jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hang T. Dang | Arka Porey | Sachchida Nand | Ramon Trevino | Patrick Manning-Lorino | William B. Hughes | Seth O. Fremin | William T. Thompson | Shree Krishna Dhakal | Hadi D. Arman | Oleg V. Larionov
Source: Chemical Science (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Kinetically-driven reactivity of sulfinylamines enables direct conversion of carboxylic acids to sulfinamides Sulfinamides are some of the most centrally important four-valent sulfur compounds that serve as critical entry points to an array of emergent medicinal functional groups, molecular tools for bioconjugation, and synthetic intermediates including sulfoximines, sulfonimidamides, and sulfonimidoyl halides, as well as a wide range of other S(iv) and S(vi) functionalities. Yet, the accessible chemical space of sulfinamides remains limited, and the approaches to sulfinamides are largely confined to two-electron nucleophilic substitution reactions. We report herein a direct radical-mediated decarboxylative sulfinamidation that for the first time enables access to sulfinamides from the broad and structurally diverse chemical space of carboxylic acids. Our studies show that the formation of sulfinamides prevails despite the inherent thermodynamic preference for the radical addition to the nitrogen atom, while a machine learning-derived model facilitates prediction of the reaction efficiency based on computationally generated descriptors of the underlying radical reactivity. Chemistry ; Chemical space ; Reactivity (psychology) ; Nucleophile ; Combinatorial chemistry ; Organic chemistry ; Catalysis ; Biochemistry ; Drug discovery Sulfur-Based Synthesis Techniques ; Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods
Authors: Yaowei Wang | Qingli Tang | Xinchen Xu | Paul Weng | Tao Ying | Yao Yang | Xiaoqin Zeng | Hong Zhu
Source: Acta Materialia (article, 2023-06-01)
Field: Materials Science — Biomaterials

Accelerated discovery of magnesium intermetallic compounds with sluggish corrosion cathodic reactions through active learning and DFT calculations Materials science ; Intermetallic ; Adsorption ; Corrosion ; Density functional theory ; Cathodic protection ; Magnesium ; Galvanic corrosion ; Hydrogen ; Chemical physics ; Electrochemistry ; Metallurgy ; Physical chemistry ; Computational chemistry ; Chemistry Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics
Authors: Lisha Liu | Yuanhua Li | Arwa Abdulkreem AL‐Huqail | Elimam Ali | Tamim Alkhalifah | Fahad Alturise | Hina Ali
Source: Chemosphere (article, 2023-04-24)
Field: Materials Science — Materials Chemistry

Green synthesis of Fe3O4 nanoparticles using Alliaceae waste (Allium sativum) for a sustainable landscape enhancement using support vector regression Allium sativum ; Support vector machine ; Nanoparticle ; Botany ; Computer science ; Biology ; Materials science ; Artificial intelligence ; Nanotechnology Nanoparticles: synthesis and applications ; Nanomaterials for catalytic reactions
Authors: Pascal T. Salzbrenner | Se Hun Joo | Lewis J. Conway | Peter I. C. Cooke | Bonan Zhu | Milosz P. Matraszek | William C. Witt | Chris J. Pickard
Source: The Journal of Chemical Physics (article, 2023-10-10)
Field: Materials Science — Materials Chemistry

Developments and further applications of ephemeral data derived potentials Machine-learned interatomic potentials are fast becoming an indispensable tool in computational materials science. One approach is the ephemeral data-derived potential (EDDP), which was designed to accelerate atomistic structure prediction. The EDDP is simple and cost-efficient. It relies on training data generated in small unit cells and is fit using a lightweight neural network, leading to smooth interactions which exhibit the robust transferability essential for structure prediction. Here, we present a variety of applications of EDDPs, enabled by recent developments of the open-source EDDP software. New features include interfaces to phonon and molecular dynamics codes, as well as deployment of the ensemble deviation for estimating the confidence in EDDP predictions. Through case studies ranging from elemental carbon and lead to the binary scandium hydride and the ternary zinc cyanide, we demonstrate that EDDPs can be trained to cover wide ranges of pressures and stoichiometries, and used to evaluate phonons, phase diagrams, superionicity, and thermal expansion. These developments complement continued success in accelerated structure prediction. Computer science ; Suite ; Computational science ; Ternary operation ; Ephemeral key ; Ternary plot ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Ibrahim Orhan | Tu C. Le | Ravichandar Babarao | Aaron W. Thornton
Source: Communications Chemistry (article, 2023-10-03)
Field: Chemistry — Inorganic Chemistry

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors Metal-Organic frameworks (MOFs) have been considered for various gas storage and separation applications. Theoretically, there are an infinite number of MOFs that can be created; however, a finite amount of resources are available to evaluate each one. Computational methods can be adapted to expedite the process of evaluation. In the context of CO<sub>2</sub> capture, this paper investigates the method of screening MOFs using machine learning trained on molecular simulation data. New descriptors are introduced to aid this process. Using all descriptors, it is shown that machine learning can predict the CO<sub>2</sub> adsorption, with an R<sup>2</sup> of above 0.9. The introduced Effective Point Charge (EPoCh) descriptors, which assign values to frameworks' partial charges based on the expected CO<sub>2</sub> uptake of an equivalent point charge in isolation, are shown to be the second most important group of descriptors, behind the Henry coefficient. Furthermore, the EPoCh descriptors are hundreds of thousands of times faster to obtain compared with the Henry coefficient, and they achieve similar results when identifying top candidates for CO<sub>2</sub> capture using pseudo-classification predictions. Context (archaeology) ; Process (computing) ; Computer science ; Artificial intelligence ; Charge (physics) ; Partial charge ; Point (geometry) ; Metal-organic framework ; Machine learning ; Series (stratigraphy) ; Adsorption ; Epoch (astronomy) ; Biological system ; Chemistry ; Mathematics ; Physics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science
Authors: Yunsheng Liu | Xingfeng He | Yifei Mo
Source: npj Computational Materials (article, 2023-09-26)
Field: Materials Science — Materials Chemistry

Discrepancies and error evaluation metrics for machine learning interatomic potentials Abstract Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics (MD) simulations. In this study, we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics, defects, and rare events (REs), compared to ab initio methods. We find that low averaged errors by current MLIP testing are insufficient, and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the evaluation metrics, and the proposed process of developing such metrics are general to MLIPs, thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling. Computer science ; Molecular dynamics ; Interatomic potential ; Atom (system on chip) ; Process (computing) ; Machine learning ; Artificial intelligence ; Statistical physics ; Data mining ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Mateus Quintano | Renaldo T. Moura | Elfi Kraka
Source: Chemical Physics Letters (article, 2023-06-10)
Field: Chemistry — Physical and Theoretical Chemistry

The pKa rule in light of local mode force constants Cocrystal ; Protonation ; Acid dissociation constant ; Chemistry ; Dissociation (chemistry) ; Pyridine ; Context (archaeology) ; Dissociation constant ; Computational chemistry ; Molecule ; Physical chemistry ; Aqueous solution ; Hydrogen bond ; Organic chemistry ; Ion Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Yuanning Zhai | Lei Zhou | Hengnian Qi | Pan Gao | Chu Zhang
Source: Plant Phenomics (review, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Application of Visible/Near-Infrared Spectroscopy and Hyperspectral Imaging with Machine Learning for High-Throughput Plant Heavy Metal Stress Phenotyping: A Review Heavy metal pollution is becoming a prominent stress on plants. Plants contaminated with heavy metals undergo changes in external morphology and internal structure, and heavy metals can accumulate through the food chain, threatening human health. Detecting heavy metal stress on plants quickly, accurately, and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties. Traditional chemical reagent-based detection methods are laborious, destructive, time-consuming, and costly. The internal and external structures of plants can be altered by heavy metal contamination, which can lead to changes in plants' absorption and reflection of light. Visible/near-infrared (V/NIR) spectroscopy can obtain plant spectral information, and hyperspectral imaging (HSI) can obtain spectral and spatial information in simple, speedy, and nondestructive ways. These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants. This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress, including unstressed and stressed identification, stress types identification, stress degrees identification, and heavy metal content estimation. The vegetation indexes, full-range spectra, and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed. The advantages, limitations, challenges, and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed. Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping. Hyperspectral imaging ; Stress (linguistics) ; Spectroscopy ; Environmental science ; Throughput ; Near-infrared spectroscopy ; Multispectral image ; Remote sensing ; Biological system ; Materials science ; Process engineering ; Computer science ; Artificial intelligence ; Optics ; Engineering ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Zhipeng Ding | Wei Su | Yinlong Luo | Lipengan Ye | Wenlong Li | Yuanhang Zhou | Bin Tang | Hongbing Yao
Source: Materials & Design (article, 2023-09-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial neural network-based inverse design of metasurface absorber with tunable absorption window Although the development of graphene broadband terahertz (THz) absorbers is relatively mature, studies focusing on modulating the absorption window remain limited. Existing research has mainly concentrated on achieving absorption window switching via the phase transition of materials like vanadium dioxide (VO2). However, this method typically allows for switching within two specific windows, lacking the capacity for arbitrary and flexible regulation. This study introduces a novel approach involving a bilayer graphene metasurface absorber (BGMA) to address this limitation, which allows arbitrary manipulation of the absorption windows. To facilitate rapid and accurate selection of the structural parameters, an artificial neural network (ANN)-based inverse design method is employed, which achieves a tunable absorption bandwidth of 6.70 THz with an error amount to 0.59%. Through in-depth mechanistic analyses, it is demonstrated that the BGMA can achieve tunable absorption between a low-frequency broadband absorption (L-FBA) mode and a high-frequency broadband absorption (H-FBA) mode. This tunability arises from the transition between graphene localized surface plasmon resonance and graphene surface plasmon resonance. Overall, the outcomes of this study underscore the remarkable tunable performance of the BGMA achieved through ANN-based inverse design. This innovation holds significant promise for applications in security detection, and object stealth. Materials science ; Terahertz radiation ; Broadband ; Absorption (acoustics) ; Graphene ; Plasmon ; Bandwidth (computing) ; Optoelectronics ; Surface plasmon ; Optics ; Computer science ; Nanotechnology ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Thermal Radiation and Cooling Technologies
Authors: Agustami Sitorus | Ravipat Lapcharoensuk
Source: Microchemical Journal (article, 2023-10-05)
Field: Chemistry — Analytical Chemistry

A rapid method to predict type and adulteration of coconut milk by near-infrared spectroscopy combined with machine learning and chemometric tools Mathematics ; Linear discriminant analysis ; Partial least squares regression ; Support vector machine ; Artificial intelligence ; Coconut oil ; Calibration ; Machine learning ; Food science ; Pattern recognition (psychology) ; Statistics ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Coconut Research and Applications ; Identification and Quantification in Food
Authors: Bahram Ghorbani | Sohrab Zendehboudi | Zahra Alizadeh Afrouzi
Source: Journal of Cleaner Production (article, 2023-02-24)
Field: Materials Science — Materials Chemistry

Thermo-economic optimization of a novel hybrid structure for power generation and portable hydrogen and ammonia storage based on magnesium–chloride thermochemical process and liquefied natural gas cryogenic energy Liquefaction ; Exergy ; Waste management ; Liquefied natural gas ; Hydrogen production ; Process engineering ; Hydrogen storage ; Natural gas ; Ammonia production ; Environmental science ; Liquid hydrogen ; Air separation ; Exergy efficiency ; Hydrogen ; Energy storage ; Chemistry ; Ammonia ; Engineering ; Thermodynamics ; Power (physics) Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Carbon Dioxide Capture Technologies
Authors: Zahra Asgar Pour | Yasser A. Alassmy | Khaled O. Sebakhy
Source: Crystals (article, 2023-06-15)
Field: Chemistry — Inorganic Chemistry

A Survey on Zeolite Synthesis and the Crystallization Process: Mechanism of Nucleation and Growth Steps Zeolites, as a class of crystalline minerals, find a wide range of applications in various fields, such as catalysis, separation, and adsorption. More recently, these materials have also been developed for advanced applications, such as gas storage, medical applications, magnetic adsorption, and zeolitic-polymeric membranes. To effectively design zeolites for such intriguing applications, it is crucial to intelligently adjust their crystal size, morphology, and defect population in relation to crystal perfection. Optimizing these fundamental parameters necessitates a deep understanding of zeolite formation mechanisms, encompassing the thermodynamics and kinetics of nucleation steps as well as crystallite growth. In this review, we discuss the formation of zeolites from this perspective, drawing on recent studies that highlight new achievements in remodeling and modifying zeolite synthesis routes. The ultimate aim is to provide better comprehension and optimize the functionality of zeolites for the aforementioned applications. Nucleation ; Zeolite ; Crystallization ; Materials science ; Crystallite ; Nanotechnology ; Adsorption ; Crystal growth ; Mechanism (biology) ; Crystal (programming language) ; Chemical engineering ; Chemistry ; Catalysis ; Computer science ; Physical chemistry ; Organic chemistry ; Engineering ; Crystallography ; Metallurgy ; Physics Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis for Biomass Conversion
Authors: Baskar Thangaraj | Fatima Mumtaz | Yawar Abbas | Dalaver H. Anjum | Pravin Raj Solomon | Jamal Hassan
Source: Molecules (article, 2023-04-10)
Field: Materials Science — Materials Chemistry

Synthesis of Graphene Oxide from Sugarcane Dry Leaves by Two-Stage Pyrolysis Natural or synthetic graphite as precursors for the preparation of graphene oxide (GO) have constraints due to their limited availability, high reaction temperature for processing of synthetic graphite and higher generation cost. The use of oxidants, long reaction duration, the generation of toxic gases and residues of inorganic salts, the degree of hazard and low yield are some of the disadvantages of the oxidative-exfoliation methods. Under these circumstances, biomass waste usage as a precursor is a viable alternative. The conversion of biomass into GO by the pyrolysis method is ecofriendly with diverse applications, which partially overcomes the waste disposal problem encountered by the existing methods. In this study, graphene oxide (GO) is prepared from dry leaves of sugarcane plant through a two-step pyrolysis method using ferric (III) citrate as a catalyst, followed by treatment with conc. H<sub>2</sub>SO<sub>4</sub>. The synthesized GO is analyzed by UV-Vis., FTIR, XRD, SEM, TEM, EDS and Raman spectroscopy. The synthesized GO has many oxygen-containing functional groups (-OH, C-OH, COOH, C-O). It shows a sheet-like structure with a crystalline size of 10.08 nm. The GO has a graphitic structure due to the Raman shift of G (1339 cm<sup>-1</sup>) and D (1591 cm<sup>-1</sup>) bands. The prepared GO has multilayers due to the ratio of 0.92 between <i>I<sub>D</sub></i> and <i>I<sub>G</sub></i>. The weight ratios between carbon and oxygen are examined by SEM-EDS and TEM-EDS and found to be 3.35 and 38.11. This study reveals that the conversion of sugarcane dry leaves into the high-value-added material GO becomes realistic and feasible and thus reduces the production cost of GO. Graphene ; Pyrolysis ; Oxide ; Raman spectroscopy ; Ferric ; Fourier transform infrared spectroscopy ; Biomass (ecology) ; Catalysis ; Yield (engineering) ; Graphite ; Graphite oxide ; Nuclear chemistry ; Materials science ; Exfoliation joint ; Chemistry ; Chemical engineering ; Organic chemistry ; Nanotechnology ; Agronomy ; Metallurgy Graphene research and applications ; Graphene and Nanomaterials Applications ; Fiber-reinforced polymer composites
Authors: Kuikui Zhang | Zeai Huang | Mingkai Yang | Mengying Liu | Yunxiao Zhou | Junjie Zhan | Ying Zhou
Source: SusMat (article, 2023-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent progress in melt pyrolysis: Fabrication and applications of high‐value carbon materials from abundant sources Abstract The escalating demand for sophisticated carbon products, including carbon black, carbon nanotubes (CNTs), and graphene, has yet to be adequately addressed by conventional techniques with respect to large‐scale, efficient, and controllable carbon material synthesis. Molten pyrolysis emerges as a propitious strategy for generating such high‐value carbon materials. Abundant carbon sources encompassing methane (CH 4 ), carbon dioxide (CO 2 ), biomass, and plastics can undergo thermal decomposition into carbon constituents within molten metal or salt media. This methodology not only obviates dependence on traditional fossil fuels but additionally enables modulation of carbon material morphologies by varying the molten media, thereby presenting substantial potential for effective and controlled carbon material fabrication. In this review, we examine the capacity of molten pyrolysis in producing high‐value carbon materials derived from CH 4 , CO 2 , biomass, and plastics. Concurrently, we present a detailed overview of the potential applications of this novel methodology, particularly emphasizing its relevance in the fields of supercapacitors, flexible materials, and electrochemical cells. Furthermore, we contemplate future trajectories for molten pyrolysis, accentuating that amalgamation with auxiliary processes or technologies—like renewable energy systems and carbon capture and storage—represents a remarkably promising route for continued investigation. Pyrolysis ; Carbon fibers ; Materials science ; Molten salt ; Nanotechnology ; Graphene ; Supercapacitor ; Renewable energy ; Biomass (ecology) ; Fossil fuel ; Methane ; Fabrication ; Process engineering ; Waste management ; Electrochemistry ; Chemistry ; Composite material ; Organic chemistry ; Engineering ; Metallurgy Supercapacitor Materials and Fabrication ; Thermochemical Biomass Conversion Processes ; Advancements in Battery Materials
Authors: Huda Salem AlSalem | Khadijah Mohammedsaleh Katubi | Mona Saad Binkadem | Soha T. Al‐Goul | Ahmed M. Wahba
Source: ACS Omega (article, 2023-10-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fabrication of Asymmetric Supercapacitors (AC@GQDs//AC) with High Electrochemical Performance Utilizing Activated Carbon and Graphene Quantum Dots Sugar cane bagasse stands as a prevalent and abundant form of solid agricultural waste, making it a prime candidate for innovative utilization. Harnessing its potential, we embarked on a groundbreaking endeavor to evaluate the sustainability of a molasses-based hydrothermal process to produce graphene quantum dots (GQDs). This pioneering initiative promises remarkable environmental benefits and holds immense economic potential. Embedding crystalline GQDs in activated carbon (AC) boost electrochemical efficiency by enhancing charge-transfer and ion migration kinetics. Optical, structural, and morphological evaluations were used to confirm the formation of GQDs. Transmission electron microscopy (TEM) investigation showed the size, shape, and fact that GQDs were monodispersed, and X-ray diffraction and Fourier transform infrared determined the structure of GQDs. The electrodes with negative (AC) and positive (AC@GQDs) polarity demonstrate a considerable specific capacitance of 220 and 265 F g<sup>-1</sup>, respectively, when measured at 0.5 A g<sup>-1</sup>. Additionally, these electrodes exhibit high-rate capabilities of 165 and 230 F g<sup>-1</sup> when measured at 5 A g<sup>-1</sup>, as determined by galvanostatic charge-discharge techniques. The supercapacitor device comprising asymmetric AC//AC@GQDs exhibits a specific capacitance of 118 F g<sup>-1</sup>. Furthermore, the asymmetric device exhibits exceptional cycling behavior, with an impressive 92% capacitance retention even after undergoing 10,000 cycles. This remarkable performance underscores the immense potential of both the negative and positive electrodes for real-world supercapacitor applications. Such findings pave the way for promising advancements in the field and offer exciting prospects for practical utilization. Supercapacitor ; Graphene ; Materials science ; Quantum dot ; Capacitance ; Nanotechnology ; Electrode ; Chemical engineering ; Optoelectronics ; Chemistry Supercapacitor Materials and Fabrication ; Carbon and Quantum Dots Applications ; Advancements in Battery Materials