jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yougen Xu | Ya-Dong Gao | Lebin Su | Haiting Wu | Hao Tian | Majian Zeng | Chunqiu Xu | Xinwei Zhu | Kuangbiao Liao
Source: Angewandte Chemie International Edition (article, 2023-10-10)
Field: Chemistry — Organic Chemistry

High‐Throughput Experimentation and Machine Learning‐Assisted Optimization of Iridium‐Catalyzed Cross‐Dimerization of Sulfoxonium Ylides A novel and convenient approach that combines high-throughput experimentation (HTE) with machine learning (ML) technologies to achieve the first selective cross-dimerization of sulfoxonium ylides via iridium catalysis is presented. A variety of valuable amide-, ketone-, ester-, and N-heterocycle-substituted unsymmetrical E-alkenes are synthesized in good yields with high stereoselectivities. This mild method avoids the use of diazo compounds and is characterized by simple operation, high step-economy, and excellent chemoselectivity and functional group compatibility. The combined experimental and computational studies identify an amide-sulfoxonium ylide as a carbene precursor. Furthermore, a comprehensive exploration of the reaction space is also performed (600 reactions) and a machine learning model for reaction yield prediction has been constructed. Amide ; Carbene ; Iridium ; Catalysis ; Chemoselectivity ; Combinatorial chemistry ; Diazo ; Chemistry ; Yield (engineering) ; Computer science ; Throughput ; Organic chemistry ; Materials science Cyclopropane Reaction Mechanisms ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Dylan M. Anstine | R.I. Zubatyuk | Olexandr Isayev
Source: ChemRxiv (preprint, 2023-10-11)
Field: Materials Science — Materials Chemistry

AIMNet2: A Neural Network Potential to Meet your Neutral, Charged, Organic, and Elemental-Organic Needs Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff. Despite this attraction, the benefits of such efficiency are only impactful when an MLIP uniquely enables insight into a target system or is broadly transferable outside of the training dataset, where models achieving the latter are seldom reported. In this work, we present the 2nd generation of our atoms-in-molecules neural network potential (AIMNet2), which is applicable to species composed of up to 14 chemical elements in both neutral and charged states, making it a valuable model for modeling the majority of non-metallic compounds. Using an exhaustive dataset of 20 million hybrid quantum chemical calculations, AIMNet2 combines ML-parameterized short-range and physics-based long-range terms to attain generalizability that reaches from simple organics to diverse molecules with “exotic” element-organic bonding. We show that AIMNet2 outperforms semi-empirical GFN-xTB and is on par with reference density functional theory for interaction energy contributions, conformer search tasks, torsion rotation profiles, and molecular-to-macromolecular geometry optimization. Overall, the demonstrated chemical coverage and computational efficiency of AIMNet2 is a significant step toward providing access to MLIPs that avoid the crucial limitation of curating additional quantum chemical data and retraining with each new application. Generalizability theory ; Parameterized complexity ; Computer science ; Artificial neural network ; Organic molecules ; Density functional theory ; Valence (chemistry) ; Molecule ; Quantum ; Range (aeronautics) ; Quantum chemistry ; Statistical physics ; Artificial intelligence ; Biological system ; Computational chemistry ; Machine learning ; Chemistry ; Algorithm ; Materials science ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Danial Abooali | Reza Soleimani
Source: Scientific Reports (article, 2023-08-17)
Field: Chemistry — Organic Chemistry

