jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yaolong Zhang | Sheng Ye | Jinxiao Zhang | Ce Hu | Jun Jiang | Bin Jiang
Source: The Journal of Physical Chemistry B (article, 2020-07-30)
Field: Materials Science — Materials Chemistry

Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties Machine learning has revolutionized the high-dimensional representations for molecular properties such as potential energy. However, there are scarce machine learning models targeting tensorial properties, which are rotationally covariant. Here, we propose tensorial neural network (NN) models to learn both tensorial response and transition properties in which atomic coordinate vectors are multiplied with scalar NN outputs or their derivatives to preserve the rotationally covariant symmetry. This strategy keeps structural descriptors symmetry invariant so that the resulting tensorial NN models are as efficient as their scalar counterparts. We validate the performance and universality of this approach by learning response properties of water oligomers and liquid water and transition dipole moment of a model structural unit of proteins. Machine-learned tensorial models have enabled efficient simulations of vibrational spectra of liquid water and ultraviolet spectra of realistic proteins, promising feasible and accurate spectroscopic simulations for biomolecules and materials. Covariant transformation ; Dipole ; Scalar (mathematics) ; Artificial neural network ; Invariant (physics) ; Universality (dynamical systems) ; Spectral line ; Symmetry (geometry) ; Statistical physics ; Physics ; Biological system ; Computer science ; Artificial intelligence ; Quantum mechanics ; Mathematics ; Geometry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Ye Sheng | Yasong Wu | Jiong Yang | Wencong Lu | P. Villars | Wenqing Zhang
Source: npj Computational Materials (article, 2020-11-10)
Field: Materials Science — Materials Chemistry

Active learning for the power factor prediction in diamond-like thermoelectric materials Abstract The Materials Genome Initiative requires the crossing of material calculations, machine learning, and experiments to accelerate the material development process. In recent years, data-based methods have been applied to the thermoelectric field, mostly on the transport properties. In this work, we combined data-driven machine learning and first-principles automated calculations into an active learning loop, in order to predict the p-type power factors (PFs) of diamond-like pnictides and chalcogenides. Our active learning loop contains two procedures (1) based on a high-throughput theoretical database, machine learning methods are employed to select potential candidates and (2) computational verification is applied to these candidates about their transport properties. The verification data will be added into the database to improve the extrapolation abilities of the machine learning models. Different strategies of selecting candidates have been tested, finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy (the Pearson R = 0.95 on untrained systems). Based on the prediction from the machine learning models, binary pnictides, vacancy, and small atom-containing chalcogenides are predicted to have large PFs. The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds. Extrapolation ; Gradient boosting ; Machine learning ; Computer science ; Artificial intelligence ; Boosting (machine learning) ; Diamond ; Materials science ; Mathematics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Advanced Thermoelectric Materials and Devices
Authors: Murat Cihan Sorkun | Séverin Astruc | J. M. V. A. Koelman | Süleyman Er
Source: npj Computational Materials (article, 2020-07-24)
Field: Materials Science — Materials Chemistry

An artificial intelligence-aided virtual screening recipe for two-dimensional materials discovery Abstract In recent years, artificial intelligence (AI) methods have prominently proven their use in solving complex problems. Across science and engineering disciplines, the data-driven approach has become the fourth and newest paradigm. It is the burgeoning of findable, accessible, interoperable, and reusable (FAIR) data generated by the first three paradigms of experiment, theory, and simulation that has enabled the application of AI methods for the scientific discovery and engineering of compounds and materials. Here, we introduce a recipe for a data-driven strategy to speed up the virtual screening of two-dimensional (2D) materials and to accelerate the discovery of new candidates with targeted physical and chemical properties. As a proof of concept, we generate new 2D candidate materials covering an extremely large compositional space, downselect 316,505 likely stable 2D materials, and predict the key physical properties of these new 2D candidates. Finally, we hone in on the most propitious candidates of functional 2D materials for energy conversion and storage. Recipe ; Computer science ; Interoperability ; Key (lock) ; Data science ; Space (punctuation) ; Artificial intelligence ; Nanotechnology ; World Wide Web ; Materials science ; Chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; 2D Materials and Applications
Authors: Han‐Wei Chu | Binesh Unnikrishnan | Anisha Anand | Yang‐Wei Lin | Chih‐Ching Huang
Source: Journal of Food and Drug Analysis (review, 2020-12-02)
Field: Materials Science — Materials Chemistry

Carbon quantum dots for the detection of antibiotics and pesticides Carbon quantum dots (CQDs) are novel nanomaterials with interesting physical and chemical properties, which are intensely studied only in the last decade. Unique properties, such as its inherent fluorescent property, high resistance to photobleaching, high surface area, ease of synthesis, flexible choice of precursor, and surface tunability enable CQDs for promising application in biosensing. Therefore, it is highly useful in clinical, forensic, medical, food and drug analyses, disease diagnosis, and various other fields of biosensing. In addition, their fluorescence properties are tunable by the interaction with certain molecules via different mechanisms, which enables their application for sensing of those molecules, such as pesticides and antibiotics. The detection of antibiotics and pesticides is especially important as they are commonly used in both the medical and agricultural fields and can affect both humans and their environment. However, these molecules do not have a specific recognition element unlike for antibodies, proteins, enzymes, and other biomarkers. Thus, the fluorescence quenching mechanism alone cannot be applied as a sensing mechanism for the CQDs-based sensing of pesticides and antibiotics. In this review, we discuss the application of various CQDs, in the detection of antibiotics, pesticides (herbicide, fungicide, insecticide), and other medicinal drugs through various detection strategies and their current limitations. Biosensor ; Nanotechnology ; Pesticide ; Antibiotics ; Photobleaching ; Carbon quantum dots ; Quantum dot ; Fluorescence ; Chemistry ; Biomolecule ; Materials science ; Environmental chemistry ; Biology ; Biochemistry Carbon and Quantum Dots Applications ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Paola Peluso | Victor Mamane | Roberto Dallocchio | Alessandro Dessì | Sergio Cossu
Source: Journal of Chromatography A (review, 2020-05-19)
Field: Chemistry — Spectroscopy

Noncovalent interactions in high-performance liquid chromatography enantioseparations on polysaccharide-based chiral selectors Chemistry ; Non-covalent interactions ; Intermolecular force ; Hydrogen bond ; Stacking ; Enantiomer ; Computational chemistry ; Steric effects ; van der Waals force ; Supramolecular chemistry ; Molecule ; Organic chemistry Analytical Chemistry and Chromatography ; Molecular spectroscopy and chirality ; Crystallization and Solubility Studies
Authors: Jean‐Michel Roger | Alessandra Biancolillo | Federico Marini
Source: Chemometrics and Intelligent Laboratory Systems (article, 2020-02-18)
Field: Chemistry — Analytical Chemistry

Sequential preprocessing through ORThogonalization (SPORT) and its application to near infrared spectroscopy Orthogonalization ; Preprocessor ; Partial least squares regression ; Boosting (machine learning) ; Calibration ; Computer science ; Artificial intelligence ; Noise (video) ; Pattern recognition (psychology) ; Data mining ; Algorithm ; Machine learning ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Zhaodong Li | Rubén O. Torres‐Ochoa | Qian Wang | Jieping Zhu
Source: Nature Communications (article, 2020-01-21)
Field: Chemistry — Organic Chemistry

Functionalization of remote C(sp3)-H bonds enabled by copper-catalyzed coupling of O-acyloximes with terminal alkynes Transition metal catalyzed Sonogashira cross-coupling of terminal alkynes with aryl(vinyl) (pseudo)halides has been successfully extended to alkyl halides for the synthesis of functionalized internal alkynes. The direct alkynylation of remote unfunctionalized sp<sup>3</sup> carbon by terminal alkynes remains difficult to realize. We report herein an approach to this synthetic challenge by developing two catalytic remote sp<sup>3</sup> carbon alkynylation protocols. In the presence of a catalytic amount of Cu(I) salt and a tridentate ligand (tBu<sub>3</sub>-terpyridine), O-acyloximes derived from cycloalkanones and acyclic ketones are efficiently coupled with terminal alkynes to afford a variety of γ- and δ-alkynyl nitriles and γ-alkynyl ketones, respectively. These reactions proceed through a domino sequence involving copper-catalyzed reductive generation of iminyl radical followed by radical translocation via either β-scission or 1,5-hydrogen atom transfer (1,5-HAT) and copper-catalyzed alkynylation of the resulting translocated carbon radicals. The protocols are applicable to complex natural products. Catalysis ; Sonogashira coupling ; Chemistry ; Alkynylation ; Combinatorial chemistry ; Alkyne ; Aryl ; Copper ; Ligand (biochemistry) ; Alkyl ; Polymer chemistry ; Organic chemistry ; Palladium Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; Sulfur-Based Synthesis Techniques
Authors: Dzung T. Nguyen | Matthias Freitag | Christian Gutheil | Kai Sotthewes | Bonnie J. Tyler | Marcus Böckmann | Mowpriya Das | Friederike Schlüter | Nikos L. Doltsinis | Heinrich F. Arlinghaus | Bart Jan Ravoo | Frank Glorius
Source: Angewandte Chemie International Edition (article, 2020-04-09)
Field: Materials Science — Materials Chemistry

An Arylazopyrazole‐Based N‐Heterocyclic Carbene as a Photoswitch on Gold Surfaces: Light‐Switchable Wettability, Work Function, and Conductance A novel photoresponsive and fully conjugated N-heterocyclic carbene (NHC) has been synthesized that combines the excellent photophysical properties of arylazopyrazoles (AAPs) with an NHC that acts as a robust surface anchor (AAP-BIMe). The formation of self-assembled monolayers (SAMs) on gold was proven by ToF-SIMS and XPS, and the organic film displayed a very high stability at elevated temperatures. This stability was also reflected in a high desorption energy, which was determined by temperature-programmed SIMS measurements. E-/Z-AAP-BIMe@Au photoisomerization resulted in reversible alterations of the surface energy (i.e. wettability), the surface potential (i.e. work function), and the conductance (i.e. resistance). The effects could be explained by the difference in the dipole moment of the isomers. Furthermore, sequential application of a dummy ligand by microcontact printing and subsequent backfilling with AAP-BIMe allowed its patterning on gold. To the best of our knowledge, this is the first example of a photoswitchable NHC on a gold surface. These properties of AAP-BIMe@Au illustrate its suitability as a molecular switch for electronic devices. Photoswitch ; Wetting ; Photoisomerization ; Conductance ; Carbene ; Work function ; Contact angle ; Materials science ; Surface energy ; Monolayer ; Ligand (biochemistry) ; Dipole ; Chemistry ; Conjugated system ; Photochemistry ; Nanotechnology ; Chemical engineering ; Physical chemistry ; Organic chemistry ; Layer (electronics) ; Polymer ; Isomerization Photochromic and Fluorescence Chemistry ; Organic Electronics and Photovoltaics ; Organic Light-Emitting Diodes Research
Authors: Laia Josa‐Culleré | Amadeu Llebaria
Source: ChemPhotoChem (article, 2020-12-04)
Field: Materials Science — Materials Chemistry

In the Search for Photocages Cleavable with Visible Light: An Overview of Recent Advances and Chemical Strategies Abstract Photopharmacological tools enable the precise spatiotemporal control of small molecule drugs. Amongst them, caged compounds incorporate a photolabile moiety which is released under illumination, thus liberating the active molecule. Caging groups have long been known and many chemical scaffolds have already been used in different applications. However, most of the initial examples are cleaved with UV light, which suffers from low tissue permeability and cell damage. Recently, caging groups that are released under visible light have been reported, which expand their utility. In this review, we outline the chemical strategies that have been used to increase the absorption wavelengths; we compare their photophysical properties, discuss their synthetic accessibility, and exemplify some of their biological applications. Moiety ; Cell permeability ; Nanotechnology ; Chemistry ; Molecule ; Absorption (acoustics) ; Combinatorial chemistry ; Computer science ; Materials science ; Organic chemistry ; Biochemistry Photochromic and Fluorescence Chemistry ; Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: Yixiao Chen | Linfeng Zhang | Handong Wang | E Weinan
Source: Journal of Chemical Theory and Computation (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory We propose a general machine learning-based framework for building an accurate and widely applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we develop a way of training self-consistent models that are capable of taking large datasets from different systems and different kinds of labels. We demonstrate that the functional that results from this training procedure gives chemically accurate predictions on energy, force, dipole, and electron density for a large class of molecules. It can be continuously improved when more and more data are available. Density functional theory ; Computer science ; Time-dependent density functional theory ; Class (philosophy) ; Energy (signal processing) ; Dipole ; Hybrid functional ; Artificial intelligence ; Functional theory ; Orbital-free density functional theory ; Machine learning ; Theoretical computer science ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Sergei Manzhos
Source: Machine Learning Science and Technology (article, 2020-03-01)
Field: Materials Science — Materials Chemistry

Machine learning for the solution of the Schrödinger equation Abstract Machine learning (ML) methods have recently been increasingly widely used in quantum chemistry. While ML methods are now accepted as high accuracy approaches to construct interatomic potentials for applications, the use of ML to solve the Schrödinger equation, either vibrational or electronic, while not new, is only now making significant headway towards applications. We survey recent uses of ML techniques to solve the Schrödinger equation, including the vibrational Schrödinger equation, the electronic Schrödinger equation and the related problems of constructing functionals for density functional theory (DFT) as well as potentials which enter semi-empirical approximations to DFT. We highlight similarities and differences and specific difficulties that ML faces in these applications and possibilities for cross-fertilization of ideas. Schrödinger equation ; Construct (python library) ; Headway ; Density functional theory ; Structural equation modeling ; Chemical equation ; Computer science ; Applied mathematics ; Mathematics ; Statistical physics ; Physics ; Quantum mechanics ; Chemistry ; Machine learning ; Simulation ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Siddharth Deshpande | Tristan Maxson | Jeffrey Greeley
Source: npj Computational Materials (article, 2020-06-18)
Field: Materials Science — Materials Chemistry

