jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Y. G. Liang | S. Lee | H. S. Yu | H. R. Zhang | Y. J. Liang | P. Y. Zavalij | X. Chen | R. D. James | L. A. Bendersky | A. V. Davydov | X. H. Zhang | I. Takeuchi
Source: Nature Communications (article, 2020-07-15)
Field: Materials Science — Materials Chemistry

Tuning the hysteresis of a metal-insulator transition via lattice compatibility Structural phase transitions serve as the basis for many functional applications including shape memory alloys (SMAs), switches based on metal-insulator transitions (MITs), etc. In such materials, lattice incompatibility between transformed and parent phases often results in a thermal hysteresis, which is intimately tied to degradation of reversibility of the transformation. The non-linear theory of martensite suggests that the hysteresis of a martensitic phase transformation is solely determined by the lattice constants, and the conditions proposed for geometrical compatibility have been successfully applied to minimizing the hysteresis in SMAs. Here, we apply the non-linear theory to a correlated oxide system (V<sub>1-x</sub>W<sub>x</sub>O<sub>2</sub>), and show that the hysteresis of the MIT in the system can be directly tuned by adjusting the lattice constants of the phases. The results underscore the profound influence structural compatibility has on intrinsic electronic properties, and indicate that the theory provides a universal guidance for optimizing phase transforming materials. Compatibility (geochemistry) ; Hysteresis ; Martensite ; Phase transition ; Lattice (music) ; Shape-memory alloy Shape Memory Alloy Transformations ; Transition Metal Oxide Nanomaterials ; Ferroelectric and Piezoelectric Materials
Authors: Wujie Wang | Tzuhsiung Yang | William H. Harris | Rafael Gómez‐Bombarelli
Source: Chemical Communications (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids Solvate ionic liquids (SIL) have promising applications as electrolyte materials. Despite the broad design space of oligoether ligands, most reported SILs are based on simple tri- and tetraglyme. Here, we describe a computational search for complex ethers that can better stabilize SILs. Through active learning, a neural network interatomic potential is trained from density functional theory data. The learned potential fulfills two key requirements: transferability across composition space, and high speed and accuracy to find low-energy ligand-ion poses across configurational space. Candidate ether ligands for Li<sup>+</sup>, Mg<sup>2+</sup> and Na<sup>+</sup> SILs with higher binding affinity and electrochemical stability than the reference compounds are identified. Lastly, their properties are related to the geometry of the coordination sphere. Ionic liquid ; Ether ; Ionic bonding ; Artificial neural network ; Chemistry ; Combinatorial chemistry ; Computer science ; Ion ; Organic chemistry ; Machine learning ; Catalysis Machine Learning in Materials Science ; Ionic liquids properties and applications ; Electronic and Structural Properties of Oxides
Authors: Chengyuan Wen | Binghan Liu | Josh Wolfgang | Timothy E. Long | Roy Odle | Shengfeng Cheng
Source: Journal of Polymer Science (article, 2020-04-13)
Field: Materials Science — Polymers and Plastics

Determination of glass transition temperature of polyimides from atomistic molecular dynamics simulations and<scp>machine‐learning</scp>algorithms Abstract Glass transition temperature ( T g ) plays an important role in controlling the mechanical and thermal properties of a polymer. Polyimides as an important category of engineering plastics have wide applications because of their superior heat resistance and mechanical strength. The capability of predicting T g for a polyimide a priori is therefore highly desirable in order to expedite the design and discovery of new polyimide polymers with targeted properties and applications. Here we explore three different approaches to either compute T g for a polyimide via all‐atom molecular dynamics simulations or predict T g via a mathematical model generated by using machine‐learning algorithms to analyze existing data collected from the literature. Our simulations reveal that T g can be determined from examining the diffusion coefficient of simple gas molecules in a polyimide as a function of temperature and the results are comparable to those derived from data on polymer density versus temperature and actually closer to the available experimental data. Furthermore, the predictive model of T g derived with machine‐learning algorithms can be used to estimate T g successfully within an uncertainty of about 20 degrees, even for polyimides yet to be synthesized experimentally. Polyimide ; Glass transition ; Polymer ; Molecular dynamics ; Thermal ; Diffusion ; Molecule Synthesis and properties of polymers ; Material Dynamics and Properties ; Membrane Separation and Gas Transport
Authors: Fan Yang | Chaonan Dai | Jianquan Tang | Jin Xuan | Jun Cao
Source: Process Safety and Environmental Protection (article, 2020-01-21)
Field: Materials Science — Materials Chemistry

A hybrid deep learning and mechanistic kinetics model for the prediction of fluid catalytic cracking performance Artificial neural network ; Fluid catalytic cracking ; Petrochemical ; Process (computing) ; Cracking ; Computer science ; Artificial intelligence ; Engineering ; Chemistry ; Physical chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Mineral Processing and Grinding
Authors: Zachary del Rosario | Matthias Rupp | Yoolhee Kim | Erin Antono | Julia Ling
Source: The Journal of Chemical Physics (article, 2020-07-09)
Field: Materials Science — Materials Chemistry

Assessing the frontier: Active learning, model accuracy, and multi-objective candidate discovery and optimization Discovering novel chemicals and materials can be greatly accelerated by iterative machine learning-informed proposal of candidates-active learning. However, standard global error metrics for model quality are not predictive of discovery performance and can be misleading. We introduce the notion of Pareto shell error to help judge the suitability of a model for proposing candidates. Furthermore, through synthetic cases, an experimental thermoelectric dataset and a computational organic molecule dataset, we probe the relation between acquisition function fidelity and active learning performance. Results suggest novel diagnostic tools, as well as new insights for the acquisition function design. Computer science ; Machine learning ; Fidelity ; Artificial intelligence ; Pareto principle ; Function (biology) ; Active learning (machine learning) ; Data mining ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Zhilong Wang | Yanqiang Han | Jinjin Li | Xiao He
Source: The Journal of Physical Chemistry B (article, 2020-03-25)
Field: Materials Science — Materials Chemistry

Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy Accurate and efficient all-atom quantum mechanical (QM) calculations for biomolecules still present a challenge to computational physicists and chemists. In this study, an extensible generalized molecular fractionation with a conjugate caps method combined with neural networks (NN-GMFCC) is developed for efficient QM calculation of protein energy. In the NN-GMFCC scheme, the total energy of a given protein is calculated by taking a proper combination of the high-precision neural network potential energies of all capped residues and overlapping conjugate caps. In addition, the two-body interaction energies of residue pairs are calculated by molecular mechanics (MM). With reference to the GMFCC/MM calculation at the ωB97XD/6-31G* level, the overall mean unsigned errors of the energy deviations and atomic force root-mean-squared errors calculated by NN-GMFCC are only 2.01 kcal/mol and 0.68 kcal/mol/Å, respectively, for 14 proteins (containing up to 13,728 atoms). Meanwhile, the NN-GMFCC approach is about 4 orders of magnitude faster than the GMFCC/MM method. The NN-GMFCC method could be systematically improved by inclusion of two-body QM interaction and multibody electronic polarization effect. Moreover, the NN-GMFCC approach can also be applied to other macromolecular systems such as DNA/RNA, and it is capable of providing a powerful and efficient approach for exploration of structures and functions of proteins with QM accuracy. Biomolecule ; Conjugate gradient method ; Potential energy ; Artificial neural network ; Chemistry ; Macromolecule ; Biological system ; Computational chemistry ; Physics ; Atomic physics ; Molecular physics ; Computer science ; Algorithm ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: José X. Soares | K. David Wegner | David S.M. Ribeiro | Armindo Melo | Ines Häusler | João L.M. Santos | Ute Resch‐Genger
Source: Nano Research (article, 2020-06-19)
Field: Materials Science — Materials Chemistry

Rationally designed synthesis of bright AgInS2/ZnS quantum dots with emission control Abstract In the blossoming field of Cd-free semiconductor quantum dots (QDs), ternary I-III-VI QDs have received increasing attention due to the ease of the environmentally friendly synthesis of high-quality materials in water, their high photoluminescence (PL) quantum yields (QYs) in the red and near infrared (NIR) region, and their inherently low toxicity. Moreover, their oxygen-insensitive long PL lifetimes of up to several hundreds of nanoseconds close a gap for applications exploiting the compound-specific parameter PL lifetime. To overcome the lack of reproducible synthetic methodologies and to enable a design-based control of their PL properties, we assessed and modelled the synthesis of high-quality MPA-capped AgInS 2 /ZnS (AIS/ZnS) QDs. Systematically refined parameters included reaction time, temperature, Ag:In ratio, S:In ratio, Zn:In ratio, MPA:In ratio, and pH using a design-of-experiment approach. Guidance for the optimization was provided by mathematical models developed for the application-relevant PL parameters, maximum PL wavelength, QY, and PL lifetime as well as the elemental composition in terms of Ag:In:Zn ratio. With these experimental data-based models, MPA:In and Ag:In ratios and pH values were identified as the most important synthesis parameters for PL control and an insight into the connection of these parameters could be gained. Subsequently, the experimental conditions to synthetize QDs with tunable emission and high QY were predicted. The excellent agreement between the predicted and experimentally found PL features confirmed the reliability of our methodology for the rational design of high quality AIS/ZnS QDs with defined PL features. This approach can be straightforwardly extended to other ternary and quaternary QDs and to doped QDs. Photoluminescence ; Quantum yield ; Quantum dot ; Ternary operation ; Materials science ; Nanosecond ; Nanotechnology ; Optoelectronics ; Analytical Chemistry (journal) ; Fluorescence ; Chemistry ; Laser ; Computer science ; Optics ; Physics ; Organic chemistry Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; Advanced Photocatalysis Techniques
Authors: Gulzhian I. Dzhardimalieva | Rose K. Baimuratova | Е. И. Кнерельман | G. I. Davydova | Sarkyt E. Kudaibergenov | Oxana V. Kharissova | Vladimir А. Zhinzhilo | Igor Е. Uflyand
Source: Polymers (article, 2020-05-01)
Field: Chemistry — Inorganic Chemistry

Synthesis of Copper(II) Trimesinate Coordination Polymer and Its Use as a Sorbent for Organic Dyes and a Precursor for Nanostructured Material Several important synthesis pathways for metal-organic frameworks (MOFs) were applied to determine how the synthesis methods and conditions affect the structure and adsorption capacity of the resulting samples. In the present work, three different synthesis routes were used to obtain copper trimesinate coordination polymer: Slow evaporation (A), solvothermal synthesis using a polyethylene glycol (PEG-1500) modulator (B), and green synthesis in water (C). This MOF was characterized by elemental analysis, infrared spectrometry, X-ray diffraction, scanning electron microscopy, thermogravimetry and volumetric nitrogen adsorption/desorption. The samples have permanent porosity and a microporous structure with a large surface area corresponding to the adsorption type I. The obtained MOF was tested as a sorbent to remove organic dyes methylene blue (МВ), Congo red (CR) and methyl violet (MV) as examples. Dye adsorption followed pseudo-first-order kinetics. The equilibrium data were fitted to the Langmuir and Freundlich isotherm models, and the isotherm constants were determined. Thermodynamic parameters, such as changes in the free energy of adsorption (ΔG<sup>0</sup>), enthalpy (ΔH<sup>0</sup>), and entropy (ΔS<sup>0</sup>), were calculated. Thermolysis of copper trimesinate leads to the formation of carbon materials Cu@C with a high purity. Sorbent ; Copper ; Materials science ; Polymer ; Coordination polymer ; Coordination complex ; Organic synthesis ; Chemical engineering ; Polymer chemistry ; Inorganic chemistry ; Organic chemistry ; Chemistry ; Metal ; Metallurgy ; Adsorption ; Composite material ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Pigment Synthesis and Properties ; Polyoxometalates: Synthesis and Applications
Authors: Natácia da Silva e Silva | Fabrício de Souza Farias | Maurício Madson dos Santos Freitas | Enrique Pino−Hernández | Vanderson Vasconcelos Dantas | Marcos Enê Chaves Oliveira | Maria Regina Sarkis Peixoto Joele | Lúcia de Fátima Henriques Lourenço
Source: Food Packaging and Shelf Life (article, 2020-12-04)
Field: Materials Science — Biomaterials

Artificial intelligence application for classification and selection of fish gelatin packaging film produced with incorporation of palm oil and plant essential oils Active packaging ; Food science ; Gelatin ; Palm oil ; Essential oil ; Antimicrobial ; Ultimate tensile strength ; Food packaging ; Materials science ; Biotechnology ; Pulp and paper industry ; Composite material ; Chemistry ; Biology ; Organic chemistry ; Engineering Nanocomposite Films for Food Packaging ; Meat and Animal Product Quality ; Consumer Packaging Perceptions and Trends
Authors: Thomas D. Downes | Simon Jones | Hanna F. Klein | Mary C. Wheldon | Masakazu Atobe | Paul S. Bond | James D. Firth | Ngai S. Chan | Laura Waddelove | Roderick E. Hubbard | David C. Blakemore | Claudia De Fusco | Stephen D. Roughley | Lewis R. Vidler | Maria Ann Whatton | Alison J.‐A. Woolford | Gail L. Wrigley | Peter O’Brien
Source: Chemistry - A European Journal (article, 2020-04-21)
Field: Materials Science — Materials Chemistry

Design and Synthesis of 56 Shape‐Diverse 3D Fragments Fragment-based drug discovery is now widely adopted for lead generation in the pharmaceutical industry. However, fragment screening collections are often predominantly populated with flat, 2D molecules. Herein, we describe a workflow for the design and synthesis of 56 3D disubstituted pyrrolidine and piperidine fragments that occupy under-represented areas of fragment space (as demonstrated by a principal moments of inertia (PMI) analysis). A key, and unique, underpinning design feature of this fragment collection is that assessment of fragment shape and conformational diversity (by considering conformations up to 1.5 kcal mol<sup>-1</sup> above the energy of the global minimum energy conformer) is carried out prior to synthesis and is also used to select targets for synthesis. The 3D fragments were designed to contain suitable synthetic handles for future fragment elaboration. Finally, by comparing our 3D fragments with six commercial libraries, it is clear that our collection has high three-dimensionality and shape diversity. Fragment (logic) ; Workflow ; Pyrrolidine ; Computer science ; Chemistry ; Stereochemistry ; Combinatorial chemistry ; Algorithm ; Database Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Chemical Synthesis and Analysis
Authors: Michael Green | Anh Thi Van Tran | Xiaobo Chen
Source: Composites Science and Technology (article, 2020-07-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Maximizing the microwave absorption performance of polypyrrole by data-driven discovery Polypyrrole ; Reflection loss ; Materials science ; Microwave ; Coating ; Composite number ; Bandwidth (computing) ; Absorption (acoustics) ; Radar ; Computer science ; Composite material ; Optoelectronics ; Nanotechnology ; Telecommunications ; Polymer Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Sarmento J. Mazivila | Ricardo N.M.J. Páscoa | Rafael C. Castro | David S.M. Ribeiro | João L.M. Santos
Source: Talanta (article, 2020-03-20)
Field: Chemistry — Analytical Chemistry

Detection of melamine and sucrose as adulterants in milk powder using near-infrared spectroscopy with DD-SIMCA as one-class classifier and MCR-ALS as a means to provide pure profiles of milk and of both adulterants with forensic evidence: A short communication Chemistry ; Chemometrics ; Melamine ; Analytical Chemistry (journal) ; Chromatography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Melamine detection and toxicity
Authors: Xuefeng Cong | Gu Zhan | Zhenbo Mo | Masayoshi Nishiura | Zhaomin Hou
Source: Journal of the American Chemical Society (article, 2020-03-08)
Field: Chemistry — Organic Chemistry

Diastereodivergent [3 + 2] Annulation of Aromatic Aldimines with Alkenes via C–H Activation by Half-Sandwich Rare-Earth Catalysts Stereodivergent catalysis is of great importance, as it can allow efficient access to all possible stereoisomers of a given product with multiple stereocenters from the same set of starting materials. We report herein the first diastereodivergent [3 + 2] annulation of aromatic aldimines with alkenes via C-H activation by half-sandwich rare-earth catalysts. This protocol provides an efficient and general route for the selective synthesis of both <i>trans</i> and <i>cis</i> diastereoisomers of multisubstituted 1-aminoindanes from the same set of aldimines and alkenes, featuring 100% atom efficiency, excellent diastereoselectivity, broad substrate scope, and good functional group compatibility. The diastereodivergence is achieved by fine-tuning the sterics or ligand/metal combination of the half-sandwich rare-earth metal complexes. Aldimine ; Chemistry ; Annulation ; Catalysis ; Stereocenter ; Steric effects ; Diastereomer ; Combinatorial chemistry ; Ligand (biochemistry) ; Stereochemistry ; Organic chemistry ; Enantioselective synthesis Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Synthesis and Catalytic Reactions
Authors: Jiajing Li | Bing Yu | Zhan Lu
Source: Chinese Journal of Chemistry (article, 2020-09-30)
Field: Chemistry — Organic Chemistry

Chiral Imidazoline Ligands and Their Applications in <scp>Metal‐Catalyzed</scp> Asymmetric Synthesis<sup>†</sup> Abstract As a structural analog of oxazoline, imidazoline (4,5‐dihydroimidazole) has received much attention in the rational design of chiral ligands. The additional N ‐substituent provides broader space for fine‐tuning of electronic and steric effects, and it offers a good handle for immobilizing onto solid supports. In the past decades, imidazoline ring has emerged as a powerful candidate for the design of chiral nitrogen‐containing ligands, as well as a significant alternative for oxazoline ring. Various chiral imidazoline ligands have been designed and utilized in asymmetric organic reactions. These new catalysts can not only be applied in classical reactions, but also be employed to develop new organic reactions with high enantioselectivities. This review provides an overview of chiral imidazoline ligands. Their applications in asymmetric synthesis are also summarized. Chemistry ; Imidazoline receptor ; Oxazoline ; Steric effects ; Substituent ; Ring (chemistry) ; Catalysis ; Combinatorial chemistry ; Electronic effect ; Enantioselective synthesis ; Stereochemistry ; Organic chemistry Synthesis and Catalytic Reactions ; Asymmetric Hydrogenation and Catalysis ; Asymmetric Synthesis and Catalysis
Authors: Ali Mirtaghavi | Andrew K. Baldwin | Nader Tanideh | Moein Zarei | Rajendran Muthuraj | Yuan Cao | Gang Zhao | Junfeng Geng | Hao Jin | Jikui Luo
Source: International Journal of Biological Macromolecules (article, 2020-08-10)
Field: Materials Science — Biomaterials

Crosslinked porous three-dimensional cellulose nanofibers-gelatine biocomposite scaffolds for tissue regeneration Biocomposite ; Biocompatibility ; Materials science ; Chemical engineering ; Nanofiber ; Bacterial cellulose ; Cellulose ; Compressive strength ; Porosity ; Biodegradation ; Composite material ; Chemistry ; Composite number ; Organic chemistry Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Aerogels and thermal insulation
Authors: Daniel T. Yonemoto | Christopher M. Papa | Cédric Mongin | Felix N. Castellano
Source: Journal of the American Chemical Society (article, 2020-06-04)
Field: Materials Science — Materials Chemistry

Thermally Activated Delayed Photoluminescence: Deterministic Control of Excited-State Decay Thermally activated photophysical processes are ubiquitous in numerous organic and metal-organic molecules, leading to chromophores with excited-state properties that can be considered an equilibrium mixture of the available low-lying states. Relative populations of the equilibrated states are governed by temperature. Such molecules have been devised as high quantum yield emitters in modern organic light-emitting diode technology and for deterministic excited-state lifetime control to enhance chemical reactivity in solar energy conversion and photocatalytic schemes. The recent discovery of thermally activated photophysics at CdSe nanocrystal-molecule interfaces enables a new paradigm wherein molecule-quantum dot constructs are used to systematically generate material with predetermined photophysical response and excited-state properties. Semiconductor nanomaterials feature size-tunable energy level engineering, which considerably expands the purview of thermally activated photophysics beyond what is possible using only molecules. This Perspective is intended to provide a nonexhaustive overview of the advances that led to the integration of semiconductor quantum dots in thermally activated delayed photoluminescence (TADPL) schemes and to identify important challenges moving into the future. The initial establishment of excited-state lifetime extension utilizing triplet-triplet excited-state equilibria is detailed. Next, advances involving the rational design of molecules composed of both metal-containing and organic-based chromophores that produce the desired TADPL are described. Finally, the recent introduction of semiconductor nanomaterials into hybrid TADPL constructs is discussed, paving the way toward the realization of fine-tuned deterministic control of excited-state decay. It is envisioned that libraries of synthetically facile composites will be broadly deployed as photosensitizers and light emitters for numerous synthetic and optoelectronic applications in the near future. Chemistry ; Photoluminescence ; Excited state ; State (computer science) ; Atomic physics ; Optoelectronics ; Physics ; Algorithm Luminescence and Fluorescent Materials ; Photoreceptor and optogenetics research ; Organic Light-Emitting Diodes Research