jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tobias Morawietz | Nongnuch Artrith
Source: Journal of Computer-Aided Molecular Design (article, 2020-10-09)
Field: Materials Science — Materials Chemistry

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future. Computer science ; Molecular dynamics ; Quantum ; Biochemical engineering ; Nanotechnology ; Materials science ; Physics ; Chemistry ; Engineering ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: S.H.E.J. Gabriëls | Puneet Mishra | Manon Mensink | Patrick Spoelstra | Ernst Woltering
Source: Postharvest Biology and Technology (article, 2020-04-22)
Field: Chemistry — Analytical Chemistry

Non-destructive measurement of internal browning in mangoes using visible and near-infrared spectroscopy supported by artificial neural network analysis Visible and near infrared spectroscopy (VNIRS) (400−1000 nm) is a key emerging non-destructive technique for fruit quality assessment. This, because it is a unique method which allows rapid access to fruit pigments and chemical properties linked to fruit quality. In the present work, VNIRS has been used to predict the internal browning in ‘Keitt’ mangoes halves. The reference analysis was performed by cutting individual mango into halves and quantifying the extent of internal browning with a standardized color imaging (CI) cabinet as a browning index (BI). The CI provided a value for the “browning index” for each mango reflecting the presence and severity of internal browning. The data modelling involved both regression and classification analysis. The regression was performed to link the VNIR spectra with the BI values obtained from the internal color analysis. The classification analysis was performed for binary classification of mango into healthy or brown. Two different analysis techniques i.e. artificial neural network (ANN) and partial least square (PLS) were utilized. The study shows that VNIRS combined with ANN can classify mangoes as healthy or having internal brown with an accuracy of over 80 %. A robust and reliable classification system can potentially improve quality decisions through the mango supply chain, thereby reducing post-harvest losses. Browning ; VNIR ; Artificial neural network ; Near-infrared spectroscopy ; Principal component analysis ; Artificial intelligence ; Pattern recognition (psychology) ; Food science ; Chemistry ; Computer science ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Leaf Properties and Growth Measurement
Authors: Yasser Vasseghian | Mohammed Berkani | Fares Almomani | Elena-Niculina Drăgoi
Source: Chemosphere (article, 2020-12-28)
Field: Chemistry — Electrochemistry

Data mining for pesticide decontamination using heterogeneous photocatalytic processes Support vector machine ; Human decontamination ; Pesticide ; Multilayer perceptron ; Artificial neural network ; Computer science ; Sensitivity (control systems) ; Process (computing) ; Bayesian network ; Artificial intelligence ; Machine learning ; Environmental science ; Biochemical engineering ; Data mining ; Engineering ; Waste management ; Ecology Electrochemical Analysis and Applications ; Water Quality Monitoring and Analysis ; Electrochemical sensors and biosensors
Authors: Krzysztof B. Beć | Justyna Grabska | Christian W. Huck
Source: Journal of Pharmaceutical and Biomedical Analysis (review, 2020-10-23)
Field: Chemistry — Analytical Chemistry

NIR spectroscopy of natural medicines supported by novel instrumentation and methods for data analysis and interpretation Near-infrared (NIR) spectroscopy is a powerful tool for qualitative and quantitative phytoanalysis. It is a rapid and high-throughput analytical method, with on-site capability, high chemical specificity, and no/minimal sample preparation. NIR spectroscopy is a powerful non-invasive and low-cost alternative with significant practical advantages compared to the conventional methods of analysis. These advantages are particularly exposed in the field of phytoanalysis. In contrast to synthetic medicines, natural products feature chemical diversity that can vary depending on the medicinal plant cultivation conditions, geographical origin or harvest time. The content of bioactive compounds and their derivatives, and thus, the quality parameters of the natural medicine need to be controlled with respect to a number of conditions. NIR spectroscopy has been proved to be particularly competitive in such difficult scenarios. In recent years, remarkable advances in the field of spectroscopic instrumentation and methods of analysis have appeared. Noteworthy was the appearance and dynamic continuing development of miniaturized, on-site capable NIR spectrometers. This was accompanied by application of new tools increasing the potential and reliability of NIR spectroscopy in phytoanalytical applications. The present review discussed the major principles of this technique and critically assesses its future application potential in phytoanalytical strategies. Major attention is given to the current development trends based on the most recent literature published in the field. Instrumentation (computer programming) ; Biochemical engineering ; Chemistry ; Near-infrared spectroscopy ; Spectroscopy ; Nanotechnology ; Reliability (semiconductor) ; Process engineering ; Computer science ; Engineering ; Materials science Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Hongyan Du | Junbo Gao | Gaoqi Weng | Junjie Ding | Xin Chai | Jinping Pang | Yu Kang | Dan Li | Dongsheng Cao | Tingjun Hou
Source: Nucleic Acids Research (article, 2020-09-25)
Field: Chemistry — Organic Chemistry

CovalentInDB: a comprehensive database facilitating the discovery of covalent inhibitors Inhibitors that form covalent bonds with their targets have traditionally been considered highly adventurous due to their potential off-target effects and toxicity concerns. However, with the clinical validation and approval of many covalent inhibitors during the past decade, design and discovery of novel covalent inhibitors have attracted increasing attention. A large amount of scattered experimental data for covalent inhibitors have been reported, but a resource by integrating the experimental information for covalent inhibitor discovery is still lacking. In this study, we presented Covalent Inhibitor Database (CovalentInDB), the largest online database that provides the structural information and experimental data for covalent inhibitors. CovalentInDB contains 4511 covalent inhibitors (including 68 approved drugs) with 57 different reactive warheads for 280 protein targets. The crystal structures of some of the proteins bound with a covalent inhibitor are provided to visualize the protein-ligand interactions around the binding site. Each covalent inhibitor is annotated with the structure, warhead, experimental bioactivity, physicochemical properties, etc. Moreover, CovalentInDB provides the covalent reaction mechanism and the corresponding experimental verification methods for each inhibitor towards its target. High-quality datasets are downloadable for users to evaluate and develop computational methods for covalent drug design. CovalentInDB is freely accessible at http://cadd.zju.edu.cn/cidb/. Covalent bond ; Drug discovery ; Covalent binding ; Combinatorial chemistry ; Chemistry ; Computational biology ; Biochemistry ; Biology ; Organic chemistry Click Chemistry and Applications ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Shunqi Lai | Ming Zhao | Jianwei Qiao | Мaxim S. Моlokeev | Zhiguo Xia
Source: The Journal of Physical Chemistry Letters (article, 2020-06-22)
Field: Materials Science — Materials Chemistry

Data-Driven Photoluminescence Tuning in Eu<sup>2+</sup>-Doped Phosphors Discovery of rare earth phosphors has generally relied on the chemical intuition and time-intensive trial-and-error synthesis; therefore, finding new materials assisted by data-driven computations is urgent. Herein, we utilize a regression model to predict the emission wavelengths of Eu<sup>2+</sup>-doped phosphors by revealing the relationships between the crystal structure and luminescence property. The emission wavelengths of [Rb<sub>(1-</sub><i><sub>x</sub></i><sub>)</sub>K<sub>(</sub><i><sub>x</sub></i><sub>)</sub>]<sub>3</sub>LuSi<sub>2</sub>O<sub>7</sub>:Eu<sup>2+</sup> (0 ≤ <i>x</i> ≤ 1) phosphors, as examples for the data-driven photoluminescence tuning, are successfully predicted on the basis of the existing data of only eight systems, also consistent with the experimental results. These phosphors can be excited by blue light and exhibit broad-band red and near-infrared emission ranging from 619 to 737 nm. These findings in Eu<sup>2+</sup>-doped silicate phosphors indicate that data-driven computations through the regression mode would have bright application in discovering novel phosphors with a target emission wavelengths. Phosphor ; Photoluminescence ; Doping ; Materials science ; Excited state ; Wavelength ; Luminescence ; Optoelectronics ; Analytical Chemistry (journal) ; Mineralogy ; Optics ; Chemistry ; Physics ; Atomic physics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Perovskite Materials and Applications
Authors: Klára Ščupáková | Frédéric Dewez | Axel Walch | Ron M. A. Heeren | Benjamin Balluff
Source: Angewandte Chemie International Edition (article, 2020-07-15)
Field: Chemistry — Spectroscopy

Morphometric Cell Classification for Single‐Cell MALDI‐Mass Spectrometry Imaging The large-scale and label-free molecular characterization of single cells in their natural tissue habitat remains a major challenge in molecular biology. We present a method that integrates morphometric image analysis to delineate and classify individual cells with their single-cell-specific molecular profiles. This approach provides a new means to study spatial biological processes such as cancer field effects and the relationship between morphometric and molecular features. Mass spectrometry imaging ; MALDI imaging ; Mass spectrometry ; Cell ; Chemistry ; Chromatography ; Matrix-assisted laser desorption/ionization ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Christopher Matthews | Romain Perriot | M. Cooper | Christopher R. Stanek | David A. Andersson
Source: Journal of Nuclear Materials (article, 2020-07-23)
Field: Materials Science — Materials Chemistry

Cluster dynamics simulation of xenon diffusion during irradiation in UO2 Xenon ; Diffusion ; Vacancy defect ; Thermal diffusivity ; Fission ; Cluster (spacecraft) ; Fission products ; Irradiation ; Chemistry ; Chemical physics ; Frenkel defect ; Uranium dioxide ; Materials science ; Radiochemistry ; Atomic physics ; Uranium ; Thermodynamics ; Nuclear physics ; Crystallography ; Neutron ; Physics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Andrej Turk | Gaurav R. Joshi | Marius Gintalas | M. Callisti | Pedro E.J. Rivera-Díaz-del-Castillo | E.I. Galindo-Nava
Source: Acta Materialia (article, 2020-05-15)
Field: Materials Science — Metals and Alloys

Quantification of hydrogen trapping in multiphase steels: Part I – Point traps in martensite We quantified systematically the H trap density in martensite resulting from the presence of dislocations, grain boundaries and retained austenite through a combination of detailed microstructural characterisation, H permeation, thermal desorption and diffusion modelling. This thorough analysis allowed for the first time to deconvolve key microstructural constituents affecting H diffusion in multi-trap martensite. Three microstructures were investigated – as-quenched, tempered at 300 °C and tempered at 450 °C. The first two microstructures had identical dislocation densities and grain size, while the as-quenched one also contained 3.5 vol.% of retained austenite. The two tempered microstructures showed a large difference in dislocation density with few other microstructural differences. The as-quenched microstructure exhibited over an order of magnitude lower H diffusivity and increased H trapping due to retained austenite, while the tempered samples exhibited very similar diffusivities, indicating that dislocations have a limited effect on H trapping. Trap density scaling laws were successfully identified and validated through diffusion simulations and experiments. It was also shown that in martensite and heavily deformed ferrite, where the average grain size is small, grain boundaries are more effective trapping sites than dislocations. Our results also show that retained austenite cannot be effectively modelled as a point trap under the local equilibrium assumption, which is frequently used to model its effect on H diffusion, and that bulk trapping must be considered at least in two dimensions, which is addressed in part II of this series. Materials science ; Martensite ; Austenite ; Dislocation ; Microstructure ; Grain boundary ; Grain size ; Trapping ; Thermal diffusivity ; Diffusion ; Metallurgy ; Thermodynamics ; Composite material ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Corrosion Behavior and Inhibition
Authors: Tahereh Nematiaram | Daniele Padula | Alessandro Landi | Alessandro Troisi
Source: Advanced Functional Materials (article, 2020-06-02)
Field: Materials Science — Materials Chemistry

On the Largest Possible Mobility of Molecular Semiconductors and How to Achieve It Abstract A large database of known molecular semiconductors is used to define a plausible physical limit to the charge carrier mobility achievable within this materials class. From a detailed study of the desirable properties in a large dataset, it is possible to establish whether such properties can be optimized independently and what would be a reasonably achievable optimum for each of them. All relevant parameters are computed from a set of almost five thousand known molecular semiconductors, finding that the best known materials are not ideal with respect to all properties. These parameters in decreasing order of importance are the molecular area, the nonlocal electron–phonon coupling, the 2D nature of transport, the local electron–phonon coupling, and the highest transfer integral. It is also found that the key properties related to the charge transport are either uncorrelated or “constructively” correlated (i.e., they improve together) concluding that a tenfold increase in mobility is within reach in a statistical sense, on the basis of the available data. It is demonstrated that high throughput screenings, when coupled with physical models of transport produce both specific target materials and a more general physical understanding of the materials space. Semiconductor ; Materials science ; Ideal (ethics) ; Coupling (piping) ; Electron mobility ; Charge (physics) ; Basis (linear algebra) ; Phonon ; Electron ; Statistical physics ; Chemical physics ; Uncorrelated ; Limit (mathematics) ; Space (punctuation) ; Electron transport chain ; Condensed matter physics ; Computer science ; Quantum mechanics ; Physics ; Optoelectronics ; Chemistry ; Statistics ; Mathematics Machine Learning in Materials Science ; Organic and Molecular Conductors Research ; Organic Electronics and Photovoltaics
Authors: Jacek K. Wychowaniec | Ronak Patel | James Leach | Rachel T. Mathomes | Vikesh Chhabria | Yogita Patil‐Sen | Araida Hidalgo‐Bastida | Robert T. Forbes | Joseph M. Hayes | Mohamed A. Elsawy
Source: Biomacromolecules (article, 2020-05-13)
Field: Materials Science — Biomaterials

Aromatic Stacking Facilitated Self-Assembly of Ultrashort Ionic Complementary Peptide Sequence: β-Sheet Nanofibers with Remarkable Gelation and Interfacial Properties Understanding peptide self-assembly mechanisms and stability of the formed assemblies is crucial for the development of functional nanomaterials. Herein, we have adopted a rational design approach to demonstrate how a minimal structural modification to a nonassembling ultrashort ionic self-complementary tetrapeptide <i>F</i>E<i>F</i>K (Phe4) remarkably enhanced the stability of self-assembly into β-sheet nanofibers and induced hydrogelation. This was achieved by replacing flexible phenylalanine residue (<i>F</i>) by the rigid phenylglycine (<i>Phg</i>), resulting in a constrained analogue <i>Phg</i>E<i>Phg</i>K (Phg4), which positioned aromatic rings in an orientation favorable for aromatic stacking. Phg4 self-assembly into stable β-sheet ladders was facilitated by π-staking of aromatic side chains alongside hydrogen bonding between backbone amides along the nanofiber axis. The contribution of these noncovalent interactions in stabilizing self-assembly was predicted by in silico modeling using molecular dynamics simulations and semiempirical quantum mechanics calculations. In aqueous medium, Phg4 β-sheet nanofibers entangled at a critical gelation concentration ≥20 mg/mL forming a network of nanofibrous hydrogels. Phg4 also demonstrated a unique surface activity in the presence of immiscible oils and was superior to commercial emulsifiers in stabilizing oil-in-water (O/W) emulsions. This was attributed to interfacial adsorption of amphiphilic nanofibrils forming nanofibrilized microspheres. To our knowledge, Phg4 is the shortest ionic self-complementary peptide rationally designed to self-assemble into stable β-sheet nanofibers capable of gelation and emulsification. Our results suggest that ultrashort ionic-complementary constrained peptides or UICPs have significant potential for the development of cost-effective, sustainable, and multifunctional soft bionanomaterials. Stacking ; Self-assembly ; Nanofiber ; Ionic bonding ; Amphiphile ; Beta sheet ; Hydrogen bond ; Chemistry ; Molecular dynamics ; Aqueous solution ; Self-healing hydrogels ; Micelle ; Chemical engineering ; Ionic liquid ; Nanomaterials ; Peptide ; Materials science ; Nanotechnology ; Organic chemistry ; Computational chemistry ; Polymer ; Molecule ; Copolymer ; Ion Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Chemical Synthesis and Analysis
Authors: Filip Szczypiński | Steven P. Bennett | Kim E. Jelfs
Source: Chemical Science (review, 2020-12-10)
Field: Materials Science — Materials Chemistry

Can we predict materials that can be synthesised? The discovery of materials is an important element in the development of new technologies and abilities that can help humanity tackle many challenges. Materials discovery is frustratingly slow, with the large time and resource cost often providing only small gains in property performance. Furthermore, researchers are unwilling to take large risks that they will only know the outcome of months or years later. Computation is playing an increasing role in allowing rapid screening of large numbers of materials from vast search space to identify promising candidates for laboratory synthesis and testing. However, there is a problem, in that many materials computationally predicted to have encouraging properties cannot be readily realised in the lab. This minireview looks at how we can tackle the problem of confirming that hypothetical materials are synthetically realisable, through consideration of all the stages of the materials discovery process, from obtaining the components, reacting them to a material in the correct structure, through to processing into a desired form. In an ideal world, a material prediction would come with an associated 'recipe' for the successful laboratory preparation of the material. We discuss the opportunity to thus prevent wasted effort in experimental discovery programmes, including those using automation, to accelerate the discovery of novel materials. Computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Jin Soo Lim | Jonathan Vandermause | M. A. Van Spronsen | Albert Musaelian | Yu Xie | Lixin Sun | C. R. O'Connor | Tobias Egle | Nicola Molinari | Jacob Florian | Kaining Duanmu | R. J. Madix | Philippe Sautet | C. M. Friend | Boris Kozinsky
Source: Journal of the American Chemical Society (article, 2020-08-14)
Field: Materials Science — Materials Chemistry

Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics The restructuring of interfaces plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different compositions and morphologies at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of long-time scale restructuring of Pd deposited on Ag using microscopy, spectroscopy, and novel simulation methods. By developing and performing accelerated machine-learning molecular dynamics followed by an automated analysis method, we discover and characterize previously unidentified surface restructuring mechanisms in an unbiased fashion, including Pd-Ag place exchange and Ag pop-out as well as step ascent and descent. Remarkably, layer-by-layer dissolution of Pd into Ag is always preceded by an encapsulation of Pd islands by Ag, resulting in a significant migration of Ag out of the surface and a formation of extensive vacancy pits within a period of microseconds. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. Our approach is broadly applicable to complex multimetallic systems and enables the previously intractable mechanistic investigation of restructuring dynamics at atomic resolution. Bimetallic strip ; Metastability ; Chemistry ; Molecular dynamics ; Chemical physics ; Restructuring ; Dissolution ; Catalysis ; Nanotechnology ; Physical chemistry ; Computational chemistry ; Materials science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; nanoparticles nucleation surface interactions