jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sujay Bagi | Shuai Yuan | Sergio Rojas‐Buzo | Yang Shao‐Horn | Yuriy Román‐Leshkov
Source: Green Chemistry (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

A continuous flow chemistry approach for the ultrafast and low-cost synthesis of MOF-808 The continous flow reactor can synthesize MOF-808—an industrially relevant MOF—in 5 minutes compared to 48 hours in Batch, while maintaining crystallinity and porosity of the final product. Crystallinity ; Porosity ; Chemistry ; Continuous flow ; Chemical engineering ; Flow chemistry ; Product (mathematics) ; Flow (mathematics) ; Process engineering ; Organic chemistry ; Biochemical engineering ; Catalysis ; Mechanics ; Mathematics ; Physics ; Engineering ; Crystallography Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Yewei Yu | Chen Zhang | Yifan Wang | Miaolei Zhou
Source: IEEE/ASME Transactions on Mechatronics (article, 2021-04-22)
Field: Materials Science — Materials Chemistry

Neural-Network-Based Iterative Learning Control for Hysteresis in a Magnetic Shape Memory Alloy Actuator The typical characteristic of a magnetic shape memory alloy (MSMA)-based actuator is rate-dependent and stress-dependent hysteresis. In addition, the hysteresis in an MSMA-based actuator includes memory, multivalued mapping, and asymmetry properties, which seriously degrade the positioning accuracy. In this article, a feedforward neural network (FNN)-based nonlinear autoregressive moving average with exogenous inputs (NARMAX) model is employed to describe the hysteresis in an MSMA-based actuator. Through a Taylor expansion, it is revealed how the FNN constructs the nonlinear function of the NARMAX model. For the controller implementation, an FNN-based iterative learning control (ILC) strategy is proposed to achieve the desired tracking performance. The contributions of this study include exploring the update rule of the FNN-based ILC and analyzing the convergence properties of the proposed controller when the initial state of the system varies. To verify the effectiveness of the proposed method, experiments on an MSMA-based actuator were conducted. The results illustrate that the proposed modeling and control methods achieve an excellent performance and that the theoretical results are correct. Control theory (sociology) ; Actuator ; Nonlinear system ; Artificial neural network ; Hysteresis ; Controller (irrigation) ; Convergence (economics) ; Computer science ; Trajectory ; Nonlinear autoregressive exogenous model ; Iterative learning control ; Control engineering ; Feed forward ; Engineering ; Artificial intelligence ; Control (management) ; Physics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Yifei Xue | Wenliang Ji | Ying Jiang | Ping Yu | Lanqun Mao
Source: Angewandte Chemie International Edition (article, 2021-08-19)
Field: Chemistry — Electrochemistry

Deep Learning for Voltammetric Sensing in a Living Animal Brain Numerous neurochemicals have been implicated in the modulation of brain function, making them appealing analytes for sensors and diagnostics. However, it is a grand challenge to selectively measure multiple neurochemicals simultaneously in vivo because of their great variations in concentrations, dynamic nature, and composition. Herein, we present a deep learning-based voltammetric sensing platform for the highly selective and simultaneous analysis of three neurochemicals in a living animal brain. The system features a carbon fiber electrode capable of capturing the mixed dynamics of a neurotransmitter, neuromodulator, and ions. Then a powerful deep neural network is employed to resolve individual chemical and spatial-temporal information. With this, a single electrochemical measurement reveals an interplaying concentration changes of dopamine, ascorbate, and ions in living rat brain, which is unobtainable with existing analytical methodologies. Our strategy provides a powerful means to expedite research in neuroscience and empower sensing-aided diagnostic applications. Brain function ; Neuroscience ; Computer science ; Nanotechnology ; Artificial intelligence ; Materials science ; Psychology Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Marzieh Moosavi‐Nasab | Sara Khoshnoudi‐Nia | Zohreh Azimifar | Shima Kamyab
Source: Scientific Reports (article, 2021-03-03)
Field: Chemistry — Analytical Chemistry