Structure-based modeling of critical micelle concentration (CMC) of anionic surfactants in brine using intelligent methods Critical micelle concentration (CMC) is one of the main physico-chemical properties of surface-active agents, also known as surfactants, with diverse theoretical and industrial applications. It is influenced by basic parameters such as temperature, pH, salinity, and the chemical structure of surfactants. Most studies have only estimated CMC at fixed conditions based on the surfactant's chemical parameters. In the present study, we aimed to develop a set of novel and applicable models for estimating CMC of well-known anionic surfactants by considering both the molecular properties of surfactants and basic affecting factors such as salinity, pH, and temperature as modeling parameters. We employed the quantitative-structural property relationship technique to employ the molecular parameters of surfactant ions. We collected 488 CMC values from literature for 111 sodium-based anionic surfactants, including sulfate types, sulfonate, benzene sulfonate, sulfosuccinate, and polyoxyethylene sulfate. We computed 1410 optimized molecular descriptors for each surfactant using Dragon software to be utilized in the modelling processes. The enhanced replacement method was used for selecting the most effective descriptors for the CMC. A multivariate linear model and two non-linear models are the outputs of the present study. The non-linear models were produced using two robust machine learning approaches, stochastic gradient boosting (SGB) trees and genetic programming (GP). Statistical assessment showed highly applicable and acceptable accuracy of the newly developed models (R<sub>SGB</sub><sup>2</sup> = 0.999395 and R<sub>GP</sub><sup>2</sup> = 0.954946). The ultimate results showed the superiority and greater ability of the SGB method for making confident predictions. Pulmonary surfactant ; Critical micelle concentration ; Sulfonate ; Brine ; Micelle ; Biological system ; Computer science ; Chemistry ; Materials science ; Chemical engineering ; Sodium ; Organic chemistry Surfactants and Colloidal Systems ; Environmental Chemistry and Analysis ; Analytical Chemistry and Chromatography
Authors: B. Moses Abraham | Oriol Piqué | Mohd Aamir Khan | Francesc Viñes | Francesc Illas | Jayant K. Singh
Source: ACS Applied Materials & Interfaces (article, 2023-06-19)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Discovery of Key Descriptors for CO<sub>2</sub>Activation over Two-Dimensional Transition Metal Carbides and Nitrides Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO<sub>2</sub> activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO<sub>2</sub> adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed <i>d</i>-band center (ε<sub>d</sub>), surface metal electronegativity (χ<sub>M</sub>), and valence electron number of metal atoms (<i>M</i><sub>V</sub>) as key descriptors for CO<sub>2</sub> activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO<sub>2</sub> activation and their posterior usage. MXenes ; Electronegativity ; Materials science ; Carbide ; Nitride ; Random forest ; Transition metal ; Valence (chemistry) ; Machine learning ; Artificial intelligence ; Nanotechnology ; Computer science ; Catalysis ; Chemistry ; Physics ; Metallurgy ; Quantum mechanics ; Layer (electronics) MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Tianhao Li | Wensong Wei | Shujuan Xing | Weiqing Min | Chunjiang Zhang | Shuqiang Jiang
Source: Foods (article, 2023-08-22)
Field: Chemistry — Analytical Chemistry

Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation The limited nutritional information provided by external food representations has constrained the further development of food nutrition estimation. Near-infrared hyperspectral imaging (NIR-HSI) technology can capture food chemical characteristics directly related to nutrition and is widely used in food science. However, conventional data analysis methods may lack the capability of modeling complex nonlinear relations between spectral information and nutrition content. Therefore, we initiated this study to explore the feasibility of integrating deep learning with NIR-HSI for food nutrition estimation. Inspired by reinforcement learning, we proposed OptmWave, an approach that can perform modeling and wavelength selection simultaneously. It achieved the highest accuracy on our constructed scrambled eggs with tomatoes dataset, with a determination coefficient of 0.9913 and a root mean square error (RMSE) of 0.3548. The interpretability of our selection results was confirmed through spectral analysis, validating the feasibility of deep learning-based NIR-HSI in food nutrition estimation. Hyperspectral imaging ; Interpretability ; Artificial intelligence ; Mean squared error ; Deep learning ; Computer science ; Estimation ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Machine learning ; Mathematics ; Statistics ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Philippe Boutinaud
Source: Physical Chemistry Chemical Physics (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Luminescence–structure relationships in solids doped with Bi<sup>3+</sup> The luminescence properties of Bi<sup>3+</sup> in 177 inorganic compounds are reviewed and rationalized based on two simple and easy-to-use models. These models are applicable to any kind of compound whose crystal structure is known. The calculation of the nephelauxetic function, <i>he</i>, involved in one of the models is described step-by-step for an easy implementation in a home-made calculator or machine-learning algorithm. Finally, a criterion based on the experimental value of the Stokes shift is introduced to help in fixing the origin of the Bi<sup>3+</sup>-related emission. Luminescence ; Simple (philosophy) ; Doping ; Calculator ; Function (biology) ; Crystal structure ; Value (mathematics) ; Materials science ; Chemistry ; Computer science ; Atomic physics ; Computational physics ; Physical chemistry ; Chemical physics ; Crystallography ; Physics ; Optoelectronics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Perovskite Materials and Applications
Authors: Chong Xia | Yuanqing Zhu | Diantao Liu | Song Zhou | Yongming Feng | Jie Shi | Yang Jun
Source: Chemical Engineering Journal (article, 2023-06-20)
Field: Materials Science — Materials Chemistry

Newly developed detailed urea decomposition mechanism by marine engine urea-SCR system crystallization test and DFT calculations Biuret test ; Urea ; Decomposition ; Chemistry ; Selective catalytic reduction ; Catalysis ; Inorganic chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Catalysis and Hydrodesulfurization Studies
Authors: Ying Meng | Wangshu Yuan | Erkinbek Uulu Aktilek | Zhuozhi Zhong | Yue Wang | Rui Gao | Zhongbin Su
Source: Ecological Informatics (article, 2023-02-24)
Field: Chemistry — Analytical Chemistry

Fine hyperspectral classification of rice varieties based on self-attention mechanism Hyperspectral imaging ; Preprocessor ; Computer science ; Convolutional neural network ; Artificial intelligence ; Canopy ; Pattern recognition (psychology) ; Precision agriculture ; Remote sensing ; Agricultural engineering ; Agriculture ; Engineering ; Botany ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Hyukjoon Kwon | Motoyuki Shiga | Hajime Kimizuka | Takuji Oda
Source: Acta Materialia (article, 2023-02-03)
Field: Materials Science — Materials Chemistry

Accurate description of hydrogen diffusivity in bcc metals using machine-learning moment tensor potentials and path-integral methods Thermal diffusivity ; Materials science ; Hydrogen ; Diffusion ; Range (aeronautics) ; Experimental data ; Statistical physics ; Thermodynamics ; Density functional theory ; Computational physics ; Computational chemistry ; Physics ; Chemistry ; Mathematics ; Quantum mechanics ; Statistics Nuclear Materials and Properties ; Hydrogen Storage and Materials ; Advanced Chemical Physics Studies
Authors: Ruimin Sun | Mingyue Dou | Ziliang Chen | Ruirui Wang | Xiangyi Zheng | Yuxiang Zhang | Chenggang Zhou | Prashanth W. Menezes
Source: Battery energy (article, 2023-02-19)
Field: Chemistry — Inorganic Chemistry

Engineering strategies of metal‐organic frameworks toward advanced batteries Abstract Metal‐organic frameworks (MOFs) integrate several advantages such as adjustable pore sizes, large specific surface areas, controllable geometrical morphology, and feasible surface modification. Benefiting from these appealing merits, MOFs have recently been extensively explored in the field of advanced secondary batteries. However, a systematic summarization of the specific functional units that these materials can act as in batteries as well as their related design strategies to underline their functions has not been perceived to date. Motivated by this point, this review dedicates to the elucidation of diverse functions of MOFs for batteries, which involve the electrodes, separators, interface modifiers, and electrolytes. Particularly, the main engineering strategies based on the physical and chemical features to enable their enhanced performance have been highlighted for the individual functions. In addition, perspectives and possible research questions in the future development of these materials have also been outlined. This review captures such progress ranging from fundamental understanding and optimized protocols to multidirectional applications of MOF‐based materials in advanced secondary batteries. Automatic summarization ; Nanotechnology ; Metal-organic framework ; Computer science ; Systems engineering ; Biochemical engineering ; Materials science ; Engineering ; Artificial intelligence ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: William C. Witt | Cas van der Oord | Elena Gelžinytė | Teemu Järvinen | Andres Ross | James P. Darby | Cheuk Hin Ho | William J. Baldwin | Matthias Sachs | James R. Kermode | Noam Bernstein | Gábor Cśanyi | Christoph Ortner
Source: The Journal of Chemical Physics (article, 2023-10-23)
Field: Materials Science — Materials Chemistry

ACEpotentials.jl: A Julia implementation of the atomic cluster expansion We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion [R. Drautz, Phys. Rev. B 99, 014104 (2019)]. As the latter provides a complete description of atomic environments, including invariance to overall translation and rotation as well as permutation of like atoms, the resulting potentials are systematically improvable and data efficient. Furthermore, the descriptor's expressiveness enables use of a linear model, facilitating rapid evaluation and straightforward application of Bayesian techniques for active learning. We summarize the capabilities of ACEpotentials.jl and demonstrate its strengths (simplicity, interpretability, robustness, performance) on a selection of prototypical atomistic modelling workflows. Interpretability ; Robustness (evolution) ; Computer science ; Workflow ; Translation (biology) ; Software ; Permutation (music) ; Simplicity ; Gravitational singularity ; Quantum ; Algorithm ; Theoretical computer science ; Computational science ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics ; Programming language ; Database ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jing Li | Chaojun Wang | Zixun Yu | Yuan Chen | Wei Li
Source: Small (review, 2023-08-01)
Field: Materials Science — Materials Chemistry

MXenes for Zinc‐Based Electrochemical Energy Storage Devices As an economical and safer alternative to lithium, zinc (Zn) is promising for realizing new high-performance electrochemical energy storage devices, such as Zn-ion batteries, Zn-ion hybrid capacitors, and Zn-air batteries. Well-designed electrodes are needed to enable efficient Zn electrochemistry for energy storage. Two-dimensional transition metal carbides and nitrides (MXenes) are emerging materials with unique electrical, mechanical, and electrochemical properties and versatile surface chemistry. They are potential material candidates for constructing high-performance electrodes of Zn-based energy storage devices. This review first briefly introduces the working mechanisms of the three Zn-based energy storage devices. Then, the recent progress on the synthesis, chemical functionalization, and structural design of MXene-based electrodes is summarized. Their performance in Zn-based devices is analyzed to establish relations between material properties, electrode structures, and device performance. Last, several research topics are proposed to be addressed for developing practical MXene-based electrodes for Zn-based energy storage devices to enable their commercialization and broad adoption in the near future. MXenes ; Energy storage ; Materials science ; Electrochemical energy storage ; Nanotechnology ; Electrochemical energy conversion ; Supercapacitor ; Electrode ; Electrochemistry ; Surface modification ; Battery (electricity) ; Commercialization ; Chemical engineering ; Chemistry ; Engineering MXene and MAX Phase Materials ; Advanced battery technologies research ; Supercapacitor Materials and Fabrication
Authors: Ziang Meng | Han Yan | Peixin Qin | Xiaorong Zhou | Xiaoning Wang | Hongyu Chen | Li Liu | Zhiqi Liu
Source: Advanced Functional Materials (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Topotactic Transition: A Promising Opportunity for Creating New Oxides Abstract Topotactic transition is a structural phase change in a matrix crystal lattice mediated by the ordered loss/gain and rearrangement of atoms, leading to unusual coordination environments and metal atoms with rare valent states. As early as in 1990s, low temperature hydride reduction is utilized to realize the topotactic transition. Since then, topological transformations are developed via multiple approaches. Especially, the recent discovery of the Ni‐based superconductivity in infinite‐layer nickelates has greatly boosted the topotactic transition mean to synthesizing new oxides for exploring exotic functional properties. In this review, a detailed and generalized introduction is provided to oxygen‐related topotactic transition. The main body of the review includes four parts: the structure‐facilitated effects, the mechanism of the topotactic transition, some examples of topotactic transition methods adopted in different metal oxides (V, Mn, Fe, Co, Ni) and the related applications. This study is to provide timely and thorough strategies to successfully realize topotactic transitions for researchers who are eager to create new oxide phases or new oxide materials with desired functions. Transition metal ; Materials science ; Oxide ; Superconductivity ; Phase transition ; Nanotechnology ; Hydride ; Superconducting transition temperature ; Chemical physics ; Crystal structure ; Metal ; Crystallography ; Condensed matter physics ; Chemistry ; Metallurgy ; Catalysis ; Physics Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials ; Semiconductor materials and devices
Authors: Sajjad Akbarzadeh | Yoann Paint | Marie‐Georges Olivier
Source: Electrochimica Acta (article, 2023-01-22)
Field: Materials Science — Materials Chemistry

A comparative study of different sol-gel coatings for sealing the plasma electrolytic oxidation (PEO) layer on AA2024 alloy Materials science ; Sol-gel ; Plasma electrolytic oxidation ; Scanning electron microscope ; Dielectric spectroscopy ; Corrosion ; Coating ; Chemical engineering ; Porosity ; Layer (electronics) ; Fourier transform infrared spectroscopy ; Alloy ; Electrolyte ; Composite material ; Electrochemistry ; Electrode ; Nanotechnology ; Chemistry Corrosion Behavior and Inhibition ; Magnesium Alloys: Properties and Applications ; Anodic Oxide Films and Nanostructures
Authors: Felipe Lopes Oliveira | Conor Cleeton | Rodrigo Neumann Barros Ferreira | Binquan Luan | Amir H. Farmahini | Lev Sarkisov | M. Steiner
Source: Scientific Data (article, 2023-04-20)
Field: Chemistry — Inorganic Chemistry

CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture applications. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO<sub>2</sub> and N<sub>2</sub> adsorption isotherms on 690 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, MPNN, PACMOF, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms. Nanoporous ; Adsorption ; Partial charge ; Force field (fiction) ; Database ; Metal-organic framework ; Monte Carlo method ; Charge (physics) ; Grand canonical ensemble ; Chemistry ; Molecule ; Computer science ; Physical chemistry ; Physics ; Organic chemistry ; Statistics ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications