jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lulu Zhang | Qiuhong Huang | Lifeng Li | Yaling Yan | Xueying Yuan | Hong Liang | Shuhua Li | Bangfen Wang | Zhiwei Qiao
Source: ACS ES&T Engineering (article, 2023-07-03)
Field: Chemistry — Inorganic Chemistry

Automatic Machine Learning Combined with High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks for Capture of Methanol and Ethanol from the Air The capture of low concentration alcohol VOCs (methanol and ethanol) from the air has also attracted more and more attention. In this work, high-throughput computational screening (HTCS) and machine learning (ML) methods based on molecular simulations were used to investigate the adsorption properties of methanol and ethanol in 31 399 hydrophobic metal–organic frameworks (MOFs). First, the structure–performance relationship of MOFs was successfully established through univariate analysis, and the key descriptors identified were LCD and Q0st. Five ML methods, Decision Tree (DT), Random Forest (RF), Back Propagation Neural Network (BPNN), Support Vector Machines (SVM), and Tree-based Pipeline Optimization Tool (TPOT), were used to predict the adsorption performance of MOFs. The automatic machine learning (Auto-ML) algorithm TPOT has the best prediction effect on the TSN of methanol and ethanol, with R2 values of 0.852 and 0.945, respectively. The accuracy of the ML model was further improved using the random search method. Analysis of the algorithms has revealed that GBR and RFR have the highest prediction accuracy and frequency, respectively, for the MOF–methanol and MOF–ethanol systems. Ten MOF materials with excellent adsorption properties (0.002 mol/kg ≥ NCH3OH ≥ 0.001 mol/kg, 0.068 mol/kg ≥ NC2H5OH ≥ 0.016 mol/kg; 420.67 ≥ SCH3OH ≥ 214.29, 3.2 × 106 ≥ SC2H5OH ≥ 8.5 × 103) were selected successfully. After analysis of their adsorption sites, it was found that the primary adsorption sites for methanol and ethanol are located near the amino and halogen groups, and the different metal centers showed great influence on the adsorption capacity of MOFs for two kinds of alcohol molecules through the analysis of their structural commonness. This work can serve as a roadmap for experimental synthesis, innovative design of MOFs, and the development of new ML algorithms. Methanol ; Adsorption ; Metal-organic framework ; Support vector machine ; Ethanol ; Chemistry ; Random forest ; Materials science ; Machine learning ; Computer science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Alex Vallone | Nooshin M. Estakhri | Nasim Mohammadi Estakhri
Source: Journal of Physics Photonics (article, 2023-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Region-specified inverse design of absorption and scattering in nanoparticles by using machine learning Abstract Machine learning provides a promising platform for both forward modeling and the inverse design of photonic structures. Relying on a data-driven approach, machine learning is especially appealing for situations when it is not feasible to derive an analytical solution for a complex problem. There has been a great amount of recent interest in constructing machine learning models suitable for different electromagnetic problems. In this work, we adapt a region-specified design approach for the inverse design of multilayered nanoparticles. Given the high computational cost of dataset generation for electromagnetic problems, we specifically investigate the case of a small training dataset, enhanced via random region specification in an inverse convolutional neural network. The trained model is used to design nanoparticles with high absorption levels and different ratios of absorption over scattering. The central design wavelength is shifted across 350–700 nm without re-training. We discuss the implications of wavelength, particle size, and the training dataset size on the performance of the model. Our approach may find interesting applications in the design of multilayer nanoparticles for biological, chemical, and optical applications as well as the design of low-scattering absorbers and antennas. Computer science ; Absorption (acoustics) ; Inverse ; Scattering ; Convolutional neural network ; Inverse problem ; Artificial neural network ; Artificial intelligence ; Machine learning ; Materials science ; Optics ; Mathematics ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Li-Kai Wan | Yixuan Xue | Jin-Wu Jiang | Harold S. Park
Source: Journal of Applied Physics (article, 2023-01-10)
Field: Materials Science — Materials Chemistry

Machine learning accelerated search of the strongest graphene/h-BN interface with designed fracture properties Two-dimensional lateral heterostructures exhibit novel electronic and optical properties that are induced by their in-plane interface for which the mechanical properties of the interface are important for the stability of the lateral heterostructure. Therefore, we performed molecular dynamics simulations and developed a convolutional neural network-based machine learning model to study the fracture properties of the interface in a graphene/hexagonal boron nitride lateral heterostructure. The molecular dynamics (MD) simulations show that the shape of the interface can cause an 80% difference in the fracture stress and the fracture strain for the interface. By using 11 500 training samples obtained with help of high-cost MD simulation, the machine learning model is able to search out the strongest interfaces with the largest fracture strain and fracture stress in a large sample space with over 150 000 structures. By analyzing the atomic configuration of these strongest interfaces, we disclose two major factors dominating the interface strength, including the interface roughness and the strength of the chemical bond across the interface. We also explore the correlation between the fracture properties and the thermal conductivity for these lateral heterostructures by examining the bond type and the shape of the graphene/hexagonal boron nitride interface. We find that interfaces comprised of stronger bonds and smoother zigzag interfaces can relieve the abrupt change of the acoustic velocity, leading to the enhancement of the interface thermal conductivity. These findings will be valuable for the application of the two-dimensional lateral heterostructure in electronic devices. Materials science ; Heterojunction ; Graphene ; Zigzag ; Fracture (geology) ; Interface (matter) ; Thermal conductivity ; Stress (linguistics) ; Boron nitride ; Molecular dynamics ; Condensed matter physics ; Nanotechnology ; Composite material ; Optoelectronics ; Computational chemistry ; Chemistry ; Geometry ; Physics Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Alfredo Pereira | Oleksandra S. Trofymchuk
Source: The Journal of Physical Chemistry C (article, 2023-07-03)
Field: Chemistry — Inorganic Chemistry

Machine Learning Prediction of High-Yield Cobalt- and Nickel-Catalyzed Borylations Borylation reactions catalyzed by cobalt and nickel compounds occupy their important niche in synthetic organic chemistry; however, the search of parameters for high-yield reactions can be time-consuming and expensive. Recently, machine learning-based regression models were able to accurately predict reactivity yields, still when data from the literature are used, less accurate models are obtained. In this work, transforming the regression problem into a classification problem, we managed to predict high-yield cobalt- and nickel-catalyzed borylations using reaction data taken from the literature. With the Random Forest algorithm, we achieve to get the area under the receiver operating characteristics (ROC) curve mean values of 0.93 for cobalt-catalyzed reaction models and 0.86 for nickel-catalyzed reaction models. In addition, the feature importance indicates that for Co-catalyzed reactions, the characteristics of the catalyst are the most important, while in Ni-catalyzed borylations, there is a greater influence of the characteristics of the reactants and products. We think that this study may be a viable alternative to take advantage of reported reactions and could be especially useful for those laboratories that do not have the possibility to perform high-throughput experimentation to optimize their catalytic reactions. Cobalt ; Catalysis ; Nickel ; Yield (engineering) ; Throughput ; Random forest ; Chemistry ; Computer science ; Biological system ; Feature selection ; Process engineering ; Machine learning ; Materials science ; Organic chemistry ; Engineering ; Metallurgy Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Chemical Synthesis and Analysis
Authors: Shuang Lyu | Wei Li | Yuanhang Xia | Yue Chen | A.H.W. Ngan
Source: Physical Review Materials (article, 2023-07-06)
Field: Materials Science — Materials Chemistry

Effects of chemical randomness on strength contributors and dislocation behaviors in a bcc multiprincipal element alloy Molecular dynamics simulations for dislocation motion are conducted in a body centered cubic TaCrVW multiprincipal element alloy with different chemical randomness based on a machine learning potential. The simulated critical stress for dislocation motion in samples with an increasing level of randomness is analyzed in terms of three contributions: (1) Peierls stress (from 438 to 431 MPa), (2) strengthening from the average fault energy (from 261 MPa to near zero), and (3) strengthening due to local fluctuations of fault energy from the average value (from 394 to 566 MPa). As the alloy randomness increases, the Peierls stress as predicted from lattice distortion is rather invariant within the range studied, while there is a clear decreasing trend of the average fault energy and hence the associated strengthening. There is an increasing trend of strength contribution (3) as the alloy randomness increases, which offsets the decreasing trend of strength contribution (2). The overall strength of the TaCrVW alloy is therefore rather invariant within the range 964--1259 MPa with different randomness. The present work elucidates the physical basis of strength in the studied TaCrVW alloy. Randomness ; Materials science ; Alloy ; Dislocation ; Lattice (music) ; Condensed matter physics ; Metallurgy ; Composite material ; Physics ; Statistics ; Mathematics Nuclear Materials and Properties ; High Entropy Alloys Studies ; High Temperature Alloys and Creep
Authors: Wei Wu | Congjian Wang | Wenjuan Bian | Bin Hua | Joshua Gomez | Christopher J. Orme | Wei Tang | Frederick F. Stewart | Dong Ding
Source: Advanced Science (article, 2023-08-26)
Field: Materials Science — Materials Chemistry

Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cell with Interfacial Electrical Sensors Using Data‐Driven Machine Learning Protonic ceramic electrochemical cells (PCECs) offer promising paths for energy storage and conversion. Despite considerable achievements made, PCECs still face challenges such as physiochemical compatibility between componenets and suboptimal solid-solid contact at the interfaces between the electrolytes and electrodes. In this study, a novel approach is proposed that combines in situ electrochemical characterization of interfacial electrical sensor embedded PCECs and machine learning to quantify the contributions of different cell components to total degradation, as well as to predict the remaining useful life. The experimental results suggest that the overpotential induced by the oxygen electrode is 48% less than that of oxygen electrode/electrolyte interfacial contact for up to 1171 h. The data-driven machine learning simulation predicts the RUL of up to 2132 h. The root cause of degradation is overpotential increase induced by oxygen electrode, which accounts for 82.9% of total cell degradation. The success of the failure diagnostic model is demonstrated by its consistency with degradation modes that do not manifest in electrolysis fade during early real operations. This synergistic approach provides valuable insights into practical failure diagnosis of PCECs and has the potential to revolutionize their development by enabling improved performance prediction and material selection for enhanced durability and efficiency. Overpotential ; Materials science ; Electrolyte ; Electrolysis ; Electrode ; Degradation (telecommunications) ; Ceramic ; Durability ; Electrochemistry ; Electrolysis of water ; Electrochemical cell ; Computer science ; Chemical engineering ; Process engineering ; Composite material ; Chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electronic and Structural Properties of Oxides
Authors: Martin Haničinec | Sebastian Mohr | Jonathan Tennyson
Source: Journal of Physics D Applied Physics (article, 2023-05-10)
Field: Materials Science — Materials Chemistry

A regression model for plasma reaction kinetics Abstract Machine learning (ML) is used to provide reactions rates appropriate for models of low temperature plasmas with a focus on A + B <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mo stretchy="false">→</mml:mo> </mml:math> C + D binary chemical reactions. The regression model is trained on data extracted from the QBD, KIDA, NFRI and UfDA databases. The regression model used a variety of data on the reactant and product species, some of which also had to be estimated using ML. The final model is a voting regressor comprising three distinct optimized regression models: a support vector regressor, random forest regressor and a gradient-boosted trees regressor model; this model is made freely available via a GitHub repository. As a sample use case, the ML results are used to augment the chemistry of a BCl 3 /H 2 gas mixture. Random forest ; Regression ; Support vector machine ; Regression analysis ; Voting ; Linear regression ; Artificial intelligence ; Machine learning ; Computer science ; Statistics ; Mathematics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies
Authors: Arnaud Allera | Alexandra M. Goryaeva | Paul Lafourcade | Jean‐Bernard Maillet | Mihai‐Cosmin Marinica
Source: Computational Materials Science (article, 2023-10-07)
Field: Materials Science — Materials Chemistry

Neighbors Map: An efficient atomic descriptor for structural analysis Computer science ; Representation (politics) ; Enhanced Data Rates for GSM Evolution ; Encoding (memory) ; Convolutional neural network ; Graph ; Molecular dynamics ; Materials science ; Statistical physics ; Computational science ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Ion-surface interactions and analysis
Authors: Ronald Machaka | Precious M. Radingoana
Source: Materials Today Communications (article, 2023-03-15)
Field: Materials Science — Materials Chemistry

Prediction of narrow HT-SMA thermal hysteresis behaviour using explainable machine learning Materials science ; SMA* ; Hysteresis ; Thermal hysteresis ; Thermal ; Thermodynamics ; Condensed matter physics ; Computer science ; Algorithm Machine Learning in Materials Science ; Shape Memory Alloy Transformations ; Ferroelectric and Negative Capacitance Devices
Authors: A. Ghaderi | Georges Ayoub | Roozbeh Dargazany
Source: Journal of Materials Science (article, 2023-12-22)
Field: Materials Science — Polymers and Plastics

Constitutive behavior and failure prediction of crosslinked polymers exposed to concurrent fatigue and thermal aging: a reduced-order knowledge-driven machine-learned model Materials science ; Constitutive equation ; Accelerated aging ; Reliability (semiconductor) ; Thermal ; Artificial neural network ; Biological system ; Durability ; Polymer ; Solid mechanics ; Computer science ; Structural engineering ; Composite material ; Finite element method ; Artificial intelligence ; Thermodynamics ; Engineering ; Physics Polymer crystallization and properties ; Mechanical Behavior of Composites ; Fuel Cells and Related Materials
Authors: Ví­ctor M. Zavala
Source: Industrial & Engineering Chemistry Research (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

Outlook: How I Learned to Love Machine Learning (A Personal Perspective on Machine Learning in Process Systems Engineering) ADVERTISEMENT RETURN TO ISSUEPREVCommentaryNEXTOutlook: How I Learned to Love Machine Learning (A Personal Perspective on Machine Learning in Process Systems Engineering)Victor M. Zavala*Victor M. ZavalaDepartment of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United StatesMathematics and Computer Science Division, 9700 South Cass Ave., Lemont, Illinois 60439, United States*E-mail: [email protected]More by Victor M. Zavalahttps://orcid.org/0000-0002-5744-7378Cite this: Ind. Eng. Chem. Res. 2023, 62, 23, 8995–9005Publication Date (Web):June 5, 2023Publication History Received9 May 2023Published online5 June 2023Published inissue 14 June 2023https://pubs.acs.org/doi/10.1021/acs.iecr.3c01565https://doi.org/10.1021/acs.iecr.3c01565article-commentaryACS PublicationsCopyright © 2023 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views3547Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (2 MB) Get e-AlertscloseSUBJECTS:Algorithms,Chemical engineering and industrial chemistry,Mathematical methods,Neural networks,Optimization Get e-Alerts Citation ; Computer science ; Perspective (graphical) ; World Wide Web ; Social media ; Artificial intelligence ; Library science Machine Learning in Materials Science ; Fault Detection and Control Systems ; Machine Learning and Algorithms
Authors: Zhenye Li | Dongyi Wang | Tingting Zhu | Chao Ni | Chao Zhou
Source: Infrared Physics & Technology (article, 2023-05-17)
Field: Chemistry — Analytical Chemistry

SCNet: A deep learning network framework for analyzing near-infrared spectroscopy using short-cut Residual ; Computer science ; Backpropagation ; Gradient descent ; Convolutional neural network ; Deep learning ; Artificial intelligence ; Feature (linguistics) ; Pattern recognition (psychology) ; Extractor ; Stochastic gradient descent ; Algorithm ; Artificial neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Janghoon Ock | Tian Tian | John R. Kitchin | Zachary W. Ulissi
Source: The Journal of Chemical Physics (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Beyond independent error assumptions in large GNN atomistic models The calculation of relative energy difference has significant practical applications, such as determining adsorption energy, screening for optimal catalysts with volcano plots, and calculating reaction energies. Although Density Functional Theory (DFT) is effective in calculating relative energies through systematic error cancellation, the accuracy of Graph Neural Networks (GNNs) in this regard remains uncertain. To address this, we analyzed ∼483 × 106 pairs of energy differences predicted by DFT and GNNs using the Open Catalyst 2020-Dense dataset. Our analysis revealed that GNNs exhibit a correlated error that can be reduced through subtraction, challenging the assumption of independent errors in GNN predictions and leading to more precise energy difference predictions. To assess the magnitude of error cancellation in chemically similar pairs, we introduced a new metric, the subgroup error cancellation ratio. Our findings suggest that state-of-the-art GNN models can achieve error reduction of up to 77% in these subgroups, which is comparable to the error cancellation observed with DFT. This significant error cancellation allows GNNs to achieve higher accuracy than individual energy predictions and distinguish subtle energy differences. We propose the marginal correct sign ratio as a metric to evaluate this performance. Additionally, our results show that the similarity in local embeddings is related to the magnitude of error cancellation, indicating the need for a proper training method that can augment the embedding similarity for chemically similar adsorbate-catalyst systems. Metric (unit) ; Computer science ; Energy (signal processing) ; Embedding ; Approximation error ; Algorithm ; Subtraction ; Similarity (geometry) ; Mathematics ; Statistics ; Artificial intelligence ; Arithmetic Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Medha Rakshit | Suman Chowdhury | Arnab Majumdar | Dipali Banerjee | Debnarayan Jana
Source: Computational Materials Science (article, 2023-09-30)
Field: Materials Science — Materials Chemistry

A first-principles study of ternary metal chalcogenides <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si21.svg" display="inline" id="d1e1775"><mml:mrow><mml:mi mathvariant="normal">Ba</mml:mi><mml:mn>2</mml:mn><mml:mi mathvariant="normal">MnX</mml:mi><mml:mn>3</mml:mn></mml:mrow></mml:math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si22.svg" display="inline" id="d1e1787"><mml:mrow><mml:mo>(</mml:mo><mml:mi mathvariant="normal">X</mml:mi><mml:mo linebreak="goodbreak" linebreakstyle="after">=</mml:mo><mml:mi mathvariant="normal">Te,Se,S</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> for efficient thermoelectric applications empowered by machine-learning interatomic potential Ternary operation ; Orthorhombic crystal system ; Density functional theory ; Thermoelectric materials ; Materials science ; Thermoelectric effect ; Phonon ; Band gap ; Condensed matter physics ; Thermal conductivity ; Figure of merit ; Crystal structure ; Thermodynamics ; Physics ; Crystallography ; Chemistry ; Computer science ; Quantum mechanics ; Optoelectronics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; 2D Materials and Applications