jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rostislav Fedorov | Ganna Gryn’ova
Source: Journal of Chemical Theory and Computation (review, 2023-07-18)
Field: Materials Science — Materials Chemistry

Unlocking the Potential: Predicting Redox Behavior of Organic Molecules, from Linear Fits to Neural Networks Redox-active organic molecules, <i>i</i>.<i>e</i>., molecules that can relatively easily accept and/or donate electrons, are ubiquitous in biology, chemical synthesis, and electronic and spintronic devices, such as solar cells and rechargeable batteries, etc. Choosing the best candidates from an essentially infinite chemical space for experimental testing in a target application requires efficient screening approaches. In this Review, we discuss modern <i>in silico</i> techniques for predicting reduction and oxidation potentials of organic molecules that go beyond conventional first-principles computations and thermodynamic cycles. Approaches ranging from simple linear fits based on molecular orbital energy approximation and energy difference approximation to advanced regression and neural network machine learning algorithms employing complex descriptors of molecular compositions, geometries, and electronic structures are examined in conjunction with relevant literature examples. We discuss the interplay between <i>ab initio</i> data and machine learning (ML), i.e., whether it is better to base predictions on low-level quantum-chemical results corrected with ML or to bypass first-principles computations entirely and instead rely on elaborate deep learning architectures. Finally, we list currently available data sets of redox-active organic molecules and their experimental and/or computed properties to facilitate the development of screening platforms and rational design of redox-active organic molecules. Chemical space ; Computer science ; Artificial neural network ; Organic molecules ; Ab initio ; Molecule ; Computation ; Biological system ; Artificial intelligence ; Machine learning ; Computational chemistry ; Chemistry ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Reisel Millán | Estefanía Bello‐Jurado | Manuel Moliner | Mercedes Boronat | Rafael Gómez‐Bombarelli
Source: ACS Central Science (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

Effect of Framework Composition and NH<sub>3</sub> on the Diffusion of Cu<sup>+</sup> in Cu-CHA Catalysts Predicted by Machine-Learning Accelerated Molecular Dynamics Cu-exchanged zeolites rely on mobile solvated Cu<sup>+</sup> cations for their catalytic activity, but the role of the framework composition in transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length and time scales or diverse compositions. We report a machine-learning interatomic potential that accurately reproduces ab initio results and effectively generalizes to allow multinanosecond simulations of large supercells and diverse chemical compositions. Biased and unbiased simulations of [Cu(NH<sub>3</sub>)<sub>2</sub>]<sup>+</sup> mobility show that aluminum pairing in eight-membered rings accelerates local hopping and demonstrate that increased NH<sub>3</sub> concentration enhances long-range diffusion. The probability of finding two [Cu(NH<sub>3</sub>)<sub>2</sub>]<sup>+</sup> complexes in the same cage, which is key for SCR-NOx reaction, increases with Cu content and Al content but does not correlate with the long-range mobility of Cu<sup>+</sup>. Supporting experimental evidence was obtained from reactivity tests of Cu-CHA catalysts with a controlled chemical composition. Molecular dynamics ; Diffusion ; Catalysis ; Reactivity (psychology) ; Ab initio ; Chemistry ; Chemical physics ; Composition (language) ; Interatomic potential ; Range (aeronautics) ; Computational chemistry ; Materials science ; Thermodynamics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Chaoqiang Feng | Jin Xi | Yaolong Zhang | Bin Jiang | Yong Zhou
Source: Journal of Chemical Theory and Computation (article, 2023-02-08)
Field: Materials Science — Materials Chemistry

