jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Carsten Schlüsener | Mergime Xhinovci | Sebastian‐Johannes Ernst | Alexa Schmitz | Niels Tannert | Christoph Janiak
Source: Chemistry of Materials (article, 2019-05-08)
Field: Chemistry — Inorganic Chemistry

Solid-Solution Mixed-Linker Synthesis of Isoreticular Al-Based MOFs for an Easy Hydrophilicity Tuning in Water-Sorption Heat Transformations The solid-solution mixed-linker approach, where a linker is partially replaced by a similar one under retention of the isoreticular metal–organic framework (MOF) structure, offers an easy and inexpensive way to fine-tune MOF properties to design tailored compounds. A total of 10 aluminum mixed-linker MOFs, [Al(OH)(X)a(Y)1–a] (X = IPA, isophthalate; Y = FDC, 2,5-furandicarboxylate) spanning between the isostructural MOFs CAU-10-H (a = 1) and MIL-160 (a = 0), were synthesized by employing different ratios of the aforementioned linkers. CAU-10-H and MIL-160 have been reported as highly promising materials for cycling water sorption for heat transformation applications. A detailed characterization with a focus on the changes in the sorption properties for water vapor showed that the hydrophilicity is readily and easily tuned through the mixed-linker approach between the limits of MIL-160 and CAU-10-H. An increasing fraction of IPA shifts the steep increase in the S-shaped water adsorption isotherm in small steps from p/p0 = ∼0.05 for MIL-160 to p/p0 = ∼0.18 for CAU-10-H. Higher coefficient of performance (COPH) values for the mixed-linker materials over MIL-160 illustrate the well-balanced hydrophobicity/hydrophilicity of the former under the exemplary calculation conditions. Linker ; Sorption ; Isostructural ; Adsorption ; Metal-organic framework ; Chemical engineering ; Materials science ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Phase Change Materials Research ; Thermal properties of materials
Authors: A.A. Richter | Christopher-Nils Mais | Laura Czech | Kyra Geyer | A. Hoeppner | Sander H. J. Smits | Tobias J. Erb | Gert Bange | Erhard Bremer
Source: Frontiers in Microbiology (article, 2019-12-10)
Field: Materials Science — Materials Chemistry

Biosynthesis of the Stress-Protectant and Chemical Chaperon Ectoine: Biochemistry of the Transaminase EctB Bacteria frequently adapt to high osmolarity surroundings through the accumulation of compatible solutes. Ectoine is a prominent member of these types of stress protectants and is produced via an evolutionarily conserved biosynthetic pathway beginning with the L-2,4-diaminobutyrate (DAB) transaminase (TA) EctB. Here, we studied EctB from the thermo-tolerant Gram-positive bacterium <i>Paenibacillus lautus</i> (<i>Pl</i>) and show that this tetrameric enzyme is highly tolerant to salt, pH, and temperature. During ectoine biosynthesis, EctB converts L-glutamate and L-aspartate-beta-semialdehyde into 2-oxoglutarate and DAB, but it also catalyzes the reverse reaction. Our analysis unravels that EctB enzymes are mechanistically identical to the PLP-dependent gamma-aminobutyrate TAs (GABA-TAs) and only differ with respect to substrate binding. Inspection of the genomic context of the <i>ectB</i> gene in <i>P. lautus</i> identifies an unusual arrangement of juxtapositioned genes for ectoine biosynthesis and import via an Ehu-type binding-protein-dependent ABC transporter. This operon-like structure suggests the operation of a highly coordinated system for ectoine synthesis and import to maintain physiologically adequate cellular ectoine pools under osmotic stress conditions in a resource-efficient manner. Taken together, our study provides an in-depth mechanistic and physiological description of EctB, the first enzyme of the ectoine biosynthetic pathway. Ectoine ; Biosynthesis ; Biochemistry ; Osmoprotectant ; Operon ; Enzyme ; Osmotic shock ; Biology ; Bacteria ; Context (archaeology) ; Chemistry ; Gene ; Proline ; Amino acid ; Escherichia coli ; Genetics Enzyme Structure and Function ; Protein Structure and Dynamics ; Bacterial Genetics and Biotechnology
Authors: Hyeoncheol Cho | Insung S. Choi
Source: ChemMedChem (article, 2019-08-07)
Field: Materials Science — Materials Chemistry

Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology Deep learning has made great strides in tackling chemical problems, but still lacks full-fledged representations for three-dimensional (3D) molecular structures for its inner working. For example, the molecular graph, commonly used in chemistry and recently adapted to the graph convolutional network (GCN), is inherently a 2D representation of 3D molecules. Herein we propose an advanced version of the GCN, called 3DGCN, which receives 3D molecular information from a molecular graph augmented by information on bond direction. While outperforming state-of-the-art deep-learning models in the prediction of chemical and biological properties, 3DGCN has the ability to both generalize and distinguish molecular rotations in 3D, beyond 2D, which has great impact on drug discovery and development, not to mention the design of chemical reactions. Molecular graph ; Computer science ; Deep learning ; Graph ; Representation (politics) ; Drug discovery ; Artificial intelligence ; Topology (electrical circuits) ; Convolutional neural network ; Theoretical computer science ; Machine learning ; Chemistry ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Bart Olsthoorn | R. Matthias Geilhufe | Stanislav S. Borysov | Alexander V. Balatsky
Source: Advanced Quantum Technologies (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Band Gap Prediction for Large Organic Crystal Structures with Machine Learning Abstract Machine‐learning models are capable of capturing the structure–property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, the Organic Materials Database (OMDB) has hosted a growing number of calculated electronic properties of previously synthesized organic crystal structures. The complexity of the organic crystals contained within the OMDB, which have on average 82 atoms per unit cell, makes this database a challenging platform for machine learning applications. In this paper, the focus is on predicting the band gap which represents one of the basic properties of a crystalline material. With this aim, a consistent dataset of 12 500 crystal structures and their corresponding DFT band gap are released, freely available for download at https://omdb.mathub.io/dataset . An ensemble of two state‐of‐the‐art models reach a mean absolute error (MAE) of 0.388 eV, which corresponds to a percentage error of 13% for an average band gap of 3.05 eV. Finally, the trained models are employed to predict the band gap for 260 092 materials contained within the Crystallography Open Database (COD) and made available online so that the predictions can be obtained for any arbitrary crystal structure uploaded by a user. Band gap ; Crystal (programming language) ; Crystal structure prediction ; Crystal structure ; Ab initio ; Electronic band structure ; Mean squared prediction error ; Mean squared error Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Bryce Meredig
Source: Chemistry of Materials (article, 2019-12-10)
Field: Materials Science — Materials Chemistry

Five High-Impact Research Areas in Machine Learning for Materials Science ADVERTISEMENT RETURN TO ISSUEEditorialNEXTFive High-Impact Research Areas in Machine Learning for Materials ScienceBryce Meredig*Bryce MeredigCitrine Informatics, Redwood City, California 94063, United States*E-mail: [email protected]More by Bryce MeredigCite this: Chem. Mater. 2019, 31, 23, 9579–9581Publication Date (Web):December 10, 2019Publication History Published online10 December 2019Published inissue 10 December 2019https://doi.org/10.1021/acs.chemmater.9b04078Copyright © 2019 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views22845Altmetric-Citations34LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (242 KB) Get e-AlertsSUBJECTS:Machine learning,Material properties,Materials,Materials science Get e-Alerts Citation ; Computer science ; Social media ; Informatics ; Altmetrics ; World Wide Web ; Library science ; Information retrieval ; Data science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Nicholas E. Jackson | Alec Bowen | Lucas Antony | Michael A. Webb | Venkatram Vishwanath | Juan Pablo
Source: Science Advances (article, 2019-03-01)
Field: Materials Science — Materials Chemistry

Electronic structure at coarse-grained resolutions from supervised machine learning Computational studies aimed at understanding conformationally dependent electronic structure in soft materials require a combination of classical and quantum-mechanical simulations, for which the sampling of conformational space can be particularly demanding. Coarse-grained (CG) models provide a means of accessing relevant time scales, but CG configurations must be back-mapped into atomistic representations to perform quantum-chemical calculations, which is computationally intensive and inconsistent with the spatial resolution of the CG models. A machine learning approach, denoted as artificial neural network electronic coarse graining (ANN-ECG), is presented here in which the conformationally dependent electronic structure of a molecule is mapped directly to CG pseudo-atom configurations. By averaging over decimated degrees of freedom, ANN-ECG accelerates simulations by eliminating backmapping and repeated quantum-chemical calculations. The approach is accurate, consistent with the CG spatial resolution, and can be used to identify computationally optimal CG resolutions. Granularity ; Computer science ; Electronic structure ; Artificial neural network ; Quantum ; Resolution (logic) ; Artificial intelligence ; Algorithm ; Sampling (signal processing) ; Degrees of freedom (physics and chemistry) ; Quantum chemical ; Statistical physics ; Biological system ; Molecule ; Physics ; Quantum mechanics ; Computer vision Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis
Authors: Tristan I. Croll | Massimo Sammito | Andriy Kryshtafovych | Randy J. Read
Source: Proteins Structure Function and Bioinformatics (article, 2019-08-13)
Field: Materials Science — Materials Chemistry

Evaluation of template‐based modeling in CASP13 Performance in the template-based modeling (TBM) category of CASP13 is assessed here, using a variety of metrics. Performance of the predictor groups that participated is ranked using the primary ranking score that was developed by the assessors for CASP12. This reveals that the best results are obtained by groups that include contact predictions or inter-residue distance predictions derived from deep multiple sequence alignments. In cases where there is a good homolog in the wwPDB (TBM-easy category), the best results are obtained by modifying a template. However, for cases with poorer homologs (TBM-hard), very good results can be obtained without using an explicit template, by deep learning algorithms trained on the wwPDB. Alternative metrics are introduced, to allow testing of aspects of structural models that are not addressed by traditional CASP metrics. These include comparisons to the main-chain and side-chain torsion angles of the target, and the utility of models for solving crystal structures by the molecular replacement method. The alternative metrics are poorly correlated with the traditional metrics, and it is proposed that modeling has reached a sufficient level of maturity that the best models should be expected to satisfy this wider range of criteria. CASP ; Ranking (information retrieval) ; Computer science ; Machine learning ; Data mining ; Artificial intelligence ; Classifier (UML) ; Template ; Protein structure prediction ; Algorithm ; Pattern recognition (psychology) Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Mitchell Wood | Mary Alice Cusentino | Brian D. Wirth | Aidan P. Thompson
Source: Physical review. B./Physical review. B (article, 2019-05-30)
Field: Materials Science — Materials Chemistry

Data-driven material models for atomistic simulation The central approximation made in classical molecular dynamics simulation of materials is the interatomic potential used to calculate the forces on the atoms. Great effort and ingenuity is required to construct viable functional forms and find accurate parametrizations for potentials using traditional approaches. Machine learning has emerged as an effective alternative approach to develop accurate and robust interatomic potentials. Starting with a very general model form, the potential is learned directly from a database of electronic structure calculations and therefore can be viewed as a multiscale link between quantum and classical atomistic simulations. Risk of inaccurate extrapolation exists outside the narrow range of time and length scales where the two methods can be directly compared. In this work, we use the spectral neighbor analysis potential (SNAP) and show how a fit can be produced with minimal interpolation errors which is also robust in extrapolating beyond training. To demonstrate the method, we have developed a tungsten-beryllium potential suitable for the full range of binary compositions. Subsequently, large-scale molecular dynamics simulations were performed of high energy Be atom implantation onto the (001) surface of solid tungsten. The machine learned W-Be potential generates a population of implantation structures consistent with quantum calculations of defect formation energies. A very shallow ($&lt;2\phantom{\rule{0.16em}{0ex}}\mathrm{nm}$) average Be implantation depth is predicted which may explain ITER diverter degradation in the presence of beryllium. Statistical physics ; Computer science ; Materials science ; Physics Machine Learning in Materials Science ; Nuclear Materials and Properties ; Advanced Materials Characterization Techniques
Authors: Trevor A. Hamlin | Brian J. Levandowski | Ayush K. Narsaria | K. N. Houk | F. Matthias Bickelhaupt
Source: Chemistry - A European Journal (article, 2019-02-19)
Field: Chemistry — Organic Chemistry

Structural Distortion of Cycloalkynes Influences Cycloaddition Rates both by Strain and Interaction Energies The reactivities of 2-butyne, cycloheptyne, cyclooctyne, and cyclononyne in the 1,3-dipolar cycloaddition reaction with methyl azide were evaluated through DFT calculations at the M06-2X/6-311++G(d)//M06-2X/6-31+G(d) level of theory. Computed activation free energies for the cycloadditions of cycloalkynes are 16.5-22.0 kcal mol<sup>-1</sup> lower in energy than that of the acyclic 2-butyne. The strained or predistorted nature of cycloalkynes is often solely used to rationalize this significant rate enhancement. Our distortion/interaction-activation strain analysis has been revealed that the degree of geometrical predistortion of the cycloalkyne ground-state geometries acts to enhance reactivity compared with that of acyclic alkynes through three distinct mechanisms, not only due to (i) a reduced strain or distortion energy, but also to (ii) a smaller HOMO-LUMO gap, and (iii) an enhanced orbital overlap, which both contribute to more stabilizing orbital interactions. Cycloaddition ; Reactivity (psychology) ; Strain (injury) ; HOMO/LUMO ; Chemistry ; Distortion (music) ; Ground state ; Molecular orbital ; Activation energy ; Computational chemistry ; Stereochemistry ; Photochemistry ; Crystallography ; Materials science ; Physical chemistry ; Molecule ; Catalysis ; Physics ; Organic chemistry ; Atomic physics Click Chemistry and Applications ; Molecular Junctions and Nanostructures ; Photochromic and Fluorescence Chemistry
Authors: Yunus Zorlu | Doğan Erbahar | Ahmet Çetinkaya | A. Bulut | Turan S. Erkal | A. Özgür Yazaydın | Jens Beckmann | Gündoğ Yücesan
Source: Chemical Communications (article, 2019-01-01)
Field: Chemistry — Inorganic Chemistry

A cobalt arylphosphonate MOF – superior stability, sorption and magnetism We report a novel metal-organic framework (MOF) based on a cobalt arylphosphonate, namely, [Co2(H4-MTPPA)]·3NMP·H2O (1·3NMP·H2O), which was prepared solvothermically from the tetrahedral linker tetraphenylmethane tetrakis-4-phosphonic acid (H8-MTPPA) and CoSO4·7H2O in N-methyl-2-pyrrolidone (NMP). Compound 1 has the highest porosity (BET surface area of 1034 m2 g-1) ever reported for a MOF based on an aryl phosphonic acid linker. The indigo blue crystals of 1·3NMP·H2O are composed of edge-shared eight-membered Co2P2O4 rings, and are thermally very stable up to 500 °C. Cobalt ; Magnetism ; Sorption ; Chemical engineering ; Materials science ; Chemistry ; Inorganic chemistry ; Adsorption ; Physical chemistry ; Physics ; Condensed matter physics ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Magnetism in coordination complexes
Authors: Jiaming Zhu | Dong Wang | Yipeng Gao | Tong‐Yi Zhang | Yunzhi Wang
Source: Materials Today (article, 2019-11-03)
Field: Materials Science — Materials Chemistry

Linear-superelastic metals by controlled strain release via nanoscale concentration-gradient engineering Materials science ; Shape-memory alloy ; Pseudoelasticity ; Nickel titanium ; Whiskers ; Composite material ; Diffusionless transformation ; Elastic modulus ; Nanowire ; Nanoscopic scale ; Modulus ; Martensite ; Nanotechnology ; Microstructure Shape Memory Alloy Transformations ; Nonlocal and gradient elasticity in micro/nano structures ; Titanium Alloys Microstructure and Properties
Authors: Kun Cao | Miao Gong | Jianfeng Yang | Jiaming Cai | Shengqi Chu | Zhiping Chen | Bin Shan | Rong Chen
Source: Journal of Catalysis (article, 2019-04-23)
Field: Materials Science — Materials Chemistry

Nickel catalyst with atomically-thin meshed cobalt coating for improved durability in dry reforming of methane Catalysis ; Carbon dioxide reforming ; Coating ; Sintering ; Chemical engineering ; Nanoparticle ; Carbon fibers ; Nickel ; Methane ; Cobalt ; Coke ; Materials science ; Carbon nanotube ; Atomic layer deposition ; Layer (electronics) ; Chemistry ; Nanotechnology ; Syngas ; Metallurgy ; Composite material ; Organic chemistry ; Composite number Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Nagendra S. Chauhan | Sivaiah Bathula | Bhasker Gahtori | S. D. Mahanti | Amrita Bhattacharya | Avinash Vishwakarma | Ruchi Bhardwaj | Vidya Nand Singh | Ajay Dhar
Source: ACS Applied Materials & Interfaces (article, 2019-08-23)
Field: Materials Science — Materials Chemistry

Compositional Tailoring for Realizing High Thermoelectric Performance in Hafnium-Free n-Type ZrNiSn Half-Heusler Alloys Compositional tailoring enables fine-tuning of thermoelectric (TE) transport parameters by synergistic modulation of electronic and vibrational properties. In the present work, the aspects of compositionally tailored defects have been explored in ZrNiSn-based half-Heusler (HH) TE materials to achieve high TE performance and cost effectiveness in n-type Hf-free HH alloys. In off-stoichiometric Ni-rich ZrNi<sub>1+<i>x</i></sub>Sn alloys in a low Ni doping limit (<i>x</i> < 0.1), excess Ni induces defects (Ni/vacancy antisite + interstitials), which tend to cause band structure modification. In addition, the structural similarity of HH and full-Heusler (FH) compounds and formation energetics lead to an intrinsic phase segregation of FH nanoscale precipitates that are coherently dispersed within the ZrNiSn HH matrix as nanoclusters. A consonance was achieved experimentally between these two competing mechanisms for optimal HH composition having both FH precipitates and Ni/vacancy antisite defects in the HH matrix by elevating the sintering temperature up to the solubility limit range of the ZrNiSn system. Defect-mediated optimization of electrical and thermal transport via carrier concentration tuning, energy filtering, and possibly all scale-hierarchical architecture resulted in a maximum <i>ZT</i> ≈ 1.1 at 873 K for the optimized ZrNi<sub>1.03</sub>Sn composition. Our findings highlight the realistic prospect of enhancing TE performance via compositional engineering approach for wide applications of TE. Materials science ; Thermoelectric effect ; Vacancy defect ; Phase (matter) ; Thermoelectric materials ; Nanoclusters ; Doping ; Solid solution ; Condensed matter physics ; Optoelectronics ; Nanotechnology ; Thermodynamics ; Metallurgy ; Thermal conductivity ; Composite material Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Thermal Expansion and Ionic Conductivity
Authors: Febrian Hillman | Hae‐Kwon Jeong
Source: ACS Applied Materials & Interfaces (article, 2019-05-03)
Field: Chemistry — Inorganic Chemistry

Linker-Doped Zeolitic Imidazolate Frameworks (ZIFs) and Their Ultrathin Membranes for Tunable Gas Separations Zeolitic imidazolate frameworks (ZIFs) have gained much interest due to their potentials in gas separations. A hybrid approach by mixing metals and/or linkers has been recently investigated to fine-tune the ZIF framework porosity and surface properties and potentially extending their separation applications for many important gas mixtures. In general, the hybrid approach requires mixing of isostructural ZIFs to maintain their topology, thus limiting the options of linkers and metal centers to obtain hybrid ZIFs. Linker-doping, as reported here, can be a strategy to expand the option of imidazolate linkers to obtain mixed-linker hybrid ZIFs. Two linkers are investigated to be doped into the ZIF-8 framework: 2-ethylimidazole (eIm) and 2-phenylimidazole (phIm). The linker-doping strategy is shown to tune the "stiffness" of Zn-N bonding as characterized by FT-IR, thereby the linker flip-flopping motion of ZIF-8, which is analyzed through gas adsorption isotherms. Furthermore, well-intergrown ultrathin eIm-doped ZIF-8 membranes are grown on α-Al<sub>2</sub>O<sub>3</sub> substrates, in which the incorporation of eIm affects the morphology and thickness of the polycrystalline membranes, improving the permeance of propylene and propane molecules. Zeolitic imidazolate framework ; Materials science ; Doping ; Linker ; Membrane ; Gas separation ; Nanotechnology ; Chemical engineering ; Imidazolate ; Metal-organic framework ; Optoelectronics ; Adsorption ; Organic chemistry ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Covalent Organic Framework Applications