jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher Egan-Morriss | Richard L. Kimber | Nigel A. Powell | Jonathan R. Lloyd
Source: Nanoscale Advances (review, 2021-12-21)
Field: Materials Science — Materials Chemistry

Biotechnological synthesis of Pd-based nanoparticle catalysts Palladium metal nanoparticles are excellent catalysts used industrially for reactions such as hydrogenation and Heck and Suzuki C-C coupling reactions. However, the global demand for Pd far exceeds global supply, therefore the sustainable use and recycling of Pd is vital. Conventional chemical synthesis routes of Pd metal nanoparticles do not meet sustainability targets due to the use of toxic chemicals, such as organic solvents and capping agents. Microbes are capable of bioreducing soluble high oxidation state metal ions to form metal nanoparticles at ambient temperature and pressure, without the need for toxic chemicals. Microbes can also reduce metal from waste solutions, revalorising these waste streams and allowing the reuse of precious metals. Pd nanoparticles supported on microbial cells (bio-Pd) can catalyse a wide array of reactions, even outperforming commercial heterogeneous Pd catalysts in several studies. However, to be considered a viable commercial option, the intrinsic activity and selectivity of bio-Pd must be enhanced. Many types of microorganisms can produce bio-Pd, although most studies so far have been performed using bacteria, with metal reduction mediated by hydrogenase or formate dehydrogenase enzymes. Dissimilatory metal-reducing bacteria (DMRB) possess additional enzymes adapted for extracellular electron transport that potentially offer greater control over the properties of the nanoparticles produced. A recent and important addition to the field are bio-bimetallic nanoparticles, which significantly enhance the catalytic properties of bio-Pd. In addition, systems biology can integrate bio-Pd into biocatalytic processes, and processing techniques may enhance the catalytic properties further, such as incorporating additional functional nanomaterials. This review aims to highlight aspects of enzymatic metal reduction processes that can be bioengineered to control the size, shape, and cellular location of bio-Pd in order to optimise its catalytic properties. Catalysis ; Nanoparticle ; Nanotechnology ; Materials science ; Chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; Microbial Fuel Cells and Bioremediation ; Advanced Photocatalysis Techniques
Authors: Santiago Vargas | Matthew R. Hennefarth | Z. J. Liu | Anastassia N. Alexandrova
Source: Journal of Chemical Theory and Computation (article, 2021-09-03)
Field: Materials Science — Materials Chemistry

Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density Reaction barriers are key to our understanding of chemical reactivity and catalysis. Certain reactions are so seminal in chemistry that countless variants, with or without catalysts, have been studied, and their barriers have been computed or measured experimentally. This wealth of data represents a perfect opportunity to leverage machine learning models, which could quickly predict barriers without explicit calculations or measurement. Here, we show that the topological descriptors of the quantum mechanical charge density in the reactant state constitute a set that is both rigorous and continuous and can be used effectively for the prediction of reaction barrier energies to a high degree of accuracy. We demonstrate this on the Diels-Alder reaction, highly important in biology and medicinal chemistry, and as such, studied extensively. This reaction exhibits a range of barriers as large as 270 kJ/mol. While we trained our single-objective supervised (labeled) regression algorithms on simpler Diels-Alder reactions in solution, they predict reaction barriers also in significantly more complicated contexts, such a Diels-Alder reaction catalyzed by an artificial enzyme and its evolved variants, in agreement with experimental changes in <i>k</i><sub>cat</sub>. We expect this tool to apply broadly to a variety of reactions in solution or in the presence of a catalyst, for screening and circumventing heavily involved computations or experiments. Leverage (statistics) ; Catalysis ; Computational chemistry ; Diels–Alder reaction ; Chemistry ; Reactivity (psychology) ; Reaction mechanism ; Quantum chemistry ; Computation ; Computer science ; Density functional theory ; Combinatorial chemistry ; Machine learning ; Organic chemistry ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Akhilesh Gandhi | M. M. Faruque Hasan
Source: Current Opinion in Chemical Engineering (article, 2021-09-27)
Field: Materials Science — Materials Chemistry

Machine learning for the design and discovery of zeolites and porous crystalline materials Sketch ; Process (computing) ; Business process discovery ; Computer science ; Scientific discovery ; Porosity ; Nanotechnology ; Biochemical engineering ; Materials science ; Artificial intelligence ; Process engineering ; Engineering ; Work in process ; Algorithm Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Souad Achour | Samira Amokrane | Salah Chegrouche | Djamel Nibou | Oussama Baaloudj
Source: Research on Chemical Intermediates (article, 2021-07-24)
Field: Chemistry — Inorganic Chemistry

Artificial neural network modeling of the hexavalent uranium sorption onto chemically activated bentonite Bentonite ; Adsorption ; Uranium ; Sorption ; Langmuir adsorption model ; Chemistry ; Hydrochloric acid ; Nuclear chemistry ; Materials science ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Metallurgy Radioactive element chemistry and processing ; Hydrocarbon exploration and reservoir analysis ; Chemical Synthesis and Characterization
Authors: Subhasish Goswami | Rajdeep Ghosh | Arohan Neog | Bitopan Das
Source: Materials Today Proceedings (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep learning based approach for prediction of glass transition temperature in polymers Glass transition ; Polymer ; Materials science ; Scope (computer science) ; Range (aeronautics) ; Transition (genetics) ; Thermodynamics ; Chemical physics ; Computer science ; Statistical physics ; Composite material ; Chemistry ; Physics Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Vipul Mann | Venkat Venkatasubramanian
Source: Computers & Chemical Engineering (article, 2021-09-11)
Field: Materials Science — Materials Chemistry

Retrosynthesis prediction using grammar-based neural machine translation: An information-theoretic approach Retrosynthetic analysis ; Artificial intelligence ; Computer science ; Grammar ; Machine translation ; Redundancy (engineering) ; Natural language processing ; Machine learning ; Rule-based machine translation ; Translation (biology) ; Character (mathematics) ; Mathematics ; Chemistry ; Linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Patrick Trampert | Dmitri Rubinstein | Faysal Boughorbel | Christian Schlinkmann | Maria Luschkova | Philipp Slusallek | Tim Dahmen | Stefan Sandfeld
Source: Crystals (article, 2021-03-05)
Field: Materials Science — Materials Chemistry

Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling The analysis of microscopy images has always been an important yet time consuming process in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points. Computer science ; Artificial intelligence ; Convolutional neural network ; Artificial neural network ; Parametric statistics ; Synthetic data ; Segmentation ; Deep learning ; Parametric model ; Adaptive sampling ; Machine learning ; Generalization ; Pattern recognition (psychology) ; Process (computing) ; Sampling (signal processing) ; Generative model ; Generative grammar ; Computer vision ; Mathematics ; Monte Carlo method Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Song Wang | Zili Wu | Sheng Dai | De‐en Jiang
Source: Angewandte Chemie International Edition (article, 2021-03-04)
Field: Materials Science — Materials Chemistry

Deep Learning Accelerated Determination of Hydride Locations in Metal Nanoclusters Although the coordinates of the metal atoms can be accurately determined by X-ray crystallography, locations of hydrides in metal nanoclusters are challenging to determine. In principle, neutron crystallography can be employed to pinpoint the hydride positions, but it requires a large crystal and a neutron source, which prevents its routine use. Here, we present a deep-learning approach that can accelerate determination of hydride locations in single-crystal X-ray structure of metal nanoclusters of different sizes. We demonstrate the efficiency of our method in predicting the most probable hydride sites and their combinations to determine the total structure for two recently reported copper nanoclusters, [Cu<sub>25</sub> H<sub>10</sub> (SPhCl<sub>2</sub> )<sub>18</sub> ]<sup>3-</sup> and [Cu<sub>61</sub> (S<sup>t</sup> Bu)<sub>26</sub> S<sub>6</sub> Cl<sub>6</sub> H<sub>14</sub> ]<sup>+</sup> whose hydride locations have not been determined by neutron diffraction. Our method can be generalized and applied to other metal systems, thereby eliminating a bottleneck in atomically precise metal hydride nanochemistry. Nanoclusters ; Hydride ; Metal ; Neutron diffraction ; Nanochemistry ; Materials science ; Crystallography ; Crystal structure ; Diffraction ; Nanotechnology ; Chemistry ; Physics ; Metallurgy ; Optics Nanocluster Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Keverne A. Louison | Ian L. Dryden | Charles A. Laughton
Source: Journal of Chemical Theory and Computation (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

GLIMPS: A Machine Learning Approach to Resolution Transformation for Multiscale Modeling We describe a general approach to transforming molecular models between different levels of resolution, based on machine learning methods. The approach uses a matched set of models at both levels of resolution for training, but requires only the coordinates of their particles and no side information (e.g., templates for substructures, defined mappings, or molecular mechanics force fields). Once trained, the approach can transform further molecular models of the system between the two levels of resolution in either direction with equal facility. Resolution (logic) ; Computer science ; Transformation (genetics) ; Set (abstract data type) ; Artificial intelligence ; Low resolution ; Machine learning ; High resolution ; Algorithm ; Chemistry ; Programming language Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Juan He | Chu Zhang | Lei Zhou | Yong He
Source: Infrared Physics & Technology (article, 2021-06-10)
Field: Chemistry — Analytical Chemistry

Simultaneous determination of five micro-components in Chrysanthemum morifolium (Hangbaiju) using near-infrared hyperspectral imaging coupled with deep learning with wavelength selection Hyperspectral imaging ; Partial least squares regression ; Spectroradiometer ; Computer science ; Dimensionality reduction ; Pattern recognition (psychology) ; Regression ; Feature selection ; Support vector machine ; Chrysanthemum morifolium ; Principal component regression ; Convolutional neural network ; Multispectral image ; Artificial intelligence ; Selection (genetic algorithm) ; Near-infrared spectroscopy ; Principal component analysis ; Biological system ; Mathematics ; Optics ; Statistics ; Machine learning ; Physics ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Nicolas Brodusch | S. Brahimi | Evelin Barbosa de Mélo | Jun Song | Stephen Yue | Nicolas Piché | Raynald Gauvin
Source: Scanning (article, 2021-05-04)
Field: Materials Science — Surfaces, Coatings and Films

Scanning Electron Microscopy versus Transmission Electron Microscopy for Material Characterization: A Comparative Study on High-Strength Steels The microstructures of quenched and tempered steels have been traditionally explored by transmission electron microscopy (TEM) rather than scanning electron microscopy (SEM) since TEM offers the high resolution necessary to image the structural details that control the mechanical properties. However, scanning electron microscopes, apart from providing larger area coverage, are commonly available and cheaper to purchase and operate compared to TEM and have evolved considerably in terms of resolution. This work presents detailed comparison of the microstructure characterization of quenched and tempered high-strength steels with TEM and SEM electron channeling contrast techniques. For both techniques, similar conclusions were made in terms of large-scale distribution of martensite lath and plates and nanoscale observation of nanotwins and dislocation structures. These observations were completed with electron backscatter diffraction to assess the martensite size distribution and the retained austenite area fraction. Precipitation was characterized using secondary imaging in the SEM, and a deep learning method was used for image segmentation. In this way, carbide size, shape, and distribution were quantitatively measured down to a few nanometers and compared well with the TEM-based measurements. These encouraging results are intended to help the material science community develop characterization techniques at lower cost and higher statistical significance. Lath ; Materials science ; Scanning electron microscope ; Transmission electron microscopy ; Microstructure ; High-resolution transmission electron microscopy ; Characterization (materials science) ; Martensite ; Electron backscatter diffraction ; Austenite ; Energy filtered transmission electron microscopy ; Dislocation ; Composite material ; Scanning transmission electron microscopy ; Nanotechnology Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Non-Destructive Testing Techniques
Authors: Yaling Yan | Lulu Zhang | Shuhua Li | Hong Liang | Zhiwei Qiao
Source: Computational Materials Science (article, 2021-03-23)
Field: Chemistry — Inorganic Chemistry

Adsorption behavior of metal-organic frameworks: From single simulation, high-throughput computational screening to machine learning Adsorption ; Commercialization ; Throughput ; Field (mathematics) ; Nanotechnology ; Porosity ; Computational simulation ; Materials science ; Computer science ; Porous medium ; Process engineering ; Metal-organic framework ; Biochemical engineering ; Chemistry ; Engineering ; Computational science ; Organic chemistry ; Telecommunications ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Enhanced Oil Recovery Techniques
Authors: Syed Sohaib Ali Shah | Ayesha Zeb | Waqar S. Qureshi | Aman Ullah Malik | Mohsin Islam Tiwana | Kerry B. Walsh | Muhammad Amin | Waleed Alasmary | Eisa Alanazi
Source: Infrared Physics & Technology (article, 2021-03-29)
Field: Chemistry — Analytical Chemistry

Mango maturity classification instead of maturity index estimation: A new approach towards handheld NIR spectroscopy Maturity (psychological) ; Index (typography) ; Mobile device ; Computer science ; Estimation ; Spectroscopy ; Materials science ; Statistics ; Remote sensing ; Environmental science ; Mathematics ; Geography ; Physics ; World Wide Web ; Engineering ; Psychology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Kai Cui | Souryadeep Bhattacharyya | Sankar Nair | J. R. Schmidt
Source: Journal of the American Chemical Society (article, 2021-10-22)
Field: Chemistry — Inorganic Chemistry

Origins of Acid-Gas Stability Behavior in Zeolitic Imidazolate Frameworks: The Unique High Stability of ZIF-71 Zeolitic imidazolate frameworks (ZIFs) are promising materials for industrial process separations, but recent literature reports have highlighted their vulnerability to acid gases (e.g., SO<sub>2</sub>, CO<sub>2</sub>, NO<sub>2</sub>, H<sub>2</sub>S), often present in practical applications. While previous work has documented the widely varying stability behavior of many ZIFs under varying (humid and dry) acid gas environments, efforts to explain or correlate these experimental observations via empirical descriptors have not succeeded. A key observation is that ZIF-71 (RHO topology) is an extraordinarily stable ZIF material, retaining both structure and porosity under prolonged humid SO<sub>2</sub> exposure whereas many other well-known ZIFs with different linkers and topologies (such as ZIF-8) were shown to degrade. Through a combination of hybrid quantum mechanics/molecular mechanics (QM/MM) based methods and statistical mechanical models, we successfully explain this important experimental observation via atomistic investigations of the reaction mechanism. Our holistic approach reveals an ∼9 times lower average defect formation rate in ZIF-71 RHO compared to ZIF-8 SOD, leading to the conclusion that the observed experimental stability of this material rises from kinetic effects. Moreover, our analysis reveals that differing stability of the two materials is determined by the <i>distributions</i> of acid gas molecules, which is difficult to capture using empirical descriptors. Our results suggest wider applicability of the present approach, toward identifying tuned functional groups and topologies that move the acid gas distributions away from more reactive sites and thus allow enhanced kinetic stability. Zeolitic imidazolate framework ; Chemistry ; Stability (learning theory) ; Porosity ; Network topology ; Degradation (telecommunications) ; Molecular dynamics ; Kinetic energy ; Molecule ; Topology (electrical circuits) ; Metal-organic framework ; Density functional theory ; Biological system ; Nanotechnology ; Chemical physics ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Materials science ; Computer science ; Machine learning ; Physics ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Covalent Organic Framework Applications
Authors: Jiawen Wang | Faqian He | Xiaoyu Yang
Source: Nature Communications (article, 2021-11-18)
Field: Chemistry — Organic Chemistry

Asymmetric construction of acyclic quaternary stereocenters via direct enantioselective additions of α-alkynyl ketones to allenamides Acyclic quaternary stereocenters are widely present in a series of biologically active natural products and pharmaceuticals. However, development of highly efficient asymmetric catalytic methods for the construction of these privileged motifs represents a longstanding challenge in organic synthesis. Herein, an efficient chiral phosphoric acid catalyzed direct asymmetric addition of α-alkynyl acyclic ketones with allenamides has been developed, furnishing the acyclic all-carbon quaternary stereocenters with excellent regioselectivities and high enantioselectivities. Extensive and detailed experimental mechanistic studies were performed to investigate the mechanism of this reaction. Despite a novel covalent allyl phosphate intermediate was found in these reactions, further studies indicated that a S<sub>N</sub>2-type mechanism with the ketone nucleophiles is not very possible. Instead, a more plausible mechanism involving the elimination of the allyl phosphate to give the α,β-unsaturated iminium intermediate, which underwent the asymmetric conjugate addition with the enol form of ketone nucleophiles under chiral anion catalysis, was proposed. In virtue of the fruitful functional groups bearing in the chiral products, the diverse derivatizations of the chiral products provided access to a wide array of chiral scaffolds with quaternary stereocenters. Stereocenter ; Enantioselective synthesis ; Nucleophile ; Iminium ; Chemistry ; Organocatalysis ; Combinatorial chemistry ; Ketone ; Michael reaction ; Stereochemistry ; Catalysis ; Organic chemistry Asymmetric Synthesis and Catalysis ; Synthetic Organic Chemistry Methods ; Synthesis and Catalytic Reactions
Authors: Koji Nakamae
Source: Measurement Science and Technology (article, 2021-01-08)
Field: Materials Science — Surfaces, Coatings and Films

Electron microscopy in semiconductor inspection Abstract Currently, semiconductor devices are manufactured in a technology node of several nanometers. Electron microscopy is mainly used in semiconductor inspection in manufacturing stages since accelerated electrons have wavelengths of nanometers or less, and a high spatial resolution can be expected. Among various electron microscopes since the scanning electron microscope (SEM) can observe the sample as it is without processing the sample, the SEM-based inspection instrument is mainly used at each stage of manufacturing the semiconductor device. The paper presents a review of SEM-based electron microscopy in semiconductor inspection. First, an overview of electron microscopy is described to understand the electron-sample interaction, the characteristics of electrons emitted from an irradiated specimen, charging, noise, and so on. Next, application areas such as mask inspection are introduced. Finally, future challenges are discussed. Semiconductor ; Scanning electron microscope ; Electron microscope ; Materials science ; Electron ; Nanometre ; Optics ; Microscopy ; Microscope ; Conventional transmission electron microscope ; Electron beam-induced deposition ; Semiconductor device ; Optoelectronics ; Resolution (logic) ; Nanotechnology ; Scanning transmission electron microscopy ; Computer science ; Physics ; Artificial intelligence ; Composite material Electron and X-Ray Spectroscopy Techniques ; Advancements in Photolithography Techniques ; Integrated Circuits and Semiconductor Failure Analysis