Accurate and Interpretable Dipole Interaction Model-Based Machine Learning for Molecular Polarizability Polarizabilities play significant roles in describing dispersive and inductive interactions of the atom and molecular systems. However, an accurate prediction of molecular polarizabilities from first principles is computationally prohibitive. Although physical models or statistical machine learning models have been proposed, either a lack of accurate description of local chemical environments or demanding a large number of samples for training has limited their practical applications. In this study, we combine a physically inspired dipole interaction model and an accurate neural network method for predicting the polarizability tensors of molecules. With the local chemical environment precisely described and the requirement of rotational covariance naturally fulfilled, this hybrid model is proven to give an accurate molecular polarizability prediction, essentially reducing the number of training samples. The atomic polarizabilities are physically interpretable and transferable to larger molecules unseen in the training set. This promising method may find its wide range of applications, such as spectroscopic simulations and the construction of polarizable force fields. Polarizability ; Dipole ; Computer science ; Machine learning ; Artificial neural network ; Range (aeronautics) ; Set (abstract data type) ; Atom (system on chip) ; Artificial intelligence ; Training set ; Molecule ; Molecular dynamics ; Statistical physics ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Wenwen Luan | Li Sun | Zuoxiang Zeng | Weilan Xue
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Optimization of a polyvinyl butyral synthesis process based on response surface methodology and artificial neural network High quality polyvinyl butyral (PVB) can be used as the intermediate film of automobile and building glass and the packaging film of photovoltaic cells. Therefore, it is necessary to optimize its synthesis process to obtain suitable products with a high acetalization degree (AD) and small particle size (<i>d</i> <sub>p</sub>). In this work, a deep eutectic solvent (DES) was selected as the catalyst, and response surface methodology (RSM) and artificial neural network (ANN) were utilized to optimize the synthesis process of PVB. The concentration of polyvinyl alcohol (<i>A</i>), the dosage of DES (<i>B</i>) and <i>n</i>-butanal (<i>C</i>), and the aging temperature (<i>D</i>) were selected as process variables, and the comprehensive score (AD, <i>d</i> <sub>p</sub> and material and energy consumption) was introduced as the response. The results showed that single-factors <i>B</i>, <i>C</i>, <i>D</i>, and the interactions <i>AB</i>, <i>BC</i> and <i>CD</i> had significant effects on the comprehensive score, and the qualified PVB products (AD > 81%, <i>d</i> <sub>p</sub> = 3-3.5 μm) were obtained under the optimal conditions obtained by RSM and ANN models. ANN is a better and more precise optimization tool than RSM. Also, DES played a dual role in catalysis and dispersion in the synthesis of PVB and showed good reusability, so it has great application potential in PVB industrial production. Response surface methodology ; Polyvinyl butyral ; Artificial neural network ; Deep eutectic solvent ; Eutectic system ; Materials science ; Process (computing) ; Chemical engineering ; Computer science ; Biological system ; Process engineering ; Artificial intelligence ; Engineering ; Machine learning ; Composite material ; Biology Polymer Science and PVC ; Environmental Impact and Sustainability ; Advanced Polymer Synthesis and Characterization
Authors: Elaheh Kazemi-Khasragh | Juan P. Fernandéz‐Blázquez | David Garoz Gómez | Carlos González | Maciej Harańczyk
Source: International Journal of Solids and Structures (article, 2023-10-31)
Field: Materials Science — Materials Chemistry

Facilitating polymer property prediction with machine learning and group interaction modelling methods Property (philosophy) ; Group (periodic table) ; Polymer ; Artificial intelligence ; Materials science ; Computer science ; Machine learning ; Biological system ; Engineering ; Composite material ; Chemistry ; Biology Machine Learning in Materials Science ; Software Engineering Research ; Computational Drug Discovery Methods
Authors: Yong Niu | Zhi-qiang Hong | Yaoqi Wang | Yanchun Zhu
Source: Journal of Materials Research and Technology (article, 2023-01-06)
Field: Materials Science — Materials Chemistry

Machine learning-based beta transus temperature prediction for titanium alloys Beta transus temperature (βtr) is one of the most crucial features of titanium alloys. It is typically used as the index while designing the heat treatment process for titanium alloys. The βtr is also a significant parameter to optimize the processing technology of titanium alloys. Four machine learning algorithms and one empirical formula is developed in this study to estimate the βtr of titanium alloys: Artificial Neural Networks (ANN), Gauss Processing Regression (GPR), Super Vector Machine (SVM), and Ensemble Regression Trees (ERT). According to the correlation coefficient (R), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to verify the accuracy of models, and the experimentally measured phase transition temperature of Ti600 alloy was also used to verify the generalization ability of the model. Choosing the best model to analyze the sensitivity of the elements and determine how each component affects the βtr. The result demonstrated that the ANN model has the highest prediction accuracy among the five models, and different model structures have different effects on predicting new data. The ANN model with 10 neurons has the highest prediction accuracy, while the ANN model with 8 neurons has the strongest generalization ability. The results of the sensitivity analysis proved that all the alloy compositions used as input parameters were valid parameters. Titanium alloy ; Materials science ; Artificial neural network ; Mean squared error ; Support vector machine ; Titanium ; Generalization ; Correlation coefficient ; Artificial intelligence ; Machine learning ; Sensitivity (control systems) ; Root mean square ; Alloy ; Algorithm ; Computer science ; Metallurgy ; Statistics ; Mathematics ; Mathematical analysis ; Engineering Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: Haoxiang Li | Hao Tian | Yewei Chen | Sian Xiao | Xiuying Zhao | Yangyang Gao | Liqun Zhang
Source: The Journal of Physical Chemistry B (article, 2023-04-05)
Field: Materials Science — Polymers and Plastics

Analyzing and Predicting the Viscosity of Polymer Nanocomposites in the Conditions of Temperature, Shear Rate, and Nanoparticle Loading with Molecular Dynamics Simulations and Machine Learning Predicting the viscosity (η) of polymer nanocomposites (PNCs) is of critical importance as it governs a dominant role in PNCs' processing and application. Machine-learning (ML) algorithms, enabled by pre-existing experimental and computational data, have emerged as robust tools for the prediction of quantitative relationships between feature parameters and various physical properties of materials. In this work, we employed nonequilibrium molecular dynamics (NEMD) simulation with ML models to systematically investigate the η of PNCs over a wide range of nanoparticle (NP) loadings (φ), shear rates (γ̇), and temperatures (<i>T</i>). With the increase in γ̇, shear thinning takes place as the value of η decreases on the orders of magnitude. In addition, the φ dependence and <i>T</i> dependence reduce to the extent that it is not visible at high γ̇. The value of η for PNCs is proportional to φ and inversely proportional to <i>T</i> below the intermediate γ̇. Using the obtained NEMD results, four machine-learning models were trained to provide effective predictions for the η. The extreme gradient boosting (XGBoost) model yields the best accuracy in η prediction under complex conditions and is further used to evaluate feature importance. This quantitative structure-property relationship (QSPR) model used physical views to investigate the effect of process parameters, such as <i>T</i>, φ, and γ̇, on the η of PNCs and paves the path for theoretically proposing reasonable parameters for successful processing. Materials science ; Viscosity ; Shear thinning ; Nanoparticle ; Shear rate ; Molecular dynamics ; Rheology ; Polymer nanocomposite ; Machine learning ; Curse of dimensionality ; Work (physics) ; Polymer ; Shear viscosity ; Biological system ; Statistical physics ; Computer science ; Artificial intelligence ; Thermodynamics ; Nanotechnology ; Composite material ; Chemistry ; Physics ; Computational chemistry Polymer crystallization and properties ; Machine Learning in Materials Science ; Carbon Nanotubes in Composites
Authors: Wenzhao Li | Mingji Liu
Source: ACS Applied Electronic Materials (article, 2023-07-27)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Workflow for Evaluating and Analyzing the Performance of High-Entropy GeTe-Based Thermoelectric Materials To guide the development of high-performance thermoelectric materials, it is essential to design appropriate material compositions and temperature environments. This study focuses on analyzing the properties of high-entropy GeTe-based thermoelectric materials under different temperature environments and chemical compositions using an interpretable machine learning workflow. First, an experimental dataset from previous research on thermoelectric materials is constructed, and descriptors based on atomic features are established. Feature selection techniques, such as Pearson correlation, univariate feature selection, and exhaustive feature selection, are applied to select relevant features. The selected features are then used in conjunction with the target variable, the ZT value, for training and testing. The effectiveness of the training is demonstrated by comparing the performance on the testing set and using cross-validation to identify the best machine learning model. Furthermore, the SHAP (SHapley Additive exPlanations) method is employed to interpret the best model. Through global interpretability, analysis of interactive variables’ contributions to the target variable, and local interpretability methods, material selection, and performance optimization are carried out. The results reveal that temperature has the greatest impact on the ZT value of thermoelectric materials, while the effects of molar volume and electronegativity sequentially diminish. By utilizing interpretable machine learning methods, we are able to predict and optimize the performance of thermoelectric materials based on known samples. This not only facilitates the evaluation and prediction of the thermoelectric properties of materials but also provides a comprehensive research workflow design approach for guiding the selection and development of high-performance GeTe-based thermoelectric materials. Interpretability ; Workflow ; Feature selection ; Thermoelectric effect ; Machine learning ; Computer science ; Artificial intelligence ; Thermoelectric materials ; Entropy (arrow of time) ; Data mining ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films
Authors: Victor V. Pogorelko | Alexander E. Mayer | E. V. Fomin | E.V. Fedorov
Source: International Journal of Mechanical Sciences (article, 2023-12-09)
Field: Materials Science — Materials Chemistry

Examination of machine learning method for identification of material model parameters In this work, we compare two methods of using artificial neural network (ANN) to determine the optimal parameters of material model by results of high-speed plate impact experiments. The first ANN is trained to determine the free surface velocity history based on the impact parameters and material model parameters; this ANN is used as a fast emulator of model to speed-up the statistical Bayesian calibration of the model parameters. The second ANN directly determines the material model parameters based on the impact parameters and the free surface velocity history, because this ANN is trained by inverse data set; this approach for inverse problem is proposed for the first time and can be more preferable for fast-track expert systems in engineering applications. Both approaches are successfully used to determine the optimal parameters of material model in the case of impact of copper and aluminum plates at different temperatures and impact velocities with experimental data from the literature. A simple relaxation plasticity model with a constant relaxation time is used in the present research as an example; more accurate coincidence with the experimental data and wider applicability of the ANN-based approach can be expected for a more realistic plasticity model. Artificial neural network ; Calibration ; Experimental data ; Computer science ; Inverse problem ; Inverse ; Machine learning ; Algorithm ; Artificial intelligence ; Mathematics ; Statistics High-Velocity Impact and Material Behavior ; Fluid Dynamics Simulations and Interactions ; Metal Forming Simulation Techniques
Authors: Léo Thiercelin | Laurent Peltier | Fodil Meraghni
Source: Computational Materials Science (article, 2023-11-04)
Field: Materials Science — Materials Chemistry

Physics-informed machine learning prediction of the martensitic transformation temperature for the design of “NiTi-like” high entropy shape memory alloys Quinary ; Electronegativity ; Ternary operation ; Diffusionless transformation ; Atomic radius ; Nickel titanium ; Binary number ; Materials science ; Shape-memory alloy ; Thermodynamics ; Machine learning ; Algorithm ; Statistical physics ; Computer science ; Artificial intelligence ; Martensite ; Mathematics ; Metallurgy ; Physics Shape Memory Alloy Transformations ; High Entropy Alloys Studies
Authors: Vasiliki Skiada | Panagiotis Katsaris | Manousos E. Kambouris | Vasileios Gkisakis | Yiannis Manoussopoulos
Source: Food Chemistry (article, 2023-07-03)
Field: Chemistry — Analytical Chemistry

Classification of olive cultivars by machine learning based on olive oil chemical composition Cultivar ; Olive oil ; Crop ; Principal component analysis ; Traceability ; Olive trees ; Composition (language) ; Chemical composition ; Biology ; Biotechnology ; Horticulture ; Agronomy ; Mathematics ; Food science ; Artificial intelligence ; Chemistry ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Sumbel Ijaz | Sadia Noureen | Bacha Rehman | Muhammad Zubair | Yehia Massoud | Muhammad Qasim Mehmood
Source: Materials Today Communications (article, 2023-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multi-material described metasurface solar absorber design with absorption prediction using machine learning models Absorption (acoustics) ; Materials science ; Solar irradiance ; Solar energy ; Predictability ; Optics ; Computer science ; Algorithm ; Physics ; Meteorology ; Engineering Metamaterials and Metasurfaces Applications ; Plant Molecular Biology Research ; Thermal Radiation and Cooling Technologies
Authors: Haoyue Guo | Matthew R. Carbone | Chuntian Cao | Jianzhou Qu | Yonghua Du | Seong‐Min Bak | Conan Weiland | Feng Wang | Shinjae Yoo | Nongnuch Artrith | Alexander Urban | Deyu Lu
Source: Scientific Data (article, 2023-06-02)
Field: Materials Science — Ceramics and Composites

Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In this work, we develop a database of sulfur K-edge XAS spectra of crystalline and amorphous lithium thiophosphate materials based on the atomic structures reported in Chem. Mater., 34, 6702 (2022). The XAS database is based on simulations using the excited electron and core-hole pseudopotential approach implemented in the Vienna Ab initio Simulation Package. Our database contains 2681 S K-edge XAS spectra for 66 crystalline and glassy structure models, making it the largest collection of first-principles computational XAS spectra for glass/ceramic lithium thiophosphates to date. This database can be used to correlate S spectral features with distinct S species based on their local coordination and short-range ordering in sulfide-based solid electrolytes. The data is openly distributed via the Materials Cloud, allowing researchers to access it for free and use it for further analysis, such as spectral fingerprinting, matching with experiments, and developing machine learning models. Thiophosphate ; Lithium (medication) ; Sulfur ; Electrolyte ; Spectroscopy ; X-ray absorption spectroscopy ; Fast ion conductor ; Chemistry ; Absorption spectroscopy ; Database ; Materials science ; Analytical Chemistry (journal) ; Inorganic chemistry ; Physical chemistry ; Physics ; Optics ; Organic chemistry ; Computer science ; Biology Glass properties and applications ; Advanced Battery Materials and Technologies ; X-ray Diffraction in Crystallography
Authors: Anan Wu | Qing Ye | Xiaowei Zhuang | Qiwen Chen | Jinkun Zhang | Jianming Wu | Xin Xu
Source: Precision Chemistry (article, 2023-02-15)
Field: Chemistry — Spectroscopy

Elucidating Structures of Complex Organic Compounds Using a Machine Learning Model Based on the <sup>13</sup>C NMR Chemical Shifts We present a protocol that combines the support vector machine (SVM) model with accurate <sup>13</sup>C chemical shift calculations at the xOPBE/6-311+G-(2d,p) level of theory, denoted as SVM-M (i.e., SVM for magnetic property). We show here that this SVM-M protocol is a versatile tool for identifying the structural and stereochemical assignment of complex organic compounds with high confidence. Of particular significance is that, by utilizing the dual role of the decision values in SVM, the present SVM-M protocol provides an accurate yet efficient solution to simultaneously handle the classification issue (i.e., "is a given structure correct or incorrect?") and the comparison-based problem (i.e., "which structure is more likely to be correct or wrong among several candidate structures?"). A significantly high success rate has been reached (i.e., ∼100% on a set of 760 sample molecules with 15928 <sup>13</sup>C chemical shifts), which makes the SVM-M protocol a powerful tool for routine applications in structural and stereochemical assignments, as well as in detecting mis-assignments, for complex organic compounds, including natural products. Support vector machine ; Protocol (science) ; Artificial intelligence ; Computer science ; Chemical shift ; Dual (grammatical number) ; Set (abstract data type) ; Data set ; Machine learning ; Quantitative structure–activity relationship ; Chemistry ; Data mining ; Pattern recognition (psychology) Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods