jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hyun Soo Han | Woosung Park | Sungwon Hwang | Hyunkyu Kim | Yelyn Sim | Subramani Surendran | Uk Sim | In Sun Cho
Source: Journal of Catalysis (article, 2020-06-16)
Field: Materials Science — Materials Chemistry

(0 2 0)-Textured tungsten trioxide nanostructure with enhanced photoelectrochemical activity X-ray photoelectron spectroscopy ; Photocurrent ; Tungsten trioxide ; Water splitting ; Nanostructure ; Chemistry ; Chemical engineering ; Nanotechnology ; Deposition (geology) ; Thin film ; Reversible hydrogen electrode ; Materials science ; Electrode ; Optoelectronics ; Tungsten ; Catalysis ; Electrochemistry ; Photocatalysis ; Working electrode ; Organic chemistry Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques ; Iron oxide chemistry and applications
Authors: Mārtiņš Ruciņš | Aiva Plotniece | Eiva Bernotienė | Wei‐Bor Tsai | Arkadij Sobolev
Source: Catalysts (article, 2020-09-04)
Field: Chemistry — Organic Chemistry

Recent Approaches to Chiral 1,4-Dihydropyridines and their Fused Analogues The purpose of this review is to highlight recent developments in the synthesis of chiral 1,4-dihydropyridines and their fused analogues. 1,4-Dihydropyridines are among the most active calcium antagonists that are used for the treatment of hypertension. Enantiomers of unsymmetrical 1,4-dihydropyridines often show different biological activities and may have even an opposite action profile. Hantzsch synthesis usually produces racemic mixtures of unsymmetrical 1,4-dihydropyridines. Therefore, the development of stereoselective synthesis of 1,4-dihydropyridines is one of the priorities of medicinal chemistry. Over the years, numerous methodologies have been developed for the production of enantiopure 1,4-dihydropyridines, such as stereoselective synthesis using chiral auxiliaries and chiral cyclocondensation partners, chromatographical methods, resolution of diastereomeric 1,4-dihydropyridine salts, enzyme catalysed kinetic resolution, or asymmetrisation of ester groups of 1,4-dihydropyridines. These approaches have been studied in detail and are relatively well established. The catalytic asymmetric approach holds the greatest promise in delivering the most practical and widely applicable methods. Substantial progress has been made toward the development of enantioselective organocatalytic methods for the construction of the chiral dihydropyridines. However, most of them do not provide a convenient way to pharmacologically important 1,4-dihydropyridine-3,5-dicarboxylates. Organocatalytic enantioselective desymmetrisation of prochiral 1,4-dihydropyridine-3,5-dicarbaldehydes also has great promise in the synthesis of pharmacologically important 1,4-dihydropyridine-3,5-dicarboxylates. Enantioselective synthesis ; Enantiopure drug ; Chemistry ; Enantiomer ; Diastereomer ; Dihydropyridine ; Kinetic resolution ; Combinatorial chemistry ; Stereoselectivity ; Stereochemistry ; Organic chemistry ; Catalysis ; Calcium Asymmetric Synthesis and Catalysis ; Chemical Synthesis and Analysis ; Synthetic Organic Chemistry Methods
Authors: John Isaac Enriquez | Fahdzi Muttaqien | M. Michiuchi | Kouji Inagaki | Masaaki Geshi | Ikutaro Hamada | Yoshitada Morikawa
Source: Carbon (article, 2020-11-23)
Field: Materials Science — Materials Chemistry

Oxidative etching mechanism of the diamond (100) surface We performed density functional theory calculations with van der Waals corrections to elucidate diamond oxidation mechanism on the atomic-level which could lead to insights that will advance the improvement of nascent nanofabrication technologies. We developed a comprehensive theory of oxidative etching of the diamond (100) surface, from the adsorption of gas phase O2, including details of metastable adsorption states, intersystem crossing, and induced surface dereconstruction, to the desorption of CO and CO2, complete etching of the top surface layer and its subsequent stabilization. Oxygen adsorption induce C-dimer bond breaking through the formation of carbonyl structures at low surface coverage. At high surface coverage, adjacent carbonyls can transform into the more stable ether chain with small energy barrier requirement. A mix of carbonyl and ether will form in surfaces with defects, and several models of possible structures have been presented. CO desorption creates a point defect that serves as nucleation site for the preferred etching direction along [011], perpendicular to the top layer C-dimer bonds. We obtained a wide range of desorption activation energies, which depend on the existence of point defects and reconstruction of surfaces. C-dimer formation and oxygen adsorption stabilize vacancies and single-atomic-layer-deep trough. Desorption ; Nucleation ; Adsorption ; Diamond ; Chemical physics ; Dimer ; Density functional theory ; Etching (microfabrication) ; Chemistry ; van der Waals force ; Dangling bond ; Materials science ; Photochemistry ; Layer (electronics) ; Physical chemistry ; Nanotechnology ; Computational chemistry ; Molecule ; Organic chemistry ; Silicon Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; High-pressure geophysics and materials
Authors: Monan Liu | Mu‐Tian Li | Shuai Xu | Han Yang | Hong‐Bo Sun
Source: Frontiers in Chemistry (review, 2020-10-16)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired Superhydrophobic Surfaces via Laser-Structuring Bioinspired superhydrophobic surfaces are an artificial functional surface that mainly extracts morphological designs from natural organisms. In both laboratory research and industry, there is a need to develop ways of giving large-area surfaces water repellence. Currently, surface modification methods are subject to many challenging requirements such as a need for chemical-free treatment or high surface roughness. Laser micro-nanofabrications are a potential way of addressing these challenges, as they involve non-contact processing and outstanding patterning ability. This review briefly discusses multiple laser patterning methods, which could be used for surface structuring toward creating superhydrophobic surfaces. Structuring ; Nanotechnology ; Materials science ; Surface modification ; Contact angle ; Surface roughness ; Surface finish ; Laser ; Surface (topology) ; Engineering ; Chemical engineering ; Optics ; Composite material ; Physics Surface Modification and Superhydrophobicity ; Laser Material Processing Techniques ; Adhesion, Friction, and Surface Interactions
Authors: Dong Hoon Hur | Woo Sung Choi | Tae Yong Kim | Sang Yup Lee | Jin Hwan Park | Ki Jun Jeong
Source: Journal of Microbiology and Biotechnology (article, 2020-07-07)
Field: Materials Science — Biomaterials

Enhanced Production of Bacterial Cellulose in <i>Komagataeibacter xylinus</i> Via Tuning of Biosynthesis Genes with Synthetic RBS Bacterial cellulose (BC) has outstanding physical and chemical properties, including high crystallinity, moisture retention, and tensile strength. Currently, the major producer of BC is <i>Komagataeibacter xylinus</i>. However, due to limited tools of expression, this host is difficult to engineer metabolically to improve BC productivity. In this study, a regulated expression system for <i>K. xylinus</i> with synthetic ribosome binding site (RBS) was developed and used to engineer a BC biosynthesis pathway. A synthetic RBS library was constructed using green fluorescent protein (GFP) as a reporter, and three synthetic RBSs (R4, R15, and R6) with different strengths were successfully isolated by fluorescence-activated cell sorting (FACS). Using synthetic RBS, we optimized the expression of three homologous genes responsible for BC production, <i>pgm</i>, <i>galU</i>, and <i>ndp</i>, and thereby greatly increased it under both static and shaking culture conditions. The final titer of BC under static and shaking conditions was 5.28 and 3.67 g/l, respectively. Our findings demonstrate that reinforced metabolic flux towards BC through quantitative gene expression represents a practical strategy for the improvement of BC productivity. Bacterial cellulose ; Ribosomal binding site ; Biosynthesis ; Green fluorescent protein ; Cell sorting ; Biochemistry ; Gene ; Chemistry ; Gene expression ; Bacteria ; Cellulose ; Crystallinity ; Biology ; Cell ; Translation (biology) ; Messenger RNA ; Genetics Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Enzyme Production and Characterization
Authors: Yong Zhao | Kunpeng Yuan | Yinqiao Liu | Steph-Yves Louis | Ming Hu | Jianjun Hu
Source: The Journal of Physical Chemistry C (article, 2020-06-12)
Field: Materials Science — Materials Chemistry

Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks Materials representation plays a key role in machine learning-based prediction of materials properties and new materials discovery. Currently both graph and three-dimensional (3D) voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD-based 3D convolutional neural networks (CNNs) for predicting the elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2170 Fm3̅m face-centered-cubic materials show that our ECD-based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Materials-Agnostic Platform for Informatics and Exploration features and ECD descriptors achieved the best fivefold cross-validation performance. More importantly, we showed that our ECD-based CNN models can achieve significantly better extrapolation performance when evaluated over nonredundant data sets, where there are few neighbor-training samples around test samples. As an additional validation, we evaluated the predictive performance of our models on 329 materials of space group Fm3̅m by comparing to density functional theory calculated values, which shows a better prediction power of our model for bulk modulus than shear modulus. Because of the unified representation power of ECD, it is expected that our ECD-based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials. Extrapolation ; Computer science ; Convolutional neural network ; Pooling ; Artificial intelligence ; Representation (politics) ; Inductive bias ; Artificial neural network ; Voxel ; Pattern recognition (psychology) ; Materials science ; Machine learning ; Algorithm ; Multi-task learning ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Amitava Choudhury
Source: Archives of Computational Methods in Engineering (article, 2020-10-22)
Field: Materials Science — Materials Chemistry

The Role of Machine Learning Algorithms in Materials Science: A State of Art Review on Industry 4.0 State (computer science) ; Computer science ; Machine learning ; Algorithm ; Artificial intelligence ; Industrial engineering ; Engineering Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Advanced materials and composites
Authors: Conrad Clement | Steven K. Kauwe | Taylor D. Sparks
Source: Integrating materials and manufacturing innovation (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

Benchmark AFLOW Data Sets for Machine Learning Computer science ; Machine learning ; Python (programming language) ; Benchmark (surveying) ; Exploit ; Artificial intelligence ; Support vector machine ; Benchmarking ; Data mining ; Artificial neural network ; Data type ; Ensemble learning ; Variety (cybernetics) ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Guozhu Li | Zheng Hu | Fang Hou | Xinyu Li | Li Wang | Xiangwen Zhang
Source: Fuel (article, 2020-01-07)
Field: Materials Science — Materials Chemistry

Machine learning enabled high-throughput screening of hydrocarbon molecules for the design of next generation fuels Hydrocarbon ; Combustion ; Process engineering ; Computer science ; Throughput ; Molecule ; Biochemical engineering ; Nanotechnology ; Environmental science ; Chemistry ; Materials science ; Organic chemistry ; Engineering ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Heesu Hwang | Sung Min Choi | Jiwon Oh | Seung‐Muk Bae | Jong‐Ho Lee | Jae-Pyeong Ahn | Jeong‐O Lee | Ki‐Seok An | Young Yoon | Jin‐Ha Hwang
Source: Journal of Power Sources (article, 2020-07-02)
Field: Materials Science — Materials Chemistry

Integrated application of semantic segmentation-assisted deep learning to quantitative multi-phased microstructural analysis in composite materials: Case study of cathode composite materials of solid oxide fuel cells Triple phase boundary ; Cathode ; Composite number ; Materials science ; Segmentation ; Solid oxide fuel cell ; Focused ion beam ; Computer science ; Phase (matter) ; Artificial intelligence ; Electrode ; Anode ; Composite material ; Ion ; Engineering ; Chemistry ; Electrical engineering Advancements in Solid Oxide Fuel Cells ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Christoph Brunken | Markus Reiher
Source: Journal of Chemical Theory and Computation (article, 2020-01-17)
Field: Materials Science — Materials Chemistry

Self-Parametrizing System-Focused Atomistic Models Computational studies of chemical reactions in complex environments such as proteins, nanostructures, or on surfaces require accurate and efficient atomistic models applicable to the nanometer scale. In general, an accurate parametrization of the atomistic entities will not be available for arbitrary system classes but demands a fast, automated, system-focused parametrization procedure to be quickly applicable, reliable, flexible, and reproducible. Here, we develop and combine an automatically parametrizable quantum chemically derived molecular mechanics model with machine-learned corrections under autonomous uncertainty quantification and refinement. Our approach first generates an accurate, physically motivated model from a minimum energy structure and its corresponding Hessian matrix by a partial Hessian fitting procedure of the force constants. This model is then the starting point to generate a large number of configurations for which additional off-minimum reference data can be evaluated on the fly. A Δ-machine learning model is trained on these data to provide a correction to energies and forces including uncertainty estimates. During the procedure, the flexibility of the machine learning model is tailored to the amount of available training data. The parametrization of large systems is enabled by a fragmentation approach. Due to their modular nature, all model construction steps allow for model improvement in a rolling fashion. Our approach may also be employed for the generation of system-focused electrostatic molecular mechanics embedding environments in a quantum-mechanical/molecular-mechanical hybrid model for arbitrary atomistic structures at the nanoscale. Parametrization (atmospheric modeling) ; Hessian matrix ; Computer science ; Modular design ; Flexibility (engineering) ; Computational science ; Algorithm ; Physics ; Mathematics ; Applied mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Fuel Cells and Related Materials
Authors: Marco Eckhoff | Knut Nikolas Lausch | Peter E. Blöchl | Jörg Behler
Source: The Journal of Chemical Physics (article, 2020-10-23)
Field: Materials Science — Materials Chemistry

Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels Lithium ion batteries often contain transition metal oxides such as Li<sub>x</sub>Mn<sub>2</sub>O<sub>4</sub> (0 ≤ x ≤ 2). Depending on the Li content, different ratios of Mn<sup>III</sup> to Mn<sup>IV</sup> ions are present. In combination with electron hopping, the Jahn-Teller distortions of the Mn<sup>III</sup>O<sub>6</sub> octahedra can give rise to complex phenomena such as structural transitions and conductance. While for small model systems oxidation and spin states can be determined using density functional theory (DFT), the investigation of dynamical phenomena by DFT is too demanding. Previously, we have shown that a high-dimensional neural network potential can extend molecular dynamics (MD) simulations of Li<sub>x</sub>Mn<sub>2</sub>O<sub>4</sub> to nanosecond time scales, but these simulations did not provide information about the electronic structure. Here, we extend the use of neural networks to the prediction of atomic oxidation and spin states. The resulting high-dimensional neural network is able to predict the spins of the Mn ions with an error of only 0.03 ℏ. We find that the Mn e<sub>g</sub> electrons are correctly conserved and that the number of Jahn-Teller distorted Mn<sup>III</sup>O<sub>6</sub> octahedra is predicted precisely for different Li loadings. A charge ordering transition is observed between 280 K and 300 K, which matches resistivity measurements. Moreover, the activation energy of the electron hopping conduction above the phase transition is predicted to be 0.18 eV, deviating only 0.02 eV from experiment. This work demonstrates that machine learning is able to provide an accurate representation of both the geometric and the electronic structure dynamics of Li<sub>x</sub>Mn<sub>2</sub>O<sub>4</sub> on time and length scales that are not accessible by ab initio MD. Spins ; Ion ; Density functional theory ; Electronic structure ; Lithium (medication) ; Ab initio ; Phase transition ; Electron ; Spin (aerodynamics) Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography
Authors: Jonas Köhler | Leon Klein | Frank Noé
Source: arXiv (Cornell University) (preprint, 2020-06-03)
Field: Materials Science — Materials Chemistry

Equivariant Flows: Exact Likelihood Generative Learning for Symmetric\n Densities Normalizing flows are exact-likelihood generative neural networks which\napproximately transform samples from a simple prior distribution to samples of\nthe probability distribution of interest. Recent work showed that such\ngenerative models can be utilized in statistical mechanics to sample\nequilibrium states of many-body systems in physics and chemistry. To scale and\ngeneralize these results, it is essential that the natural symmetries in the\nprobability density -- in physics defined by the invariances of the target\npotential -- are built into the flow. We provide a theoretical sufficient\ncriterion showing that the distribution generated by \\textit{equivariant}\nnormalizing flows is invariant with respect to these symmetries by design.\nFurthermore, we propose building blocks for flows which preserve symmetries\nwhich are usually found in physical/chemical many-body particle systems. Using\nbenchmark systems motivated from molecular physics, we demonstrate that those\nsymmetry preserving flows can provide better generalization capabilities and\nsampling efficiency.\n Homogeneous space ; Equivariant map ; Invariant (physics) ; Generalization ; Generative model ; Statistical physics ; Generative grammar ; Distribution (mathematics) ; Symmetry (geometry) ; Probability distribution ; Benchmark (surveying) ; Flow (mathematics) ; Mathematics ; Applied mathematics ; Computer science ; Pure mathematics ; Artificial intelligence ; Physics ; Mathematical analysis ; Geometry ; Statistics ; Mathematical physics Machine Learning in Materials Science ; Neural Networks and Applications ; Topic Modeling
Authors: Javier E. Santos | Mohammed Mehana | Hao Wu | Maša Prodanović | Qinjun Kang | Nicholas Lubbers | Hari Viswanathan | Michael J. Pyrcz
Source: The Journal of Physical Chemistry C (article, 2020-09-18)
Field: Materials Science — Materials Chemistry

Modeling Nanoconfinement Effects Using Active Learning Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow. Active learning (machine learning) ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Petroleum Processing and Analysis ; Mineral Processing and Grinding
Authors: Masahiro Sakurai | Renhai Wang | Timothy Liao | Chao Zhang | Huaijun Sun | Yang Sun | Haidi Wang | Xin Zhao | Songyou Wang | Balamurugan Balasubramanian | Xiaoshan Xu | D. J. Sellmyer | Vladimir Antropov | Jian-Hua Zhang | Cai‐Zhuang Wang | Kai-Ming Ho | James R. Chelikowsky
Source: Physical Review Materials (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

Discovering rare-earth-free magnetic materials through the development of a database An open-access database specialized for magnetic compounds, as well as for magnetic clusters, is developed with a focus on magnets free from rare earths. Data-intensive methods are used to facilitate the theoretical and experimental design and discovery of new magnetic materials. The utility of the datasets for computational screening, machine-learning modeling, and experimental fabrication is discussed. Rare earth ; Materials science ; Magnet ; Focus (optics) ; Fabrication ; Nanotechnology ; Database ; Computer science ; Mechanical engineering ; Metallurgy ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques