceramics and Composites

Applicability of machine learning on predicting the mechanochemical wear of the borosilicate and phosphate glass Borosilicate glass ; Support vector machine ; Materials science ; Artificial neural network ; Kriging ; Mechanochemistry ; Machine learning ; Artificial intelligence ; Regression ; Linear regression ; Computer science ; Biological system ; Composite material ; Nanotechnology ; Mathematics ; Statistics Glass properties and applications ; Lubricants and Their Additives ; Thermal and Kinetic Analysis
Authors: Austen C. Adams | Sauraj Jha | David J. Lary | Jason D. Slinker
Source: ChemPlusChem (article, 2021-11-12)
Field: Chemistry — Electrochemistry

Machine Learning for Estimating Electron Transfer Rates From Square Wave Voltammetry Electrochemistry of surface-bound molecules is of high importance for numerous electronic and sensor applications. Extracting the electron transfer rate is beneficial for understanding surface-bound processes, but it requires experimental or computational rigor. We evaluate methods to determine electron transfer rates from large voltammetry sets from experiments via machine learning using decision tree ensembles, neural networks, and gaussian process regression models. We applied these to reproduce computational measures of electron transfer rates modeled by first principles. The best machine learning models were a random forest with 80 decision trees and a neural network with Bayesian regularization, producing root mean squared errors of 0.37 and 0.49 s<sup>-1</sup> , respectively, corresponding to mean percent errors of 0.38 % and 0.52 %, respectively. This work establishes machine learning methods for rapidly acquiring electron transfer rates across large datasets for widespread applications. Electron transfer ; Square (algebra) ; Voltammetry ; Electron ; Square wave ; Computer science ; Electrochemistry ; Materials science ; Chemistry ; Physics ; Photochemistry ; Physical chemistry ; Mathematics ; Quantum mechanics ; Electrode ; Voltage Electrochemical Analysis and Applications ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Ze-Yi Zhu | Yefei Li | Cheng Shang | Zhi‐Pan Liu
Source: The Journal of Physical Chemistry C (article, 2021-07-30)
Field: Materials Science — Materials Chemistry

Thermodynamics and Catalytic Activity of Ruthenium Oxides Grown on Ruthenium Metal from a Machine Learning Atomic Simulation Ru is a poor catalyst for oxidizing CO under ultra-high-vacuum conditions but exhibits high catalytic activity under ambient O2 pressure. The system is thus regarded as a prototypical system to understand the catalysis of oxides grown on metal. While previous works focus on either bulky metal or bulky oxides, here, we explore the atomic structure of Ru/RuO2 interfaces and the active site for CO oxidation by using stochastic surface walking global optimization combined with a global neural network potential (SSW-NN) method. Five Ru/RuO2 interfaces are identified with the modified phenomenological theory of martensitic crystallography, and the two most stable interfaces are Ru(101̅0)//RuO2(101) and Ru (0001)//RuO2(101), both involving the less stable oxide (101) plane. By gradually growing RuOx on Ru(101̅0) and evaluate the thermodynamics, we now complete the atomic picture for oxide growth on Ru metal. The oxide starts to form after 1.75 ML O coverage, and a thin film of RuO2(101) appears immediately after 2 ML O coverage; these RuOx thin layers are, however, thermodynamically unstable and tend to aggregate into thick RuO2 islands due to the >4% lattice mismatch. Importantly, our catalytic activity analyses for CO oxidation on different surface sites demonstrate that the metastable thin layer RuO2 on Ru is catalytically even more active than bulky oxide surfaces due to the lattice compression of the oxide film. These results indicate the significance to search for metastable structures at phase boundaries in real catalysts. The advent of global neural network simulations provides a viable route to probe the complex catalysis phenomena occurring at phase boundaries/junctions. Ruthenium oxide ; Oxide ; Catalysis ; Ruthenium ; Metastability ; Metal ; Oxidizing agent ; Materials science ; Thin film ; Chemical engineering ; Chemical physics ; Chemistry ; Nanotechnology ; Metallurgy Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Hankook Lee | Sung Soo Ahn | Seung-Woo Seo | You Young Song | Eunho Yang | Sung Ju Hwang | Jinwoo Shin
Source:  (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning Retrosynthesis, of which the goal is to find a set of reactants for synthesizing a target product, is an emerging research area of deep learning. While the existing approaches have shown promising results, they currently lack the ability to consider availability (e.g., stability or purchasability) of the reactants or generalize to unseen reaction templates (i.e., chemical reaction rules). In this paper, we propose a new approach that mitigates the issues by reformulating retrosynthesis into a selection problem of reactants from a candidate set of commercially available molecules. To this end, we design an efficient reactant selection framework, named RetCL (retrosynthesis via contrastive learning), for enumerating all of the candidate molecules based on selection scores computed by graph neural networks. For learning the score functions, we also propose a novel contrastive training scheme with hard negative mining. Extensive experiments demonstrate the benefits of the proposed selection-based approach. For example, when all 671k reactants in the USPTO database are given as candidates, our RetCL achieves top-1 exact match accuracy of 71.3% for the USPTO-50k benchmark, while a recent transformer-based approach achieves 59.6%. We also demonstrate that RetCL generalizes well to unseen templates in various settings in contrast to template-based approaches. Retrosynthetic analysis ; Computer science ; Benchmark (surveying) ; Selection (genetic algorithm) ; Artificial intelligence ; Machine learning ; Template ; Set (abstract data type) ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Rohit Modee | Sheena Agarwal | Ashwini Verma | Kavita Joshi | U. Deva Priyakumar
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

DART: deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers Recently, machine learning (ML) has proven to yield fast and accurate predictions of chemical properties to accelerate the discovery of novel molecules and materials. The majority of the work is on organic molecules, and much more work needs to be done for inorganic molecules, especially clusters. In the present work, we introduce a simple topological atomic descriptor called TAD, which encodes chemical environment information of each atom in the cluster. TAD is a simple and interpretable descriptor where each value represents the atom count in three shells. We also introduce the DART deep learning enabled topological interaction model, which uses TAD as a feature vector to predict energies of metal clusters, in our case gallium clusters with sizes ranging from 31 to 70 atoms. The DART model is designed based on the principle that the energy is a function of atomic interactions and allows us to model these complex atomic interactions to predict the energy. We further introduce a new dataset called GNC_31-70, which comprises structures and DFT optimized energies of gallium clusters with sizes ranging from 31 to 70 atoms. We show how DART can be used to accelerate the process of identification of low energy structures without geometry optimization. Albeit using a topological descriptor, DART achieves a mean absolute error (MAE) of 3.59 kcal mol<sup>-1</sup> (0.15 eV) on the test set. We also show that our model can distinguish core and surface atoms in the Ga-70 cluster, which the model has never encountered earlier. Finally, we demonstrate the transferability of the DART model by predicting energies for about 6k unseen configurations picked up from molecular dynamics (MD) data for three cluster sizes (46, 57, and 60) within seconds. The DART model was able to reduce the load on DFT optimizations while identifying unique low energy structures from MD data. Energy (signal processing) ; Metal ; Chemical physics ; Materials science ; Computer science ; Computational chemistry ; Artificial intelligence ; Chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yang Hong | Dan Han | Bo Hou | Xinyu Wang | Jingchao Zhang
Source: International Journal of Heat and Mass Transfer (article, 2021-02-20)
Field: Materials Science — Materials Chemistry

High-Throughput Computations of Cross-Plane Thermal Conductivity in Multilayer Stanene Materials science ; Thermal conductivity ; Computation ; Ultimate tensile strength ; Throughput ; Artificial neural network ; Thermal ; Coupling (piping) ; Computer science ; Electronic structure ; Conductivity ; Composite material ; Optoelectronics ; Artificial intelligence ; Algorithm ; Condensed matter physics ; Physics ; Thermodynamics ; Telecommunications Thermal properties of materials ; Machine Learning in Materials Science ; Graphene research and applications
Authors: Kengo Manabe | Miki Nakano | Koji Miyake | Yasuo Norikane
Source: Chemical Engineering Journal (article, 2021-05-28)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired extremely rapid self-repairing coatings for long-life repeated features Materials science ; Nanotechnology Surface Modification and Superhydrophobicity ; Polymer composites and self-healing ; Polymer Surface Interaction Studies
Authors: Atsushi Kubo | Yoshitaka Umeno
Source: Materials (article, 2021-03-25)
Field: Materials Science — Ceramics and Composites

Machine-Learning-Based Atomistic Model Analysis on High-Temperature Compressive Creep Properties of Amorphous Silicon Carbide Ceramic matrix composites (CMCs) based on silicon carbide (SiC) are used for high-temperature applications such as the hot section in turbines. For such applications, the mechanical properties at a high temperature are essential for lifetime prediction and reliability design of SiC-based CMC components. We developed an interatomic potential function based on the artificial neural network (ANN) model for silicon-carbon systems aiming at investigation of high-temperature mechanical properties of SiC materials. We confirmed that the developed ANN potential function reproduces typical material properties of the single crystals of SiC, Si, and C consistent with first-principles calculations. We also validated applicability of the developed ANN potential to a simulation of an amorphous SiC through the analysis of the radial distribution function. The developed ANN potential was applied to a series of creep test for an amorphous SiC model, focusing on the amorphous phase, which is expected to be formed in the SiC-based composites. As a result, we observed two types of creep behavior due to different atomistic mechanisms depending on the strain rate. The evaluated activation energies are lower than the experimental values in literature. This result indicates that an amorphous region can play an important role in the creep process in SiC composites. Materials science ; Silicon carbide ; Creep ; Amorphous solid ; Composite material ; Ceramic ; Amorphous silicon ; Carbide ; Ceramic matrix composite ; Silicon ; Crystalline silicon ; Metallurgy ; Crystallography Advanced ceramic materials synthesis ; Advanced materials and composites ; Thermal properties of materials
Authors: Nicodemo Di Pasquale | Joshua D. Elliott | Panagiotis Hadjidoukas | Paola Carbone
Source: Journal of Chemical Theory and Computation (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

Dynamically Polarizable Force Fields for Surface Simulations via Multi-output Classification Neural Networks We present a general procedure to introduce electronic polarization into classical Molecular Dynamics (MD) force fields using a Neural Network (NN) model. We apply this framework to the simulation of a solid-liquid interface where the polarization of the surface is essential to correctly capture the main features of the system. By introducing a multi-input, multi-output NN and treating the surface polarization as a discrete classification problem, we are able to obtain very good accuracy in terms of quality of predictions. Through the definition of a custom loss function we are able to impose a physically motivated constraint within the NN itself making this model extremely versatile, especially in the modeling of different surface charge states. The NN is validated considering the redistribution of electronic charge density within a graphene based electrode in contact with an aqueous electrolyte solution, a system highly relevant to the development of next generation low-cost supercapacitors. We compare the performances of our NN/MD model against Quantum Mechanics/Molecular Dynamics simulations where we obtain a most satisfactory agreement. Artificial neural network ; Computer science ; Molecular dynamics ; Polarizability ; Polarization (electrochemistry) ; Statistical physics ; Physics ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Machine Learning and ELM
Authors: Andrea Martini | Alexander A. Guda | С. А. Гуда | Aram L. Bugaev | Оlga V. Safonova | А. В. Солдатов
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine learning powered by principal component descriptors as the key for sorted structural fit of XANES Modern synchrotron radiation sources and free electron laser made X-ray absorption spectroscopy (XAS) an analytical tool for the structural analysis of materials under in situ or operando conditions. Fourier approach applied to the extended region of the XAS spectrum (EXAFS) allows the estimation of the number of structural and non-structural parameters which can be refined through a fitting procedure. The near edge region of the XAS spectrum (XANES) is also sensitive to the coordinates of all the atoms in the local cluster around the absorbing atom. However, in contrast to EXAFS, the existing approaches of quantitative analysis provide no estimation for the number of structural parameters that can be evaluated for a given XANES spectrum. This problem exists both for the classical gradient descent approaches and for modern machine learning methods based on neural networks. We developed a new approach for rational fit based on principal component descriptors of the spectrum. In this work the principal component analysis (PCA) is applied to a dataset of theoretical spectra calculated a priori on a grid of variable structural parameters of a molecule or cluster. Each principal component of the dataset is related then to a combined variation of several structural parameters, similar to the vibrational normal mode. Orthogonal principal components determine orthogonal deformations that can be extracted independently upon the analysis of the XANES spectrum. Applying statistical criteria, the PCA-based fit of the XANES determines the accessible structural information in the spectrum for a given system. XANES ; Principal component analysis ; Key (lock) ; Component (thermodynamics) ; Computer science ; Pattern recognition (psychology) ; Biological system ; Artificial intelligence ; Chemistry ; Spectroscopy ; Physics ; Thermodynamics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics