jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vyshnavi Vennelakanti | Irem B. Kilic | Gianmarco Terrones | Chenru Duan | Heather J. Kulik
Source: The Journal of Physical Chemistry A (article, 2023-12-27)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of the Experimental Transition Temperature of Fe(II) Spin-Crossover Complexes Spin-crossover (SCO) complexes are materials that exhibit changes in the spin state in response to external stimuli, with potential applications in molecular electronics. It is challenging to know a priori how to design ligands to achieve the delicate balance of entropic and enthalpic contributions needed to tailor a transition temperature close to room temperature. We leverage the SCO complexes from the previously curated SCO-95 data set [Vennelakanti et al. <i>J. Chem. Phys</i>. <i>159,</i> 024120 (<b>2023</b>)] to train three machine learning (ML) models for transition temperature (<i>T</i><sub>1/2</sub>) prediction using graph-based revised autocorrelations as features. We perform feature selection using random forest-ranked recursive feature addition (RF-RFA) to identify the features essential to model transferability. Of the ML models considered, the full feature set RF and recursive feature addition RF models perform best, achieving moderate correlation to experimental <i>T</i><sub>1/2</sub> values. We then compare ML <i>T</i><sub>1/2</sub> predictions to those from three previously identified best-performing density functional approximations (DFAs) which accurately predict SCO behavior across SCO-95, finding that the ML models predict <i>T</i><sub>1/2</sub> more accurately than the best-performing DFAs. In addition, we study ML model predictions for a set of 18 SCO complexes for which only estimated <i>T</i><sub>1/2</sub> values are available. Upon excluding outliers from this set, the RF-RFA RF model shows a strong correlation to estimated <i>T</i><sub>1/2</sub> values with a Pearson's <i>r</i> of 0.82. In contrast, DFA-predicted <i>T</i><sub>1/2</sub> values have large errors and show no correlation to estimated <i>T</i><sub>1/2</sub> values over the same set of complexes. Overall, our study demonstrates slightly superior performance of ML models in comparison with some of the best-performing DFAs, and we expect ML models to improve further as larger data sets of SCO complexes are curated and become available for model training. Spin crossover ; Crossover ; Condensed matter physics ; Spin (aerodynamics) ; Materials science ; Chemical physics ; Physics ; Statistical physics ; Thermodynamics ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Xin Wang | Shu Li | Feng Liu
Source: Scripta Materialia (article, 2023-12-03)
Field: Materials Science — Materials Chemistry

Modeling for free dendrite growth based on physically-informed machine learning method Extrapolation ; Interpolation (computer graphics) ; Dendrite (mathematics) ; Materials science ; Kinetic energy ; Experimental data ; Supercooling ; Thermodynamics ; Statistical physics ; Biological system ; Computer science ; Mathematics ; Mathematical analysis ; Physics ; Artificial intelligence ; Geometry ; Statistics ; Classical mechanics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; nanoparticles nucleation surface interactions
Authors: Bulat Yakupov | Ivan Smirnov
Source: Fluids (article, 2023-05-26)
Field: Materials Science — Materials Chemistry

Application of Machine Learning to Predict the Acoustic Cavitation Threshold of Fluids The acoustic cavitation of fluids, as well as related physical and chemical phenomena, causes a variety of effects that are highly important in technological processes and medicine. Therefore, it is important to be able to control the conditions that allow cavitation to begin and progress. However, the accurate prediction of acoustic cavitation is dependent on a complex relationship between external influence parameters and fluid characteristics. The multiparameter problem restricts the development of successful theoretical models. As a result, it is critical to identify the most important parameters influencing the onset of the cavitation process. In this paper, the ultrasonic frequency, hydrostatic pressure, temperature, degassing, density, viscosity, volume, and surface tension of a fluid were investigated using machine learning to determine their significance in predicting acoustic cavitation strength. Three machine learning models based on support vector regression (SVR), ridge regression (RR), and random forest (RF) algorithms with different input parameters were trained. The results showed that the SVM algorithm performed better than the other two algorithms. The parameters affecting the active cavitation nuclei, namely hydrostatic pressure, ultrasound frequency, and outgassing degree, were found to be the most important input parameters influencing the prediction of the cavitation threshold. Other parameters have a minor impact when compared to the first three, and their role can be compensated for by alternative variables. The further development of the obtained results provides a new way to optimize and improve existing theoretical models. Cavitation ; Support vector machine ; Hydrostatic pressure ; Computer science ; Viscosity ; Ultrasound ; Surface tension ; Process (computing) ; Hydrostatic equilibrium ; Acoustics ; Materials science ; Artificial intelligence ; Machine learning ; Mechanics ; Physics ; Thermodynamics Ultrasound and Cavitation Phenomena ; Cavitation Phenomena in Pumps ; Fluid Dynamics and Mixing
Authors: Ehsan Shahini | Narendra Chaulagain | Karthik Shankar | Tian Tang
Source: ACS Applied Materials & Interfaces (article, 2023-11-08)
Field: Materials Science — Materials Chemistry

Predicting Free Energies of Exfoliation and Solvation for Graphitic Carbon Nitrides Using Machine Learning As a metal-free and visible-light-responsive photocatalyst, graphitic carbon nitride (g-C<sub>3</sub>N<sub>4</sub>) has emerged as a new research hotspot and has attracted broad attention in the field of solar energy conversion and thin-film transistors. Liquid-phase exfoliation (LPE) is the best-known method for the synthesis of 2D g-C<sub>3</sub>N<sub>4</sub> nanosheets. In LPE, bulk g-C<sub>3</sub>N<sub>4</sub> is exfoliated in a solvent via high-shear mixing or sonication in order to produce a stable suspension of individual nanosheets. Two parameters of importance in gauging the performance of a solvent in LPE are the free energy required to exfoliate a unit area of layered materials into individual sheets in the solvent (Δ<i>G</i><sub>exf</sub>) and the solvation free energy per unit area of a nanosheet (Δ<i>G</i><sub>sol</sub>). While approximations for the free energies exist, they are shown in our previous work to be inaccurate and incapable of capturing the experimentally observed efficacy of LPE. Molecular dynamics (MD) simulations can provide accurate free-energy calculations, but doing so for every single solvent is time- and resource-consuming. Herein, machine learning (ML) algorithms are used to predict Δ<i>G</i><sub>exf</sub> and Δ<i>G</i><sub>sol</sub> for g-C<sub>3</sub>N<sub>4</sub>. First, a database for Δ<i>G</i><sub>exf</sub> and Δ<i>G</i><sub>sol</sub> is created based on a series of MD simulations involving 49 different solvents with distinct chemical structures and properties. The data set also includes values of critical descriptors for the solvents, including density, surface tension, dielectric constant, etc. Different ML methods are compared, accompanied by descriptor selection, to develop the most accurate model for predicting Δ<i>G</i><sub>exf</sub> and Δ<i>G</i><sub>sol</sub>. The extra tree regressor is shown to be the best performer among the six ML methods studied. Experimental validation of the model is conducted by performing dispersibility tests in several solvents for which the free energies are predicted. Finally, the influence of the selected descriptors on the free energies is analyzed, and strategies for solvent selection in LPE are proposed. Materials science ; Graphitic carbon nitride ; Solvation ; Dielectric ; Exfoliation joint ; Nanosheet ; Solvent ; Nanotechnology ; Graphene ; Optoelectronics ; Organic chemistry ; Photocatalysis ; Catalysis Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Jili Li | Yefei Li
Source: Journal of Materials Informatics (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

Recent advances in the interface structure prediction for heteromaterial systems The atomic structures of solid-solid interfaces in materials are of fundamental importance for understanding the physical properties of interfacial materials, which is, however, difficult to determine both in experimental and theoretical approaches. New theoretical methodologies utilizing various global optimization algorithms and machine learning (ML) potentials have emerged in recent years, offering a promising approach to unraveling interfacial structures. In this review, we give a concise overview of state-of-the-art techniques employed in the studies of interfacial structures, e.g., ML-assisted phenomenological theory for the global search of interface structure (ML-interface). We also present a few applications of these methodologies. Interface (matter) ; Computer science ; Nanotechnology ; Materials science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Seonho Lee | Jiwon Kim | Jaewon Byun | Junghee Joo | Yoon Jae Lee | Tae‐Hyun Kim | Soonho Hwangbo | Jeehoon Han | Sung‐Kon Kim | Jechan Lee
Source: Applied Energy (article, 2023-08-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Environmentally-viable utilization of chicken litter as energy recovery and electrode production: A machine learning approach Environmental science ; Fossil fuel ; Litter ; Waste management ; Renewable energy ; Energy storage ; Process engineering ; Pulp and paper industry ; Materials science ; Environmental engineering ; Chemistry ; Ecology ; Engineering ; Biology Supercapacitor Materials and Fabrication ; Environmental Impact and Sustainability ; Microbial Fuel Cells and Bioremediation
Authors: Franco Aquistapace | Daniel Castillo-Castro | Rafael I. González | Nicolás Amigó | Gonzalo García Vidable | Diego Tramontina | Felipe J. Valencia | Eduardo M. Bringa
Source: Journal of Materials Science (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Plasticity in diamond nanoparticles: dislocations and amorphization during loading and dislocation multiplication during unloading Materials science ; Dislocation ; Diamond ; Peierls stress ; Condensed matter physics ; Dislocation creep ; Plasticity ; Anisotropy ; Annealing (glass) ; Phase (matter) ; Crystallography ; Composite material ; Optics ; Chemistry Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Advanced materials and composites
Authors: Marc Lehner | Paul Katzberger | Niels Maeder | Carl C. G. Schiebroek | Jakob Teetz | Gregory A. Landrum | Sereina Riniker
Source: Journal of Chemical Information and Modeling (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment We present a robust and computationally efficient approach for assigning partial charges of atoms in molecules. The method is based on a hierarchical tree constructed from attention values extracted from a graph neural network (GNN), which was trained to predict atomic partial charges from accurate quantum-mechanical (QM) calculations. The resulting dynamic attention-based substructure hierarchy (DASH) approach provides fast assignment of partial charges with the same accuracy as the GNN itself, is software-independent, and can easily be integrated in existing parametrization pipelines, as shown for the Open force field (OpenFF). The implementation of the DASH workflow, the final DASH tree, and the training set are available as open source/open data from public repositories. Partial charge ; Dash ; Computer science ; Workflow ; Substructure ; Hierarchy ; Set (abstract data type) ; Data mining ; Theoretical computer science ; Charge (physics) ; Physics ; Engineering ; Quantum mechanics ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Peng Guo | Yongjian Li | Zhiwei Lin | Yating Li | Peng Su
Source: Applied Energy (article, 2023-07-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Characterization and calculation of losses in soft magnetic composites for motors with SVPWM excitation Excitation ; Harmonics ; Voltage ; Waveform ; Pulse-width modulation ; Materials science ; Control theory (sociology) ; Acoustics ; Physics ; Engineering ; Electrical engineering ; Computer science Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic Properties of Alloys
Authors: Yuxuan Fei | Ang Li | Chen Zhang | Hengyong Tu | Lei Zhu | Zhen Huang
Source: Energy Conversion and Management (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Performance optimization of solid oxide electrolysis cell for syngas production by high temperature co-electrolysis via differential evolution algorithm with practical constraints Syngas ; Electrolysis ; High-temperature electrolysis ; Process engineering ; Methane ; Carbon dioxide ; Chemical engineering ; Polymer electrolyte membrane electrolysis ; Power to gas ; Hydrogen production ; Materials science ; Oxide ; Chemistry ; Catalysis ; Electrode ; Organic chemistry ; Engineering Advancements in Solid Oxide Fuel Cells ; Catalytic Processes in Materials Science ; Chemical Looping and Thermochemical Processes
Authors: Gabriella Bruno | Matthew Lynch | Ryan Jacobs | Dane Morgan | Kevin G. Field
Source: Microscopy and Microanalysis (article, 2023-07-22)
Field: Materials Science — Surfaces, Coatings and Films

Evaluation of Human-Bias in Labeling of Ambiguous Features in Electron Microscopy Machine Learning Models Journal Article Evaluation of Human-Bias in Labeling of Ambiguous Features in Electron Microscopy Machine Learning Models Get access Gabriella Bruno, Gabriella Bruno Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, United States Corresponding author: gbruno@umich.edu Search for other works by this author on: Oxford Academic Google Scholar Matthew J Lynch, Matthew J Lynch Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, United States Search for other works by this author on: Oxford Academic Google Scholar Ryan Jacobs, Ryan Jacobs Department of Materials Science and Engineering, University of Wisconsin – Madison, Madison, WI, United States Search for other works by this author on: Oxford Academic Google Scholar Dane D Morgan, Dane D Morgan Department of Materials Science and Engineering, University of Wisconsin – Madison, Madison, WI, United States Search for other works by this author on: Oxford Academic Google Scholar Kevin G Field Kevin G Field Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, United States Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 29, Issue Supplement_1, 1 August 2023, Pages 1493–1494, https://doi.org/10.1093/micmic/ozad067.767 Published: 22 July 2023 Library science ; Artificial intelligence ; Art history ; History ; Computer science Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Deepak Balram | Kuang‐Yow Lian | Neethu Sebastian | Salman S. Alharthi | Hamed M. Al‐Saidi | Virendra Kumar Yadav | Deepak Kumar | Vineet Kumar
Source: Journal of Alloys and Compounds (article, 2023-08-29)
Field: Chemistry — Analytical Chemistry

A novel ternary nanocomposite based electrochemical sensor coupled with regularized neural network for nanomolar detection of sunset yellow FCF Ternary operation ; Materials science ; Nanorod ; Electrochemical gas sensor ; Detection limit ; Differential pulse voltammetry ; Nanocomposite ; Cyclic voltammetry ; Electrochemistry ; Chemical engineering ; Nanotechnology ; Nuclear chemistry ; Chemistry ; Electrode ; Chromatography ; Computer science ; Physical chemistry Dye analysis and toxicity ; Electrochemical sensors and biosensors ; Electrochemical Analysis and Applications
Authors: Guobin Zhao | Yu Chen | Yongchul G. Chung
Source: Industrial & Engineering Chemistry Research (article, 2023-09-06)
Field: Chemistry — Inorganic Chemistry

High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters Accurate evaluation of adsorbent materials’ performance requires carrying out process simulations that take an analytical isotherm model as an input. In this work, we report a machine learning (ML) approach to approximate the saturation loading of nanoporous materials, an essential parameter for modeling the adsorption-based process simulation. Large-scale grand canonical Monte Carlo (GCMC) simulations were carried out to compute the single-component isotherms for Xe and Kr from the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF) Database 2019. The generated data were used to fit the Langmuir model equation to obtain the saturation loading parameters, which were used as a basis to train several ML models. The performance of trained ML models was then compared with the pore volume-based approach, typically used in the literature, to approximate the saturation loading of the adsorbent material. Ideal vacuum swing adsorption (IVSA) simulations were carried out to screen a large number of MOFs. We found that the ML model better estimates the saturation loading from the curve fitting compared to the pore volume approach. Finally, we carried out high-fidelity vacuum swing adsorption simulations on 15 Xe-selective MOFs. While the IVSA approach provides quantitative information about the process performance metrics, we found that the commonly used performance metrics, such as Xe/Kr IAST selectivity, work as well as the shortcut methods (IVSA simulation) in ranking the adsorbent materials for Xe/Kr separation. Nanoporous ; Saturation (graph theory) ; Adsorption ; Computation ; Monte Carlo method ; Materials science ; Work (physics) ; Langmuir adsorption model ; Computer science ; Biological system ; Thermodynamics ; Chemistry ; Physics ; Algorithm ; Mathematics ; Nanotechnology ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science
Authors: Khadijeh Jafari | Mohsen Heidari | Ali Fatehizadeh | Kavoos Dindarloo | Vali Alipour | Omid Rahmanian
Source: Heliyon (article, 2023-07-24)
Field: Chemistry — Analytical Chemistry

Extensive sorption of Amoxicillin by highly efficient carbon-based adsorbent from palm kernel: Artificial neural network modeling In the present study, a new sorbent was fabricated from Palm kernel (PK) by dry thermochemical activation with NaOH and characterized by FTIR, X-ray diffraction, FE-SEM and BET, which was used for the Amoxicillin (AMX) sorption from aqueous solution. The influence of effective parameters such as pH, reaction time, adsorbent dosage, AMX concentration and ionic strength on the sorption efficacy of AMX removal were evaluated. The main functional groups on the surface of the magnetic activated carbon of Palm Kernel (MA-PK) were C-C, C-O, C[bond, double bond]O and hydroxyl groups. The specific surface of char, activated carbon Palm Kernel (AC-PK) and MA-PK were 4.3, 1648.8 and 1852.4 m<sup>2</sup>/g, respectively. The highest sorption of AMX (400 mg/L) was obtained by using 1 g/L of sorbent at solution pH of 5 after 60 min contact time, which corresponding to 98.77%. Non-linear and linear models of isotherms and kinetics models were studied. The data fitted well with Hill isotherm (R<sup>2</sup> = 0.987) and calculated maximum sorption capacity were 719.07 and 512.27 mg/g from Hill and Langmuir, respectively. A study of kinetics shows that the adsorption of AMX follows the Elovich model with R<sup>2</sup> = 0.9998. Based on the artificial neural network (ANN) modeling, the MA-PK dosage and contact time showed the most important parameters in the removal of AMX with relative importance of 36.5 and 25.7%, respectively. Lastly, the fabricated MA-PK was successfully used to remove the AMX from hospital wastewater. Palm kernel ; Sorption ; Artificial neural network ; Adsorption ; Activated carbon ; Kernel (algebra) ; Palm ; Mathematics ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry ; Palm oil ; Food science ; Physics Analytical Methods in Pharmaceuticals ; Dye analysis and toxicity ; Spectroscopy and Chemometric Analyses
Authors: Dhritiman Saha | T. Senthilkumar | C. B. Singh | Peter Pauls | Annamalai Manickavasagan
Source: Food and Bioproducts Processing (article, 2023-07-25)
Field: Chemistry — Analytical Chemistry

Rapid and non-destructive detection of hard to cook chickpeas using NIR hyperspectral imaging and machine learning Hyperspectral imaging ; Convolutional neural network ; Pattern recognition (psychology) ; Artificial intelligence ; Partial least squares regression ; Mean squared error ; Mathematics ; Artificial neural network ; Root mean square ; Biological system ; Computer science ; Biology ; Engineering ; Statistics Spectroscopy and Chemometric Analyses ; GABA and Rice Research ; Advanced Chemical Sensor Technologies
Authors: Alexandre Luiz Korte de Azevedo | Talita Helen Bombardelli Gomig | Michel Batista | Fabrício Klerynton Marchini | Cléverton Spautz | Íris Rabinovich | Ana Paula Martins Sebastião | Jaqueline Carvalho de Oliveira | Daniela Fiori Gradia | Iglenir João Cavalli | Enilze Maria de Souza Fonseca Ribeiro
Source: Journal of Proteomics (article, 2023-06-28)
Field: Chemistry — Spectroscopy

High-throughput proteomics of breast cancer subtypes: Biological characterization and multiple candidate biomarker panels to patients' stratification Breast cancer ; Biomarker discovery ; Biomarker ; Proteomics ; Cancer ; Malignancy ; Oncology ; Proteome ; Medicine ; Disease ; Bioinformatics ; Internal medicine ; Computational biology ; Biology ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; Cancer Genomics and Diagnostics ; Ferroptosis and cancer prognosis