jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Frédéric Dumur
Source: European Polymer Journal (article, 2020-02-13)
Field: Chemistry — Organic Chemistry

Recent advances on pyrene-based photoinitiators of polymerization Photoinitiators of polymerization activable under low light intensity and in the visible range are actively researched by both the academic and industrial communities. To efficiently harvest light and initiate a polymerization process, polyaromatic structures are ideal candidates. In this field, pyrene can be cited as a relevant example. Over the years, a wide range of Type I and Type II photoinitiators have been developed, ranging from push-pull dyes to polyaromatic structures, ferrocenium, ketones, chromones or acridinediones. In this review, an overview of the different pyrene-based photoinitiators reported to date is presented. Especially, a structure-performance relationship will be established with benchmark photoinitiators, evidencing all the interests of this polyaromatic structure. Polymerization ; Photopolymer ; Pyrene ; Photochemistry ; Chemistry ; Materials science ; Polymer science ; Polymer ; Organic chemistry Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; Photochromic and Fluorescence Chemistry
Authors: Peng Wan | Hang Zou | Kelu Wang | Zhengzhi Zhao
Source: Journal of Alloys and Compounds (article, 2020-01-30)
Field: Materials Science — Materials Chemistry

Research on hot deformation behavior of Zr-4 alloy based on PSO-BP artificial neural network Equiaxed crystals ; Materials science ; Isothermal process ; Microstructure ; Deformation (meteorology) ; Strain rate ; Artificial neural network ; Approximation error ; Compression (physics) ; Phase (matter) ; Alloy ; Lath ; Composite material ; Metallurgy ; Thermodynamics ; Artificial intelligence ; Mathematics ; Computer science ; Martensite ; Algorithm Nuclear Materials and Properties ; Metallurgy and Material Forming ; Advanced materials and composites
Authors: Wei Ma | Yongmin Liu
Source: Science China Physics Mechanics and Astronomy (article, 2020-06-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures Deep learning ; Artificial intelligence ; Computer science ; Bottleneck ; Machine learning ; Scheme (mathematics) ; Supervised learning ; Test data ; Data modeling ; Artificial neural network Metamaterials and Metasurfaces Applications ; Animal Vocal Communication and Behavior ; Acoustic Wave Phenomena Research
Authors: Brandon R. Sutherland
Source: Joule (article, 2020-05-01)
Field: Materials Science — Materials Chemistry

Solar Materials Find Their Band Gap Serendipity ; Photovoltaics ; Photovoltaic system ; Nanotechnology ; Computer science ; Chemical space ; Solar cell ; Data science ; Engineering physics ; Engineering ; Materials science ; Physics ; Chemistry ; Electrical engineering ; Drug discovery Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Martin Fitzner | Georg Wuitschik | Raffael Koller | Jean‐Michel Adam | Torsten Schindler | Jean‐Louis Reymond
Source: Chemical Science (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

What can reaction databases teach us about Buchwald–Hartwig cross-couplings? Despite the widespread and increasing usage of Pd-catalyzed C-N cross couplings, finding good conditions for these reactions can be challenging. Practitioners mostly rely on few methodology studies or anecdotal experience. This is surprising, since the advent of data-driven experimentation and the large amount of knowledge in databases allow for data-driven insight. In this work, we address this by analyzing more than 62 000 Buchwald-Hartwig couplings gathered from CAS, Reaxys and the USPTO. Our meta-analysis of the reaction performance generates data-driven cheatsheets for reaction condition recommendation. It also provides an interactive tool to find rarer ligands with optimal performance regarding user-selected substrate properties. With this we give practitioners promising starting points. Furthermore, we study bias and diversity in the literature and summarize the current state of the reaction data, including its pitfalls. Hence, this work will also be useful for future data-driven developments such as the optimization of reaction conditions <i>via</i> machine learning. Chemistry ; Database ; Computer science ; Combinatorial chemistry Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions
Authors: Zhou Lin | Alexander W. Kohn | Troy Van Voorhis
Source: The Journal of Physical Chemistry C (article, 2020-01-20)
Field: Materials Science — Materials Chemistry

Toward Prediction of Nonradiative Decay Pathways in Organic Compounds II: Two Internal Conversion Channels in BODIPYs Boron-dipyrromethene (BODIPY) molecules are widely used as laser dyes and have therefore become a popular research topic within recent years. Numerous studies have been reported for the rational design of BODIPY derivatives based on their photophysical properties, including absorption and fluorescence wavelengths (λabs and λfl), oscillator strength (f), nonradiative pathways, and quantum yield (Φ). In the present work, we illustrate a theoretical, semiempirical model that accurately predicts Φ for various BODIPY compounds on the basis of inexpensive electronic structure calculations, following the data-driven algorithm proposed by us in a previous study [Kohn et al. J. Phys. Chem. C. 2019, 123, 15394]. The model allows us to identify the dominant nonradiative channel of any BODIPY molecule using its structure exclusively and to establish a correlation between the activation energy (Ea) and the fluorescence quantum yield (Φfl). On the basis of our calculations, either the S1 → S0 or La → Lb internal conversion (IC) mechanism dominates in the majority of BODIPY derivatives, depending on the structural and electronic properties of the substituents. In either case, the nonradiative rate (knr) exhibits a straightforward Arrhenius-like relation with the associated Ea. More interestingly, the S1 → S0 mechanism proceeds via a highly distorted intermediate structure in which the core BODIPY plane and the substituent at the 1-position are twisted, while the internal rotation of the very same substituent induces the La → Lb transition. Our model reproduces kfl, knr, and Φfl to mean absolute errors (MAEs) of 0.16 decades, 0.87 decades, and 0.26, when all outliers are considered. These results allow us to validate the predictive power of the proposed data-driven algorithm in Φfl. They also indicate that the model has a great potential to facilitate and accelerate the machine learning aided design of BODIPY dyes for imaging and sensing applications, given sufficient experimental data and appropriate molecular descriptors. BODIPY ; Chemistry ; Substituent ; Internal conversion ; Arrhenius equation ; Quantum yield ; Photochemistry ; Molecule ; Fluorescence ; Oscillator strength ; Computational chemistry ; Activation energy ; Physical chemistry ; Stereochemistry ; Physics ; Electron ; Organic chemistry ; Quantum mechanics ; Spectral line Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Porphyrin and Phthalocyanine Chemistry
Authors: D. Sahoo | Priyanka Priyadarshini | Adyasha Aparimita | D. Alagarasan | R. Ganesan | S. Varadharajaperumal | Ramakanta Naik
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Role of annealing temperature on optimizing the linear and nonlinear optical properties of As<sub>40</sub>Se<sub>50</sub>Ge<sub>10</sub> films The present work shows the effect of annealing conditions on the linear and nonlinear optical properties of two-year-old thermally evaporated 800 nm As<sub>40</sub>Se<sub>50</sub>Ge<sub>10</sub> thin films. The aging effect in this film is clearly noticeable as compared with the old observation. The two-year-old films were annealed at different temperatures like 373, 413, 453 and 493 K for 1 h. The optical parameters were calculated from the optical transmittance and reflectance spectra measured at normal incidence of light by spectrometer in the 500-1000 nm range. The linear refractive index (<i>n</i>) and extinction coefficient (<i>k</i>) were decreased with annealing temperature. The observation reveals the increase in optical band gap with increase in annealing temperature while the width of the tail in the gap has an opposite behaviour. The oscillator energy, dispersion energy, dielectric constant, the loss factor, and optical conductivity were discussed in detail. The nonlinear refractive index and optical susceptibility were calculated by using Miller's formula which decreased with annealing temperature. The changes in both linear and nonlinear optical parameters with annealing temperature showed that annealing temperature can be considered as a useful factor for controlling the optical properties of As<sub>40</sub>Se<sub>50</sub>Ge<sub>10</sub> chalcogenide films which could be the candidate for numerous photonic applications. The structural study was done by X-ray diffraction and Raman spectroscopy. Annealing (glass) ; Refractive index ; Materials science ; Band gap ; Transmittance ; Raman spectroscopy ; Dielectric ; Chalcogenide ; Molar absorptivity ; Temperature coefficient ; Optics ; Atmospheric temperature range ; Analytical Chemistry (journal) ; Optoelectronics ; Chemistry ; Composite material ; Thermodynamics Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Nonlinear Optical Materials Studies
Authors: Hongliang Duan | Ling Wang | Chengyun Zhang | L. Jay Guo | Jianjun Li
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Retrosynthesis with attention-based NMT model and chemical analysis of “wrong” predictions We consider retrosynthesis to be a machine translation problem. Accordingly, we apply an attention-based and completely data-driven model named Tensor2Tensor to a data set comprising approximately 50 000 diverse reactions extracted from the United States patent literature. The model significantly outperforms the seq2seq model (37.4%), with top-1 accuracy reaching 54.1%. We also offer a novel insight into the causes of grammatically invalid SMILES, and conduct a test in which experienced chemists select and analyze the "wrong" predictions that may be chemically plausible but differ from the ground truth. The effectiveness of our model is found to be underestimated and the "true" top-1 accuracy reaches as high as 64.6%. Retrosynthetic analysis ; Set (abstract data type) ; Computer science ; Translation (biology) ; Artificial intelligence ; Machine learning ; Training set ; Data set ; Natural language processing ; Ground truth ; Machine translation ; Chemistry ; Programming language Machine Learning in Materials Science ; RNA and protein synthesis mechanisms ; Machine Learning in Bioinformatics
Authors: Saba Kalantary | Ali Jahani | Reza Jahani
Source: Scientific Reports (article, 2020-05-15)
Field: Materials Science — Biomaterials

MLR and ANN Approaches for Prediction of Synthetic/Natural Nanofibers Diameter in the Environmental and Medical Applications Fiber diameter plays an important role in the properties of electrospinning of nanofibers. However, one major problem is the lack of a comprehensive method that can link processing parameters to nanofibers' diameter. The objective of this study is to develope an artificial neural network (ANN) modeling and multiple regression (MLR) analysis approaches to predict the diameter of nanofibers. Processing parameters, including weight ratio, voltage, injection rate, and distance, were considered as independent variables and the nanofiber diameter as the dependent variable of the ANN model. The results of ANN modeling, especially its high accuracy (R<sup>2</sup> = 0.959) in comparison with MLR results (R<sup>2</sup> = 0.564), introduced the prediction the diameter of nanofibers model (PDNFM) as a comparative model for predicting the diameter of poly (3-caprolactone) (PCL)/gelatin (Gt) nanofibers. According to the result of sensitivity analysis of the model, the values of weight ratio, distance, injection rate, and voltage, respectively, were identified as the most significant parameters which influence PDNFM. Nanofiber ; Electrospinning ; Gelatin ; Materials science ; Artificial neural network ; Linear regression ; Biological system ; Biomedical engineering ; Computer science ; Composite material ; Mathematics ; Statistics ; Artificial intelligence ; Polymer ; Chemistry ; Engineering Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Mikko Mäkelä | Marja Rissanen | Herbert Sixta
Source: Resources Conservation and Recycling (article, 2020-06-20)
Field: Chemistry — Analytical Chemistry

Machine vision estimates the polyester content in recyclable waste textiles Global textile production is mainly based on polyester and cotton fibers. A majority of textiles at the end of their lifecycle are currently landfilled or incinerated, but will be increasingly recycled in the future. Here, we discuss how the polyester content in blended textiles can be estimated based on hyperspectral near infrared imaging with the aim of developing machine vision for textile characterization and recycling. Differences in the textile samples were first visualized based on a principal component model and the polyester contents of individual image pixels were then predicted using image regression. The results showed average prediction errors of 2.2-4.5% within a range of 0-100% polyester and enabled visualizing the spatial changes in the polyester contents of the textiles. We foresee that digitalized tools similar to what we report here will be increasingly important in the future as more emphasis is placed on coordinated collection, sorting and reuse of waste textiles. Polyester ; Textile ; Reuse ; Process engineering ; Hyperspectral imaging ; Sorting ; Textile industry ; Pulp and paper industry ; Computer science ; Environmental science ; Incineration ; Materials science ; Artificial intelligence ; Waste management ; Engineering ; Composite material Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Cultural Heritage Materials Analysis
Authors: Yun Zhang | Xiaojie Xu
Source: Journal of Molecular Graphics and Modelling (article, 2020-11-09)
Field: Materials Science — Materials Chemistry

Machine learning glass transition temperature of styrenic random copolymers Copolymer ; Glass transition ; Transition (genetics) ; Polymer science ; Materials science ; Computer science ; Thermodynamics ; Chemistry ; Polymer ; Composite material ; Physics Material Dynamics and Properties ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Ben. J. Tickner | Olga Semenova | Wissam Iali | Peter J. Rayner | Adrian C. Whitwood | Simon B. Duckett
Source: Catalysis Science & Technology (article, 2020-01-01)
Field: Chemistry — Spectroscopy

Optimisation of pyruvate hyperpolarisation using SABRE by tuning the active magnetisation transfer catalyst Hyperpolarisation techniques such as signal amplification by reversible exchange (SABRE) can deliver NMR signals several orders of magnitude larger than those derived under Boltzmann conditions. SABRE is able to catalytically transfer latent magnetisation from <i>para</i>-hydrogen to a substrate in reversible exchange <i>via</i> temporary associations with an iridium complex. SABRE has recently been applied to the hyperpolarisation of pyruvate, a substrate often used in many <i>in vivo</i> MRI studies. In this work, we seek to optimise the pyruvate-<sup>13</sup>C<sub>2</sub> signal gains delivered through SABRE by fine tuning the properties of the active polarisation transfer catalyst. We present a detailed study of the effects of varying the carbene and sulfoxide ligands on the formation and behaviour of the active [Ir(H)<sub>2</sub>(η<sup>2</sup>-pyruvate)(sulfoxide)(NHC)] catalyst to produce a rationale for achieving high pyruvate signal gains in a cheap and refreshable manner. This optimisation approach allows us to achieve signal enhancements of 2140 and 2125-fold for the 1-<sup>13</sup>C and 2-<sup>13</sup>C sites respectively of sodium pyruvate-1,2-[<sup>13</sup>C<sub>2</sub>]. Catalysis ; Chemistry ; Organic chemistry Advanced NMR Techniques and Applications ; Advanced Battery Materials and Technologies ; NMR spectroscopy and applications
Authors: Guillaume Noirbent | Frédéric Dumur
Source: European Polymer Journal (article, 2020-04-27)
Field: Chemistry — Organic Chemistry