Graph theory approach to determine configurations of multidentate and high coverage adsorbates for heterogeneous catalysis Abstract Heterogeneous catalysts constitute a crucial component of many industrial processes, and to gain an understanding of the atomic-scale features of such catalysts, ab initio density functional theory is widely employed. Recently, growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates, which bind to the surface through multiple atoms. Describing all possible adsorbate configurations for such systems, however, is often not possible based on chemical intuition alone. To systematically treat such complexities, we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations. These representations, when combined with workflows such as evolutionary algorithms, can systematically generate high coverage adsorbate models and classify unique minimum energy multidentate adsorbate configurations for surfaces of low symmetry, including multi-elemental alloy surfaces, steps, and kinks. Two case studies are presented which demonstrate these capabilities; first, an analysis of a coverage-dependent phase diagram of absorbate NO on the Pt 3 Sn(111) terrace surface, and second, an investigation of adsorption energies, together with identifying unique minimum energy configurations, for the reaction intermediate propyne (CHCCH 3 *) adsorbed on a PdIn(021) step surface. The evolutionary algorithm approach reproduces high coverage configurations of NO on Pt 3 Sn(111) using only 15% of the number of simulations required for a brute force approach. Furthermore, the screening of potentially hundreds of multidentate adsorbates is shown to be possible without human intervention. The strategy presented is quite general and can be applied to a spectrum of complex atomic systems. Density functional theory ; Ab initio ; Graph theory ; Chemical physics ; Denticity ; Computer science ; Catalysis ; Heterogeneous catalysis ; Materials science ; Statistical physics ; Computational chemistry ; Chemistry ; Physics ; Mathematics ; Quantum mechanics ; Combinatorics ; Metal Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Abdullahi Abbas Adam | John Ojur Dennis | Yas Al‐Hadeethi | E.M. Mkawi | Bashir Abubakar Abdulkadir | Fahad Usman | Yarima Mudassir Hassan | Ismael A. Wadi | Mustapha Sani
Source: Polymers (review, 2020-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

State of the Art and New Directions on Electrospun Lignin/Cellulose Nanofibers for Supercapacitor Application: A Systematic Literature Review Supercapacitors are energy storage devices with high power density, rapid charge/discharge rate, and excellent cycle stability. Carbon-based supercapacitors are increasingly attracting attention because of their large surface area and high porosity. Carbon-based materials research has been recently centered on biomass-based materials due to the rising need to maintain a sustainable environment. Cellulose and lignin constitute the major components of lignocellulose biomass. Since they are renewable, sustainable, and readily accessible, lignin and cellulose-based supercapacitors are economically viable and environmentally friendly. This review aims to systematically analyze published research findings on electrospun lignin, cellulose, and lignin/cellulose nanofibers for use as supercapacitor electrode materials. A rigorous scientific approach was employed to screen the eligibility of relevant articles to be included in this study. The research questions and the inclusion criteria were clearly defined. The included articles were used to draw up the research framework and develop coherent taxonomy of literature. Taxonomy of research literature generated from the included articles was classified into review papers, electrospun lignin, cellulose, and lignin/cellulose nanofibers for use as supercapacitor electrode materials. Furthermore, challenges, recommendations, and research directions for future studies were equally discussed extensively. Before this study, no review on electrospun lignin/cellulose nanofiber-based supercapacitors has been reported. Thus, this systematic review will provide a reference for other researchers interested in developing biomass-based supercapacitors as an alternative to conventional supercapacitors based on petroleum products. Supercapacitor ; Lignin ; Cellulose ; Materials science ; Nanofiber ; Biomass (ecology) ; Nanotechnology ; Pulp and paper industry ; Chemical engineering ; Chemistry ; Organic chemistry ; Electrode ; Engineering ; Capacitance ; Ecology Supercapacitor Materials and Fabrication ; Electrospun Nanofibers in Biomedical Applications ; Lignin and Wood Chemistry
Authors: J. Byggmästar | K. Nordlund | F. Djurabekova
Source: Physical Review Materials (article, 2020-09-28)
Field: Materials Science — Materials Chemistry

Gaussian approximation potentials for body-centered-cubic transition metals We develop a set of machine-learning interatomic potentials for elemental V, Nb, Mo, Ta, and W using the Gaussian approximation potential framework. The potentials show good accuracy and transferability for elastic, thermal, liquid, defect, and surface properties. All potentials are augmented with accurate repulsive potentials, making them applicable to radiation damage simulations involving high-energy collisions. We study melting and liquid properties in detail and use the potentials to provide melting curves up to 400 GPa for all five elements. Transferability ; Gaussian ; Transition metal ; Interatomic potential ; Coherent potential approximation ; Melting temperature ; Set (abstract data type) Machine Learning in Materials Science ; Nuclear Materials and Properties ; Fusion materials and technologies