jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Balaranjan Selvaratnam | Ranjit T. Koodali
Source: Catalysis Today (article, 2020-08-21)
Field: Materials Science — Materials Chemistry

Machine learning in experimental materials chemistry Nanotechnology ; Chemical space ; Characterization (materials science) ; Computer science ; Field (mathematics) ; Process (computing) ; Biochemical engineering ; Chemistry ; Artificial intelligence ; Engineering ; Drug discovery ; Materials science ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Benjamin Rizkin | Albert Shkolnik | Neil Ferraro | Ryan L. Hartman
Source: Nature Machine Intelligence (article, 2020-04-06)
Field: Materials Science — Materials Chemistry

Combining automated microfluidic experimentation with machine learning for efficient polymerization design Microreactor ; Polymerization ; Computer science ; Automation ; Process engineering ; Reagent ; Materials science ; Catalysis ; Chemistry ; Polymer ; Mechanical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Peng Kang | Zhongli Liu | Hakima Abou‐Rachid | Hong Guo
Source: The Journal of Physical Chemistry A (article, 2020-06-08)
Field: Materials Science — Materials Chemistry

Machine-Learning Assisted Screening of Energetic Materials In this work, machine learning (ML), materials informatics (MI), and thermochemical data are combined to screen potential candidates of energetic materials. To directly characterize energetic performance, the heat of explosion Δ<i>H</i><sub><i>e</i></sub> is used as the target property. The critical descriptors of cohesive energy, averaged over all constituent elements and the oxygen balance, are found by forward stepwise selection from a large number of possible descriptors. With them and a theoretically labeled Δ<i>H</i><sub><i>e</i></sub> training data set, a satisfactory surrogate ML model is trained. The ML model is applied to large databases ICSD and PubChem to predict Δ<i>H</i><sub><i>e</i></sub>. At the gross-level filtering by the ML model, 2732 molecular candidates based on carbon, hydrogen, nitrogen, and oxygen (CHNO) with high Δ<i>H</i><sub><i>e</i></sub> values are predicted. Afterward, a fine-level thermochemical screening is carried out on the 2732 materials, resulting in 262 candidates with TNT equivalent power index <i>P</i><sub><i>e</i>(TNT)</sub> greater than 1.5. Raising <i>P</i><sub><i>e</i>(TNT)</sub> further to larger than 1.8, 29 potential candidates are found from the 2732 materials, all are new to the current reservoir of well-known energetic materials. PubChem ; Computer science ; Process engineering ; Materials science ; Nuclear engineering ; Chemistry ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Arun Baskaran | Genevieve Kane | Krista Biggs | R. Hull | D. Lewis
Source: Computational Materials Science (article, 2020-03-03)
Field: Materials Science — Materials Chemistry

Adaptive characterization of microstructure dataset using a two stage machine learning approach Artificial intelligence ; Microstructure ; Computer science ; Convolutional neural network ; Pattern recognition (psychology) ; Materials science ; Pipeline (software) ; Segmentation ; Equiaxed crystals ; Image processing ; Metallurgy ; Image (mathematics) Machine Learning in Materials Science ; Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Surya R. Kalidindi
Source: Journal of Applied Physics (article, 2020-07-28)
Field: Materials Science — Materials Chemistry

Feature engineering of material structure for AI-based materials knowledge systems This tutorial introduces systematically the foundational concepts undergirding the recently formulated AI (artificial intelligence)-based materials knowledge system (AI-MKS) framework. More specifically, these concepts deal with features engineering the heterogeneous material internal structure to obtain low-dimensional representations that can then be combined with machine learning models to establish low-computational cost surrogate models for capturing the process–structure–property linkages over a hierarchy of material structure/lengths scales. Generally referred to as materials knowledge systems (MKS), this framework synergistically leverages the emergent AI/ML (machine learning) toolsets in conjunction with the modern experimental and physics-based simulation toolsets employed currently by the domain experts in the materials field. The primary goal of this tutorial is to present to the domain expert the foundations needed to understand and take advantage of the impending opportunities arising from a synergistic integration of AI/ML tools into the current materials innovation efforts while identifying a specific path forward for accomplishing this goal. Hierarchy ; Computer science ; Domain (mathematical analysis) ; Artificial intelligence ; Field (mathematics) ; Domain knowledge ; Expert system ; Process (computing) ; Feature (linguistics) ; Property (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Siwen Wang | Hemanth Somarajan Pillai | Hongliang Xin
Source: Nature Communications (article, 2020-11-30)
Field: Materials Science — Materials Chemistry

Bayesian learning of chemisorption for bridging the complexity of electronic descriptors Building upon the d-band reactivity theory in surface chemistry and catalysis, we develop a Bayesian learning approach to probing chemisorption processes at atomically tailored metal sites. With representative species, e.g., *O and *OH, Bayesian models trained with ab initio adsorption properties of transition metals predict site reactivity at a diverse range of intermetallics and near-surface alloys while naturally providing uncertainty quantification from posterior sampling. More importantly, this conceptual framework sheds light on the orbitalwise nature of chemical bonding at adsorption sites with d-states characteristics ranging from bulk-like semi-elliptic bands to free-atom-like discrete energy levels, bridging the complexity of electronic descriptors for the prediction of novel catalytic materials. Chemisorption ; Bridging (networking) ; Intermetallic ; Bayesian probability ; Ab initio ; Electronic structure ; Chemical physics ; Adsorption ; Density functional theory ; Materials science ; Computational chemistry ; Chemistry ; Computer science ; Statistical physics ; Physics ; Physical chemistry ; Alloy ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Bin Sun | Maral Vafaie | Larissa Levina | Mingyang Wei | Yitong Dong | Yajun Gao | Hao Ting Kung | Margherita Biondi | Andrew H. Proppe | Bin Chen | Min‐Jae Choi | Laxmi Kishore Sagar | Oleksandr Voznyy | Shana O. Kelley | Frédéric Laquai | Zheng‐Hong Lu | Sjoerd Hoogland | F. Pelayo Garcı́a de Arquer | Edward H. Sargent
Source: Nano Letters (article, 2020-03-31)
Field: Materials Science — Materials Chemistry

Ligand-Assisted Reconstruction of Colloidal Quantum Dots Decreases Trap State Density Increasing the power conversion efficiency (PCE) of colloidal quantum dot (CQD) solar cells has relied on improving the passivation of CQD surfaces, enhancing CQD coupling and charge transport, and advancing device architecture. The presence of hydroxyl groups on the nanoparticle surface, as well as dimers-fusion between CQDs-has been found to be the major source of trap states, detrimental to optoelectronic properties and device performance. Here, we introduce a CQD reconstruction step that decreases surface hydroxyl groups and dimers simultaneously. We explored the dynamic interaction of charge carriers between band-edge states and trap states in CQDs using time-resolved spectroscopy, showing that trap to ground-state recombination occurs mainly from surface defects in coupled CQD solids passivated using simple metal halides. Using CQD reconstruction, we demonstrate a 60% reduction in trap density and a 25% improvement in charge diffusion length. These translate into a PCE of 12.5% compared to 10.9% for control CQDs. Passivation ; Quantum dot ; Nanocrystal ; Materials science ; Nanoparticle ; Colloid ; Diffusion ; Trap (plumbing) ; Nanotechnology ; Surface charge ; Potential well ; Optoelectronics ; Chemical physics ; Chemistry ; Physical chemistry Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Kathryn S. Deeg | Daiane Damasceno Borges | Daniele Ongari | Nakul Rampal | Leopold Talirz | Aliaksandr V. Yakutovich | Johanna M. Huck | Berend Smit
Source: ACS Applied Materials & Interfaces (article, 2020-03-26)
Field: Materials Science — Materials Chemistry

In Silico Discovery of Covalent Organic Frameworks for Carbon Capture We screen a database of more than 69 000 hypothetical covalent organic frameworks (COFs) for carbon capture using parasitic energy as a metric. To compute CO<sub>2</sub>-framework interactions in molecular simulations, we develop a genetic algorithm to tune the charge equilibration method and derive accurate framework partial charges. Nearly 400 COFs are identified with parasitic energy lower than that of an amine scrubbing process using monoethanolamine; more than 70 are better performers than the best experimental COFs and several perform similarly to Mg-MOF-74. We analyze the effect of pore topology on carbon capture performance to guide the development of improved carbon capture materials. Carbon fibers ; Materials science ; Covalent bond ; Metric (unit) ; In silico ; Amine gas treating ; Nanotechnology ; Topology (electrical circuits) ; Computer science ; Organic chemistry ; Chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Ondrej Dyck | Lizhi Zhang | Mina Yoon | Jacob L. Swett | Dale K. Hensley | Cheng Zhang | Philip D. Rack | Jason D. Fowlkes | Andrew R. Lupini | Stephen Jesse
Source: Carbon (article, 2020-11-07)
Field: Materials Science — Materials Chemistry

Doping transition-metal atoms in graphene for atomic-scale tailoring of electronic, magnetic, and quantum topological properties Graphene ; Dopant ; Materials science ; Nanotechnology ; Atomic units ; Doping ; Transition metal ; Fabrication ; Density functional theory ; Chemical physics ; Topology (electrical circuits) ; Optoelectronics ; Chemistry ; Computational chemistry ; Physics ; Quantum mechanics Graphene research and applications ; Quantum and electron transport phenomena ; Electronic and Structural Properties of Oxides
Authors: Dmytro Guzenko | S.K. Burley | José M. Duarte
Source: PLoS Computational Biology (article, 2020-07-08)
Field: Materials Science — Materials Chemistry

Real time structural search of the Protein Data Bank Detection of protein structure similarity is a central challenge in structural bioinformatics. Comparisons are usually performed at the polypeptide chain level, however the functional form of a protein within the cell is often an oligomer. This fact, together with recent growth of oligomeric structures in the Protein Data Bank (PDB), demands more efficient approaches to oligomeric assembly alignment/retrieval. Traditional methods use atom level information, which can be complicated by the presence of topological permutations within a polypeptide chain and/or subunit rearrangements. These challenges can be overcome by comparing electron density volumes directly. But, brute force alignment of 3D data is a compute intensive search problem. We developed a 3D Zernike moment normalization procedure to orient electron density volumes and assess similarity with unprecedented speed. Similarity searching with this approach enables real-time retrieval of proteins/protein assemblies resembling a target, from PDB or user input, together with resulting alignments (http://shape.rcsb.org). Protein Data Bank ; Protein Data Bank (RCSB PDB) ; Computer science ; Similarity (geometry) ; Nearest neighbor search ; Protein structure ; Normalization (sociology) ; Data mining ; Computational biology ; Structural similarity ; Bioinformatics ; Artificial intelligence ; Biology ; Image (mathematics) ; Biochemistry Enzyme Structure and Function ; Genomics and Phylogenetic Studies ; Advanced Proteomics Techniques and Applications
Authors: T. Manobala | Sudhir K. Shukla | T. Subba Rao | M. Dharmendira Kumar
Source: Chemosphere (article, 2020-11-02)
Field: Chemistry — Inorganic Chemistry

Kinetic modelling of the uranium biosorption by Deinococcus radiodurans biofilm Biosorption ; Sorption ; Uranyl ; Deinococcus radiodurans ; Chemistry ; Uranium ; Biofilm ; Adsorption ; Nuclear chemistry ; Bioremediation ; Environmental chemistry ; Chemical engineering ; Organic chemistry ; Contamination ; Materials science ; Metallurgy ; Ion ; Biochemistry ; Geology Radioactive element chemistry and processing ; Geochemistry and Elemental Analysis ; Radioactive contamination and transfer
Authors: Yunguang Zhang | Shuo Li | Haile Liu | Wei Long | Xiaodong Zhang
Source: Frontiers in Chemistry (review, 2020-04-03)
Field: Materials Science — Materials Chemistry

Enzyme-Like Properties of Gold Clusters for Biomedical Application In recent years, the rapid development of nanoscience and technology has provided a new opportunity for the development and preparation of new inorganic enzymes. Nanozyme is a new generation of artificial mimetic enzyme, which like natural enzymes, can efficiently catalyze the substrate of enzyme under mild conditions, exhibiting catalytic efficiency, and enzymatic reaction kinetics similar to natural enzymes. However, nanozymes exist better stability than native enzymes, it can still maintain 85 % catalytic activity in strong acid and alkali (pH 2~10) or large temperature range (4~90°C). This provides conditions for designing complex catalytic systems. In this review, we discussed the enzymatic attributes and biomedical applications of gold nanoclusters, including peroxidase-like, catalase-like, detection of heavy metal ions, and therapy of brain and cancer etc. This review can help us understand the current research status nanozymes. Nanoclusters ; Enzyme ; Catalysis ; Catalytic efficiency ; Substrate (aquarium) ; Chemistry ; Nanotechnology ; Combinatorial chemistry ; Peroxidase ; Metal ions in aqueous solution ; Cancer therapy ; Materials science ; Metal ; Biochemistry ; Organic chemistry ; Biology ; Cancer Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications
Authors: Feige Zhang | Hairong Zhang | Xin Wang | Peng Chen | Yan-Fei Hu | Xiaoyi Zhang | Yaru Zhao
Source: Scientific Reports (article, 2020-04-08)
Field: Materials Science — Materials Chemistry

Probing the structural evolution and electronic properties of divalent metal Be2Mgn clusters from small to medium-size Bimetallic clusters have aroused increased attention because of the ability to tune their own properties by changing size, shape, and doping. In present work, a structural search of the global minimum for divalent bimetal Be<sub>2</sub>Mg<sub>n</sub> (n = 1-20) clusters are performed by utilizing CALYPSO structural searching method with subsequent DFT optimization. We investigate the evolution of geometries, electronic properties, and nature of bonding from small to medium-sized clusters. It is found that the structural transition from hollow 3D structures to filled cage-like frameworks emerges at n = 10 for Be<sub>2</sub>Mg<sub>n</sub> clusters, which is obviously earlier than that of Mg<sub>n</sub> clusters. The Be atoms prefer the surface sites in small cluster size, then one Be atom tend to embed itself inside the magnesium motif. At the number of Mg larger than eighteen, two Be atoms have been completely encapsulated by caged magnesium frameworks. In all Be<sub>2</sub>Mg<sub>n</sub> clusters, the partial charge transfer from Mg to Be takes place. An increase in the occupations of the Be-2p and Mg-3p orbitals reveals the increasing metallic behavior of Be<sub>2</sub>Mg<sub>n</sub> clusters. The analysis of stability shows that the cluster stability can be enhanced by Be atoms doping and the Be<sub>2</sub>Mg<sub>8</sub> cluster possesses robust stability across the cluster size range of n = 1-20. There is s-p hybridization between the Be and Mg atoms leading to stronger Be-Mg bonds in Be<sub>2</sub>Mg<sub>8</sub> cluster. This finding is supported by the multi-center bonds and Mayer bond order analysis. Cluster (spacecraft) ; Bimetallic strip ; Divalent ; Crystallography ; Atom (system on chip) ; Magnesium ; Cluster size ; Metal ; Materials science ; Divalent metal ; Chemical physics ; Bimetal ; Molecular dynamics ; Chemistry ; Computational chemistry ; Physical chemistry Boron and Carbon Nanomaterials Research ; Superconductivity in MgB2 and Alloys ; MXene and MAX Phase Materials
Authors: Abhilash Pullanchiyodan | Libu Manjakkal | Saoirse Dervin | Dhayalan Shakthivel | Ravinder Dahiya
Source: Advanced Materials Technologies (article, 2020-02-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metal Coated Conductive Fabrics with Graphite Electrodes and Biocompatible Gel Electrolyte for Wearable Supercapacitors Abstract Fabric‐based supercapacitors have received considerable interest as energy storage devices for wearable systems. This work demonstrates the use of metal coated fabrics as the active material and current collector with nontoxic polyvinyl alcohol (PVA)‐KCl gel electrolyte for wearable supercapacitors (SCs). To evaluate the influence of the metal coating, the electrochemical and capacitive studies are carried out and results are compared with a newly developed metal free graphite printed textile (cellulose‐polyester) (CP‐Gr) based SC. It is evident that the homemade graphite paste electrode printed on the top of Armor FR (Ni/Cu coated polyester fabric) (AFR‐Gr) and Nora Dell (Ni/Cu/Ag coated polyamide) (ND‐Gr) based SCs with PVA‐KCl electrolyte exhibits the specific capacitance of 99.06 and 46.88 mF cm −2 , respectively, at sweep rate of 5 mV s −1 . These values are 24 and 52 times greater than that of CP‐Gr based SC. The AFR‐Gr and ND‐Gr based SCs have an excellent energy density of 8.81 and 4.17 µWh cm −2 , respectively, at 5 mV s −1 . The fabricated ND‐Gr based SC gives a stable response for more than 5000 charging/discharging cycles. Finally, the nontoxic nature of the PVA‐KCl gel electrolyte is evaluated and confirmed through in vitro cytocompatibility assessment with adult human dermal fibroblasts cells for wearable applications. Materials science ; Supercapacitor ; Electrolyte ; Polyvinyl alcohol ; Polyester ; Electrode ; Graphite ; Current collector ; Chemical engineering ; Composite material ; Capacitance ; Electrochemistry ; Polyamide ; Chemistry Supercapacitor Materials and Fabrication ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Büşra Uzun | Ömer Cívalek | Mustafa Özgür Yaylı
Source: Mechanics Based Design of Structures and Machines (article, 2020-11-11)
Field: Materials Science — Materials Chemistry

Vibration of FG nano-sized beams embedded in Winkler elastic foundation and with various boundary conditions In the current study, vibration analysis of functionally graded (FG) nano-sized beams resting on a elastic foundation is presented via a finite element method. The elastic foundation is simulated by using one-parameter Winkler type elastic foundation model. Euler-Bernoulli beam theory and Eringen’s nonlocal elasticity theory are utilized to model the functionally graded nano-sized beams with various boundary conditions such as simply supported at both ends (S-S), clamped-clamped (C-C) and clamped-simply supported (C-S). Material properties of functionally graded nanobeam vary across the thickness direction according to the power-law distribution. The vibration behaviors of functionally graded nanobeam composed of alumina (Al2O3) and steel are shown using nonlocal finite element formulation. The importance of this paper is the utilize of shape functions and the Eringen's nonlocal elasticity theory to set up the stiffness matrices and mass matrices of the functionally graded nano-sized beam resting on Winkler elastic foundation for free vibration analysis. Bending stiffness, foundation stiffness and mass matrices are obtained to realize the solution of vibration problem of the FG nanobeam. The influences of power-law exponent (k), dimensionless nonlocal parameters (e0a/L), dimensionless Winkler foundation parameters (KW), mode numbers and boundary conditions on frequencies are investigated via several numerical examples and shown by a number of tables and figures. Vibration ; Boundary value problem ; Stiffness ; Dimensionless quantity ; Timoshenko beam theory ; Beam (structure) ; Elasticity (physics) ; Bending stiffness ; Finite element method ; Materials science ; Material properties ; Normal mode ; Mathematical analysis ; Structural engineering ; Mechanics ; Physics ; Mathematics ; Composite material ; Engineering ; Acoustics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Ryou Kubota | Kazutoshi Nagao | Wataru Tanaka | Ryotaro Matsumura | Takuma Aoyama | Kenji Urayama | Itaru Hamachi
Source: Nature Communications (article, 2020-08-14)
Field: Materials Science — Biomaterials

Control of seed formation allows two distinct self-sorting patterns of supramolecular nanofibers Self-sorting double network hydrogels comprising orthogonal supramolecular nanofibers have attracted attention as artificially-regulated multi-component systems. Regulation of network patterns of self-sorted nanofibers is considered as a key for potential applications such as optoelectronics, but still challenging owing to a lack of useful methods to prepare and analyze the network patterns. Herein, we describe the selective construction of two distinct self-sorting network patterns, interpenetrated and parallel, by controlling the kinetics of seed formation with dynamic covalent oxime chemistry. Confocal imaging reveals the interpenetrated self-sorting network was formed upon addition of O-benzylhydroxylamine to a benzaldehyde-tethered peptide-type hydrogelator in the presence of lipid-type nanofibers. We also succeed in construction of a parallel self-sorting network through deceleration of seed formation using a slow oxime exchange reaction. Through careful observation, the formation of peptide-type seeds and nanofibers is shown to predominantly occur on the surface of the lipid-type nanofibers via highly dynamic and thermally-fluctuated processes. Nanofiber ; Supramolecular chemistry ; Sorting ; Nanotechnology ; Materials science ; Biological system ; Chemistry ; Computer science ; Molecule ; Biology ; Organic chemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Luminescence and Fluorescent Materials