Recent advances on naphthalic anhydrides and 1,8-naphthalimide-based photoinitiators of polymerization Polymerization ; Photopolymer ; Chemistry ; Materials science ; Combinatorial chemistry ; Organic chemistry ; Polymer Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; Dental materials and restorations
Authors: Carla B. Roces | Emily Charlotte Port | Nikolaos N. Daskalakis | Julie A. Watts | Jonathan W. Aylott | Gavin Halbert | Yvonne Perrie
Source: International Journal of Pharmaceutics (article, 2020-07-02)
Field: Materials Science — Biomaterials

Rapid scale-up and production of active-loaded PEGylated liposomes Liposome ; Materials science ; Microfluidics ; Active ingredient ; Doxorubicin ; Nanotechnology ; Biomedical engineering ; Chemistry ; Pharmacology ; Medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Characterization and Applications of Magnetic Nanoparticles
Authors: Daniel Reker | Emily Hoyt | Gonçalo J. L. Bernardes | Tiago Rodrigues
Source: Cell Reports Physical Science (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

Adaptive Optimization of Chemical Reactions with Minimal Experimental Information Optimizing reaction conditions depends on expert chemistry knowledge and laborious exploration of reaction parameters. To automate this task and augment chemical intuition, we here report a computational tool to navigate search spaces. Our approach (LabMate.ML) integrates random sampling of 0.03%–0.04% of all search space as input data with an interpretable, adaptive machine-learning algorithm. LabMate.ML can optimize many real-valued and categorical reaction parameters simultaneously, with minimal computational resources and time. In nine prospective proof-of-concept studies pursuing distinctive objectives, we demonstrate how LabMate.ML can identify optimal goal-oriented conditions for several different chemistries and substrates. Double-blind competitions and the conducted expert surveys reveal that its performance is competitive with that of human experts. LabMate.ML does not require specialized hardware, affords quantitative and interpretable reactivity insights, and autonomously formalizes chemical intuition, thereby providing an innovative framework for informed, automated experiment selection toward the democratization of synthetic chemistry. Intuition ; Computer science ; Categorical variable ; Machine learning ; Chemical space ; Artificial intelligence ; Adaptive sampling ; Proof of concept ; Chemistry ; Mathematics ; Drug discovery Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lagnajit Pattanaik | John Ingraham | Colin A. Grambow | William H. Green
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Generating transition states of isomerization reactions with deep learning Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions. Isomerization ; Transition state ; Chemistry ; Transition (genetics) ; Chemical physics ; Computational chemistry ; Photochemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kai Guo | Markus J. Buehler
Source: Extreme Mechanics Letters (article, 2020-10-10)
Field: Materials Science — Materials Chemistry

A semi-supervised approach to architected materials design using graph neural networks Computer science ; Truss ; Artificial neural network ; Artificial intelligence ; Graph ; Exploit ; Machine learning ; Theoretical computer science ; Engineering ; Structural engineering Machine Learning in Materials Science ; Polymer composites and self-healing ; Topology Optimization in Engineering
Authors: Yixing Wang | Min Zhang | Anqi Lin | Akshay Iyer | Aditya Prasad | Xiaolin Li | Yichi Zhang | Linda S. Schadler | Wei Chen | L. Catherine Brinson
Source: Molecular Systems Design & Engineering (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Mining structure–property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks In this paper, a data driven and deep learning approach for modeling structure–property relationship of polymer nanocomposites is demonstrated. This method is applicable to understand other material mechanisms and guide the design of material with targeted performance. Convolutional neural network ; Property (philosophy) ; Finite element method ; Nanocomposite ; Task (project management) ; Computer science ; Deep learning ; Materials science ; Element (criminal law) ; Artificial intelligence ; Nanotechnology ; Engineering ; Structural engineering ; Systems engineering Polymer Nanocomposites and Properties ; Asphalt Pavement Performance Evaluation ; Polymer crystallization and properties