Evaluation of the total volatile basic nitrogen (TVB-N) content in fish fillets using hyperspectral imaging coupled with deep learning neural network and meta-analysis Recently, hyperspectral-imaging (HSI), as a rapid and non-destructive technique, has generated much interest due to its unique potential to monitor food quality and safety. The specific aim of the study is to investigate the potential of the HSI (430-1010 nm) coupled with Linear Deep Neural Network (LDNN) to predict the TVB-N content of rainbow trout fillet during 12 days storage at 4 ± 2 °C. After the acquisition of hyperspectral images, the TVB-N content of fish fillets was obtained by a conventional method (micro-Kjeldahl distillation). To simplify the calibration models, nine optimal wavelengths were selected by the successive projections algorithm. A seven layers LDNN was designed to estimate the TVB-N content of samples. The LDNN model showed acceptable performance for prediction of TVB-N content of fish fillet (R<sup>2</sup>p = 0.853; RSMEP = 3.159 and RDP = 3.001). The performance of LDNN model was comparable with the results of previous works. Although, the results of the meta-analysis did not show any significant difference between various chemometric models. However, the least-squares support vector machine algorithm showed better prediction results as compared to the other models (RMSEP: 2.63 and R<sup>2</sup><sub>p</sub> = 0.897). Further studies are required to improve the prediction power of the deep learning model for prediction of rainbow-trout fish quality. Hyperspectral imaging ; Fish <Actinopterygii> ; Artificial neural network ; Nitrogen ; Computer science ; Food science ; Artificial intelligence ; Chemistry ; Pattern recognition (psychology) ; Biology ; Fishery ; Organic chemistry Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Water Quality Monitoring and Analysis
Authors: Senyang Liu | Xiaohu Liu | Jinying Yuan | Jie Bao
Source: Research (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Multidimensional Information Encryption and Storage: When the Input Is Light The issue of information security is closely related to every aspect of daily life. For pursuing a higher level of security, much effort has been continuously invested in the development of information security technologies based on encryption and storage. Current approaches using single-dimension information can be easily cracked and imitated due to the lack of sufficient security. Multidimensional information encryption and storage are an effective way to increase the security level and can protect it from counterfeiting and illegal decryption. Since light has rich dimensions (wavelength, duration, phase, polarization, depth, and power) and synergy between different dimensions, light as the input is one of the promising candidates for improving the level of information security. In this review, based on six different dimensional features of the input light, we mainly summarize the implementation methods of multidimensional information encryption and storage including material preparation and response mechanisms. In addition, the challenges and future prospects of these information security systems are discussed. Encryption ; Computer science ; Information security ; Computer security ; Dimension (graph theory) ; Information storage ; Mathematics ; Database Luminescence and Fluorescent Materials ; Advanced Memory and Neural Computing ; Photochromic and Fluorescence Chemistry
Authors: Siqiang Fang | Jian‐Ping Tan | Jianke Pan | Hongkui Zhang | Yuan Chen | Xiaoyu Ren | Tianli Wang
Source: Angewandte Chemie International Edition (article, 2021-03-17)
Field: Chemistry — Organic Chemistry

