jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Upadesh Subedi | Sachin Poudel | Khem Gyanwali | Yuri Amorim Coutinho | G. Matula | Anil Kunwar
Source: Metals (article, 2022-11-04)
Field: Materials Science — Materials Chemistry

State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning Though the martensitic transformation has been a commonly investigated topic in the field of experimental and computational materials science, the understanding of this mechanism in a variety of alloys is yet far from complete. In this era of Industry 4.0, there have been ongoing trends on employing machine learning (ML) techniques for the study of the martensitic alloys, and such data-driven approaches are expected to unravel a great amount of information about the process-structure-property behaviour relationship in this class of materials. However, with the availability of a large variety of datasets and with an option to use different ML models, a bulk amount of information has already been generated with regard to martensitic alloys. The discovery and design of shape memory alloys can be accelerated if the multi-principal element functional alloys and martensitic transformation phenomenon are studied extensively using machine learning techniques. Thus, it is necessary to highlight the major categories or aspects of these alloys that have been predicted with ML. The present work performs a state-of-the-art review on the machine learning models developed for the quantification of aspects such as martensitic start temperature (Ms), materials properties, microstructure, mechanisms etc., on the alloys. Diffusionless transformation ; Martensite ; Shape-memory alloy ; Variety (cybernetics) ; Materials science ; Principal (computer security) ; Machine learning ; Artificial intelligence ; Microstructure ; Field (mathematics) ; Computer science ; Metallurgy ; Mathematics Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Titanium Alloys Microstructure and Properties
Authors: Andrij Vasylenko | Dmytro Antypov | Vladimir V. Gusev | Michael W. Gaultois | Matthew S. Dyer | Matthew J. Rosseinsky
Source: Research Square (preprint, 2022-02-18)
Field: Materials Science — Materials Chemistry

Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties Selection (genetic algorithm) ; Element (criminal law) ; Computer science ; Machine learning ; Nanotechnology ; Materials science ; Political science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Duozheng Ma | Xin Li | Jun Liang | Zhendong Wang | Weimin Yang
Source: Microporous and Mesoporous Materials (article, 2022-05-31)
Field: Chemistry — Inorganic Chemistry

Distilling seed-assisted zeolite synthesis conditions by machine learning Zeolite ; Crystallization ; Transformation (genetics) ; Domain (mathematical analysis) ; Materials science ; Chemical engineering ; Computer science ; Process engineering ; Nanotechnology ; Artificial intelligence ; Chemistry ; Engineering ; Mathematics ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Sheng Gong | Tian Xie | Yang Shao‐Horn | Rafael Gómez‐Bombarelli | Jeffrey C. Grossman
Source: Research Square (preprint, 2022-10-06)
Field: Materials Science — Materials Chemistry

Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity Graph ; Artificial neural network ; Computer science ; Theoretical computer science ; Artificial intelligence ; Data science ; Cognitive science ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xiangdong Wang | Tian Lu | Wenyan Zhou | Xiaobo Ji | Wencong Lu | Jiong Yang
Source: Chemistry - An Asian Journal (article, 2022-09-11)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Ternary Gold Alloy Materials with Low Resistivity via an Interpretable Machine Learning Strategy New ternary gold alloys with low resistivities (ρ) were screened out via an interpretable machine learning strategy by using the support vector regression (SVR) model integrated with SHAP analysis. The correlation coefficient (R) and the root mean square error (RMSE) of test set were 0.876 and 0.302, respectively, indicating the strong generalization ability of the model. The average ρ of top 10 candidates was 1.22×10<sup>-7</sup> Ω m, which was 41% lower than the known minimum of 2.08×10<sup>-7</sup> Ω m. The outputs of SVR model were analyzed with the critical SHAP values including first ionization energy of C-site (584 kJ ⋅ mol<sup>-1</sup> ), electronegativity of C-site (1.72) and the second ionization energy of B-site (1135 kJ ⋅ mol<sup>-1</sup> ), respectively. Moreover, an online web server was developed to share the model at http://materials-data-mining.com/onlineservers/wxdaualloy. Electronegativity ; Ternary operation ; Support vector machine ; Mean squared error ; Ionization energy ; Test set ; Electrical resistivity and conductivity ; Generalization ; Correlation coefficient ; Coefficient of determination ; Ionization ; Materials science ; Machine learning ; Analytical Chemistry (journal) ; Mathematics ; Computer science ; Chemistry ; Statistics ; Physics ; Mathematical analysis ; Ion Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques ; Electrocatalysts for Energy Conversion
Authors: Ebrahim Tangestani | Samira Ghanbarzadeh | Javier Fernández-García
Source: Catalysis Letters (article, 2022-05-07)
Field: Materials Science — Materials Chemistry

Prediction of Catalytic Hydrogen Generation by Water–Gas Shift Reaction Using a Neural Network Approach Water-gas shift reaction ; Artificial neural network ; Chemistry ; Catalysis ; Multilayer perceptron ; Sensitivity (control systems) ; Biological system ; Hydrogen ; Reliability (semiconductor) ; Water gas ; Reaction conditions ; Work (physics) ; Process engineering ; Power (physics) ; Computer science ; Artificial intelligence ; Thermodynamics ; Syngas ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Hrishikesh Joshi | Nicole Wilde | Thomas S. Asche | Dorit Wolf
Source: Chemie Ingenieur Technik (article, 2022-10-11)
Field: Materials Science — Materials Chemistry

Developing Catalysts via Structure‐Property Relations Discovered by Machine Learning: An Industrial Perspective Abstract Industrial catalyst development is a complex issue that requires optimization of performance, synthesis, costs, and engineering aspects. During the development, structure‐property relations are often used to provide valuable insights into the catalyst. However, conventionally, this process is time‐consuming and costly. Advancements in the field of automation for experimentation, data collection, and simulations have allowed the use of machine learning (ML) strategies for this development. Herein we provide an industrial perspective on ML strategies for the development of solid catalysts. Automation ; Perspective (graphical) ; Field (mathematics) ; Property (philosophy) ; Process (computing) ; Development (topology) ; Computer science ; Catalysis ; Manufacturing engineering ; Industrial engineering ; Engineering ; Biochemical engineering ; Artificial intelligence ; Process engineering ; Chemistry ; Mechanical engineering ; Mathematics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Hieu A. Doan | Chenyang Li | Logan Ward | Mingxia Zhou | Larry A. Curtiss | Rajeev S. Assary
Source: Digital Discovery (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

Accelerating the evaluation of crucial descriptors for catalyst screening <i>via</i> message passing neural network Graph neural networks developed for adsorption energy prediction on molybdenum carbide catalysts provide a significant acceleration over density functional theory calculations. Artificial neural network ; Catalysis ; Molybdenum ; Density functional theory ; Computer science ; Adsorption ; Energy (signal processing) ; Graph ; Materials science ; Chemistry ; Artificial intelligence ; Computational chemistry ; Mathematics ; Inorganic chemistry ; Theoretical computer science ; Physical chemistry ; Statistics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalysis and Oxidation Reactions
Authors: P. H. R. Amaral | Alvaro David Torrez Baptista | Dawany Dionísio | Thiago Lopes | Júlio Romano Meneghini | Caetano R. Miranda
Source: Journal of The Electrochemical Society (article, 2022-11-01)
Field: Materials Science — Materials Chemistry

A Machine Learning Model for Adsorption Energies of Chemical Species Applied to CO <sub>2</sub> Electroreduction Machine learning methods are applied to obtain adsorption energies of different chemical species on (100), (111), and (211) FCC surfaces of several transition metals and Pb. Based on information available in databases containing adsorption energies obtained via first-principles calculations, we implemented MLPRegressor, XGBRegressor, Support Vector Regressor, and Stacking machine learning models. The fourth model is created from the combination of the previous three through a Stacking technique. In a broader context, our results showed the robustness of machine learning models and the ability of these methods to speed up the screening materials to specific goals, at a low computational cost. We emphasize the ability of our models to predict the adsorption energy for different systems. Due to their generality of them, we were able to make ion predictions on metallic surfaces, taking into account the influence of different functionals. This capability is of special significance due to the difficulty of calculating the correct energy for charged systems by traditional atomistic simulations. From then on, we made predictions for important chemical species in the CO 2 electroreduction process, such as the radical anion CO 2 −• , an important intermediary for obtaining new products in view of a negative carbon footprint. Adsorption ; Robustness (evolution) ; Stacking ; Generality ; Support vector machine ; Computer science ; Ion ; Chemistry ; Materials science ; Machine learning ; Physical chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Photocatalysis Techniques
Authors: Yunhao Xu | Xun Huang | Cunpu Li | Zidong Wei | Meng Wang
Source: AIChE Journal (article, 2022-04-08)
Field: Materials Science — Materials Chemistry

Predicting structure‐dependent properties directly from the three dimensional molecular images via convolutional neural networks Abstract Machine learning (ML) provides an efficient method to predict the unknown properties during the exploration of new materials, but how to efficiently represent the molecules as input is still not fully solved. Inspired by image processing, one of the classical ML tasks, this work developed a method to predict the structure‐dependent properties by converting the atom position into a three‐dimensional (3D) molecular image and learning the structure features from the image via a classical convolutional neural networks. After trained with datasets larger than 12,000 species, a very high accuracy is obtained in predicting both theoretical molecular energy and experimental properties including melting points, boiling points, and flash points. Since stereoscopic information is explicitly and accurately represented by the molecular images, our model successfully distinguish the melting points and boiling points of molecules with similar structure, including those of trans–cis isomers. Convolutional neural network ; Boiling point ; Computer science ; Artificial neural network ; Image (mathematics) ; Melting point ; Artificial intelligence ; Atom (system on chip) ; Biological system ; Algorithm ; Molecule ; Position (finance) ; Pattern recognition (psychology) ; Materials science ; Chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Keyou Mao | Tyler Gerczak | Jason Harp | Casey McKinney | Timothy G. Lach | Ömer Karakoç | Andrew Nelson | Kurt A. Terrani | Chad M. Parish | Philip D. Edmondson
Source: Communications Materials (article, 2022-04-19)
Field: Materials Science — Materials Chemistry

Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning Abstract Characterizing oxide nuclear fuels is difficult due to complex fission products, which result from time-evolving system chemistry and extreme operating environments. Here, we report a machine learning-enhanced approach that accelerates the characterization of spent nuclear fuels and improves the accuracy of identifying nanophase fission products and bubbles. We apply this approach to commercial, high-burnup, irradiated light-water reactor fuels, demonstrating relationships between fission product precipitates and gases. We also gain understanding of the fission versus decay pathways of precipitates across the radius of a fuel pellet. An algorithm is provided for quantifying the chemical segregation of the fission products with respect to the high-burnup structure, which enhances our ability to process large amounts of microscopy data, including approaching the atomistic-scale. This may provide a faster route for achieving physics-based fuel performance modeling. Burnup ; Nuclear fission product ; Fission products ; Fission ; Nuclear fuel ; Materials science ; Nuclear engineering ; Nuclear physics ; Physics ; Engineering ; Neutron Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Hydrocarbon exploration and reservoir analysis
Authors: Sumbel Ijaz | Sadia Noureen | Bacha Rehman | Muhammad Zubair | Muhammad Qasim Mehmood | Yehia Massoud
Source:  (article, 2022-12-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of machine-learning techniques for characteristic analysis of refractory materials Flat optics have become capable of achieving unprecedented functionalities through electromagnetic (EM) wave manipulation by employing the metasurfaces. The most crucial part in the design of metasurface is the selection the constitutive component i.e. the meta-atom’s material and structure so that it exhibits the precise operation as per the desired application. The unit-cell design calls for an iterative loop of simulations in order to explore the EM responses for intended operation. In this work, we have studied the absorption response of refractory materials under visible light radiations for their utilization in energy harvesting applications. The absorption response estimation using machine-learning techniques for the materials having very high melting-points, mechanical stabilities and inertness to the atmosphere has been carried out to investigate their performance in the broadband range. The presented regression models incorporate hybrid data format i.e. they simultaneously contend with 3-D and 1-D properties of various shapes of nano-resonators. The images’ feature extraction is carried out by employing Singular Value Decomposition. The trained models are potent enough to bypass the repetitive sequence of optimization involved in conventional EM solvers. Additionally, the models are capable of predicting the optimum shape along with structural dimensions of unit-cell. For forward model, the MSEs for training and testing are 1.302×10<sup>-2</sup> and 3.269×10<sup>-2</sup> while R<sup>2</sup> scores are 0.9804 and 0.8764, respectively. The approach applied is so robust that, irrespective of complexity of unit-cell structure is, it serves the purpose of predicting the distinct structure with highest performance while bypassing the time- and computationally-intensive EM simulations. Absorption (acoustics) ; Computer science ; Singular value decomposition ; Mechanical engineering ; Algorithm ; Materials science ; Optics ; Physics ; Engineering Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Sanha Lee | Kristaps Ermanis | Jonathan M. Goodman
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

MolE8: finding DFT potential energy surface minima values from force-field optimised organic molecules with new machine learning representations The use of machine learning techniques in computational chemistry has gained significant momentum since large molecular databases are now readily available. Predictions of molecular properties using machine learning have advantages over the traditional quantum mechanics calculations because they can be cheaper computationally without losing the accuracy. We present a new extrapolatable and explainable molecular representation based on bonds, angles and dihedrals that can be used to train machine learning models. The trained models can accurately predict the electronic energy and the free energy of small organic molecules with atom types C, H N and O, with a mean absolute error of 1.2 kcal mol<sup>-1</sup>. The models can be extrapolated to larger organic molecules with an average error of less than 3.7 kcal mol<sup>-1</sup> for 10 or fewer heavy atoms, which represent a chemical space two orders of magnitude larger. The rapid energy predictions of multiple molecules, up to 7 times faster than previous ML models of similar accuracy, has been achieved by sampling geometries around the potential energy surface minima. Therefore, the input geometries do not have to be located precisely on the minima and we show that accurate density functional theory energy predictions can be made from force-field optimised geometries with a mean absolute error 2.5 kcal mol<sup>-1</sup>. Maxima and minima ; Force field (fiction) ; Potential energy surface ; Molecule ; Field (mathematics) ; Surface (topology) ; Energy (signal processing) ; Organic molecules ; Potential energy ; Computational chemistry ; Chemical physics ; Materials science ; Computer science ; Statistical physics ; Physics ; Chemistry ; Artificial intelligence ; Mathematics ; Classical mechanics ; Quantum mechanics ; Geometry ; Mathematical analysis ; Pure mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Xiaoxiu Wei | Jianfeng Wang | Chao Wang | Shijie Zhu | Liguo Wang | Shaokang Guan
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2022-09-29)
Field: Materials Science — Biomaterials

Prediction of electronic work function of the second phase in binary magnesium alloy based on machine learning method Gradient boosting ; Support vector machine ; Boosting (machine learning) ; Materials science ; Decision tree ; Linear regression ; Machine learning ; Regression analysis ; Work (physics) ; Artificial intelligence ; Function (biology) ; Computer science ; Thermodynamics ; Random forest ; Physics Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Metal and Thin Film Mechanics
Authors: Gyoung S. Na
Source: Computational Materials Science (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

Substructure interaction graph network with node augmentation for hybrid chemical systems of heterogeneous substructures Complex chemical systems containing multiple heterogeneous substructures are common in real-world chemical applications, such as hybrid perovskites and inorganic catalysts. Although graph neural networks (GNNs) have achieved numerous successes in predicting the physical and chemical properties of a single molecule or crystal structure, GNNs for multiple heterogeneous substructures have not yet been studied in chemical science. In this paper, we propose substructure interaction graph network with node augmentation (SIGNNA) that is an integrated architecture of heterogeneous GNNs to predict the physical and chemical properties from the interactions between the heterogeneous substructures of the chemical systems. In addition to the network architecture, we devise a node augmentation method to generate valid subgraphs from given chemical systems for graph-based machine learning, even though the decomposed substructures are physically invalid. SIGNNA outperformed state-of-the-art GNNs in the experimental evaluations and the high-throughput screening on benchmark materials datasets of hybrid organic–inorganic perovskites and inorganic catalysts. The source code of SIGNNA is publicly available at https://github.com/ngs00/signna. Substructure ; Computer science ; Graph ; Heterogeneous network ; Node (physics) ; Theoretical computer science ; Physics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Graph Neural Networks
Authors: Nikhil V. S. Avula | Shivanand Kumar Veesam | Sudarshan Behera | Sundaram Balasubramanian
Source: Machine Learning Science and Technology (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids Abstract Shear viscosity, though being a fundamental property of all fluids, is computationally expensive to calculate from equilibrium molecular dynamics simulations. Recently, machine learning (ML) methods have been used to augment molecular simulations in many contexts, thus showing promise to estimate viscosity too in a relatively inexpensive manner. However, ML methods face significant challenges—such as overfitting, when the size of the data set is small, as is the case with viscosity. In this work, we train seven ML models to predict the shear viscosity of a Lennard–Jones fluid, with particular emphasis on addressing issues arising from a small data set. Specifically, the issues related to model selection, performance estimation and uncertainty quantification were investigated. First, we show that the widely used performance estimation procedure of using a single unseen data set shows a wide variability—in estimating the errors on—small data sets. In this context, the common practice of using cross validation (CV) to select the hyperparameters (model selection) can be adapted to estimate the generalization error (performance estimation) as well. We compare two simple CV procedures for their ability to do both model selection and performance estimation, and find that k-fold CV based procedure shows a lower variance of error estimates. Also, these CV procedures naturally lead to an ensemble of trained ML models. We discuss the role of performance metrics in training and evaluation and propose a method to rank the ML models based on multiple metrics. Finally, two methods for uncertainty quantification—Gaussian process regression (GPR) and ensemble method—were used to estimate the uncertainty on individual predictions. The uncertainty estimates from GPR were also used to construct an applicability domain using which the ML models provided even more reliable predictions on an independent viscosity data set generated in this work. Overall, the procedures prescribed in this work, together, lead to robust ML models for small data sets. Overfitting ; Hyperparameter ; Computer science ; Machine learning ; Context (archaeology) ; Model selection ; Generalization ; Artificial intelligence ; Uncertainty quantification ; Robustness (evolution) ; Algorithm ; Mathematics ; Artificial neural network ; Chemistry Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Computational Drug Discovery Methods