Enantiodivergent Kinetic Resolution of 1,1′‐Biaryl‐2,2′‐Diols and Amino Alcohols by Dipeptide‐Phosphonium Salt Catalysis Inspired by the Atherton–Todd Reaction A highly enantiodivergent organocatalytic method is disclosed for the synthesis of atropisomeric biaryls via kinetic resolution inspired by a dipeptide-phosphonium salt-catalyzed Atherton-Todd (A-T) reaction. This flexible approach led to both R- and S-enantiomers by fine-tuning of bifunctional phosphonium with excellent selectivity factors (s) of up to 1057 and 525, respectively. The potential of newly synthesized O-phosphorylated biaryl diols was illustrated by the synthesis of axially chiral organophosphorus compounds. Mechanistic investigations suggest that the bifunctional phosphonium halide catalyst differentiates between the in-situ-generated P-species in the A-T process, mainly involving phosphoryl chloride and phosphoric anhydride, thus leading to highly enantiodivergent O-phosphorylation reactions. Furthermore hydrogen bonding interactions between the catalysts and phosphorus molecules were crucial in asymmetric induction. Phosphonium ; Chemistry ; Kinetic resolution ; Bifunctional ; Phosphonium salt ; Dipeptide ; Catalysis ; Enantiomer ; Enantioselective synthesis ; Combinatorial chemistry ; Organocatalysis ; Organic chemistry ; Amino acid Axial and Atropisomeric Chirality Synthesis ; Molecular spectroscopy and chirality ; Chemical synthesis and alkaloids
Authors: Haopeng Cai | Chaochao Feng | Hongbo Xiao | Bowen Cheng
Source: Journal of Alloys and Compounds (article, 2021-10-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis of Fe3O4/rGO@PANI with three-dimensional flower-like nanostructure and microwave absorption properties Materials science ; Reflection loss ; Graphene ; Microwave ; Polyaniline ; Composite number ; Oxide ; Absorption (acoustics) ; Ternary operation ; Impedance matching ; Nanostructure ; Composite material ; Chemical engineering ; Nanotechnology ; Electrical impedance ; Polymerization ; Polymer ; Metallurgy Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Jia Chen | Shengke Li | Zeyu Wang | Yating Pan | Jianwen Wei | Siyu Lu | Qingwen Zhang | Lianhui Wang | Ruibing Wang
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of an AIEgen functionalized cucurbit[7]uril for subcellular bioimaging and synergistic photodynamic therapy and supramolecular chemotherapy Aggregation-induced emission (AIE) based fluorophores (AIEgens) have attracted increasing attention for biomedical applications due to their unique optical properties. Here we report an AIE photosensitizer functionalized CB[7], namely AIECB[7], which could spontaneously self-assemble into nanoaggregates in aqueous solutions. Interestingly, the carbonyl-lace of CB[7] may potentially act as a proton acceptor in an acidic environment to fine-tune the fluorescence and singlet oxygen generation of AIECB[7] nanoaggregates by regulating the inner stacking of AIEgens. Additionally, benefiting from the guest-binding properties of CB[7], oxaliplatin was included into AIECB[7] nanoaggregates for combined photodynamic therapy and supramolecular chemotherapy. To show the modular versatility of this supramolecular system, a hypoxia-activatable prodrug banoxantrone (AQ4N) was loaded into AIECB[7] nanoaggregates, which exhibited synergistic antitumor effects on a multicellular tumor spheroid model (MCTS). This work not only provides AIECB[7] for versatile theranostic applications, but also offers important new insights into the design and development of macrocycle-conjugated AIE materials for diverse biomedical applications. Singlet oxygen ; Photodynamic therapy ; Photosensitizer ; Supramolecular chemistry ; Prodrug ; Chemistry ; Stacking ; Combinatorial chemistry ; Nanotechnology ; Photochemistry ; Materials science ; Oxygen ; Organic chemistry ; Molecule ; Biochemistry Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes ; Mass Spectrometry Techniques and Applications
Authors: Sarika Jadhav | Ramchandra S. Kalubarme | Norihiro Suzuki | Chiaki Terashima | Junyoung Mun | Bharat B. Kale | Suresh Gosavi | Akira Fujishima
Source: ACS Omega (article, 2021-02-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Cobalt-Doped Manganese Dioxide Hierarchical Nanostructures for Enhancing Pseudocapacitive Properties Herein, overall improvement in the electrochemical performance of manganese dioxide is achieved through fine-tuning the microstructure of partially Co-doped manganese dioxide nanomaterial using facile hydrothermal method with precise control of preparative parameters. The structural investigation exhibits formation of a multiphase compound accompanied by controlled reflections of α-MnO<sub>2</sub> as well as γ-MnO<sub>2</sub> crystalline phases. The morphological examination manifests the presence of MnO<sub>2</sub> nanowires having a width of 70-80 nm and a length of several microns. The Co-doped manganese dioxide electrode displayed a particular capacitive behavior along with a rising order of capacitance concerning with increased cobalt ion concentration suitable for certain limits. The value of specific capacitance achieved by a 5% Co-doped manganese dioxide sample was 1050 F g<sup>-1</sup> at 0.5 A g<sup>-1</sup>, which was nearly threefold greater than that achieved by a bare manganese dioxide electrode. Furthermore, Co-doped manganese dioxide nanocomposite electrode exhibits exceptional capacitance retention (92.7%) till 10,000 cycles. It shows the good cyclability as well as stability of the material. Furthermore, we have demonstrated the solid-state supercapacitor with good energy and power density. Manganese ; Materials science ; Supercapacitor ; Cobalt ; Nanocomposite ; Capacitance ; Electrode ; Chemical engineering ; Electrochemistry ; Doping ; Nanomaterials ; Nanotechnology ; Optoelectronics ; Chemistry ; Metallurgy Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Advancements in Battery Materials
Authors: Guillaume Combes | Ana‐Marija Vučković | Martina Perić Bakulić | Rodolphe Antoine | Vlasta Bonačić‐Koutecký | Katarina Trajković
Source: Cancers (review, 2021-08-21)
Field: Materials Science — Materials Chemistry

Nanotechnology in Tumor Biomarker Detection: The Potential of Liganded Nanoclusters as Nonlinear Optical Contrast Agents for Molecular Diagnostics of Cancer Cancer is one of the leading causes of premature death, and, as such, it can be prevented by developing strategies for early and accurate diagnosis. Cancer diagnostics has evolved from the macroscopic detection of malignant tissues to the fine analysis of tumor biomarkers using personalized medicine approaches. Recently, various nanomaterials have been introduced into the molecular diagnostics of cancer. This has resulted in a number of tumor biomarkers that have been detected in vitro and in vivo using nanodevices and corresponding imaging techniques. Atomically precise ligand-protected noble metal quantum nanoclusters represent an interesting class of nanomaterials with a great potential for the detection of tumor biomarkers. They are characterized by high biocompatibility, low toxicity, and suitability for controlled functionalization with moieties specifically recognizing tumor biomarkers. Their non-linear optical properties are of particular importance as they enable the visualization of nanocluster-labeled tumor biomarkers using non-linear optical techniques such as two-photon-excited fluorescence and second harmonic generation. This article reviews liganded nanoclusters among the different nanomaterials used for molecular cancer diagnosis and the relevance of this new class of nanomaterials as non-linear optical probe and contrast agents. Nanoclusters ; Nanotechnology ; Nanomaterials ; Biocompatibility ; Materials science ; Cancer ; Cancer detection ; Biomarker ; Cancer biomarkers ; Chemistry ; Medicine Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Toby Lewis‐Atwell | Piers A. Townsend | Matthew N. Grayson
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2021-12-30)
Field: Materials Science — Materials Chemistry

Machine learning activation energies of chemical reactions Abstract Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar. This article is categorized under: Structure and Mechanism &gt; Reaction Mechanisms and Catalysis Computer and Information Science &gt; Visualization Activation energy ; Homogeneous ; Chemical reaction ; Reaction mechanism ; Chemistry ; Computer science ; Catalysis ; Field (mathematics) ; Computational chemistry ; Biological system ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Statistical physics ; Physical chemistry ; Organic chemistry ; Mathematics ; Physics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Joohwi Lee | Ryoji Asahi
Source: Computational Materials Science (article, 2021-01-29)
Field: Materials Science — Materials Chemistry

Transfer learning for materials informatics using crystal graph convolutional neural network Convolutional neural network ; Computer science ; Big data ; Transfer of learning ; Graph ; Crystal (programming language) ; Materials informatics ; Deep learning ; Artificial neural network ; Regression ; Artificial intelligence ; Materials science ; Machine learning ; Data mining ; Theoretical computer science ; Mathematics ; Statistics ; Health informatics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Stefan Heinen | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: The Journal of Chemical Physics (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space The interplay of kinetics and thermodynamics governs reactive processes, and their control is key in synthesis efforts. While sophisticated numerical methods for studying equilibrium states have well advanced, quantitative predictions of kinetic behavior remain challenging. We introduce a reactant-to-barrier (R2B) machine learning model that rapidly and accurately infers activation energies and transition state geometries throughout the chemical compound space. R2B exhibits improving accuracy as training set sizes grow and requires as input solely the molecular graph of the reactant and the information of the reaction type. We provide numerical evidence for the applicability of R2B for two competing text-book reactions relevant to organic synthesis, E2 and S<sub>N</sub>2, trained and tested on chemically diverse quantum data from the literature. After training on 1-1.8k examples, R2B predicts activation energies on average within less than 2.5 kcal/mol with respect to the coupled-cluster singles doubles reference within milliseconds. Principal component analysis of kernel matrices reveals the hierarchy of the multiple scales underpinning reactivity in chemical space: Nucleophiles and leaving groups, substituents, and pairwise substituent combinations correspond to systematic lowering of eigenvalues. Analysis of R2B based predictions of ∼11.5k E2 and S<sub>N</sub>2 barriers in the gas-phase for previously undocumented reactants indicates that on average, E2 is favored in 75% of all cases and that S<sub>N</sub>2 becomes likely for chlorine as nucleophile/leaving group and for substituents consisting of hydrogen or electron-withdrawing groups. Experimental reaction design from first principles is enabled due to R2B, which is demonstrated by the construction of decision trees. Numerical R2B based results for interatomic distances and angles of reactant and transition state geometries suggest that Hammond's postulate is applicable to S<sub>N</sub>2, but not to E2. Chemical space ; SN2 reaction ; Chemistry ; Nucleophile ; Substituent ; Computational chemistry ; Eigenvalues and eigenvectors ; Reaction coordinate ; Physics ; Stereochemistry ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Kobi Felton | Jan G. Rittig | Alexei A. Lapkin
Source: Chemistry - Methods (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Summit: Benchmarking Machine Learning Methods for Reaction Optimisation Abstract In the fine chemicals industry, reaction screening and optimisation are essential to development of new products. However, this screening can be extremely time and labor intensive, especially when intuition is used. Machine learning offers a solution through iterative suggestions of new experiments based on past experimental data, but knowing which machine learning strategy to apply in a particular case is still difficult. Here, we develop chemically‐motivated virtual benchmarks for reaction optimisation and compare several strategies on these benchmarks. The benchmarks and strategies are encompassed in an open‐source framework named Summit. The results of our tests show that Bayesian optimisation strategies perform very well across the types of problems faced in chemical reaction optimisation, while many strategies commonly used in reaction optimisation fail to find optimal solutions. Benchmarking ; Computer science ; Intuition ; Summit ; Machine learning ; Artificial intelligence ; Bayesian probability ; Bayesian optimization Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation