jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nikolaos V. Tzouras | Ekaterina A. Martynova | Xinyuan Ma | Thomas Scattolin | Benjamin Hupp | Hendrik Busen | Marina Saab | Ziyun Zhang | Laura Falivene | Gianmarco Pisanò | Kristof Van Hecke | Luigi Cavallo | Catherine S. J. Cazin | Andreas Steffen | Steven P. Nolan
Source: Chemistry - A European Journal (article, 2021-05-26)
Field: Chemistry — Organic Chemistry

Simple Synthetic Routes to Carbene‐M‐Amido (M=Cu, Ag, Au) Complexes for Luminescence and Photocatalysis Applications The development of novel and operationally simple synthetic routes to carbene-metal-amido (CMA) complexes of copper, silver and gold relevant for photonic applications are reported. A mild base and sustainable solvents allow all reactions to be conducted in air and at room temperature, leading to high yields of the targeted compounds even on multigram scales. The effect of various mild bases on the N-H metallation was studied in silico and experimentally, while a mechanochemical, solvent-free synthetic approach was also developed. Our photophysical studies on [M(NHC)(Cbz)] (Cbz=carbazolyl) indicate that the occurrence of fluorescent or phosphorescent states is determined primarily by the metal, providing control over the excited state properties. Consequently, we demonstrate the potential of the new CMAs beyond luminescence applications by employing a selected CMA as a photocatalyst. The exemplified synthetic ease is expected to accelerate the applications of CMAs in photocatalysis and materials chemistry. Phosphorescence ; Carbene ; Photocatalysis ; Luminescence ; Chemistry ; Combinatorial chemistry ; Solvent ; Metal ; Photochemistry ; Nanotechnology ; Fluorescence ; Materials science ; Catalysis ; Organic chemistry ; Optoelectronics N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions ; Organoboron and organosilicon chemistry
Authors: Vivek Revi | Saurabh Kasodariya | Anjana Talapatra | Ghanshyam Pilania | Alankar Alankar
Source: Computational Materials Science (article, 2021-07-08)
Field: Materials Science — Materials Chemistry

Machine learning elastic constants of multi-component alloys Component (thermodynamics) ; Materials science ; Constant (computer programming) ; Thermodynamics ; Computer science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Chi Chen | Shyue Ping Ong
Source: npj Computational Materials (article, 2021-10-22)
Field: Materials Science — Materials Chemistry

AtomSets as a hierarchical transfer learning framework for small and large materials datasets Abstract Predicting properties from a material’s composition or structure is of great interest for materials design. Deep learning has recently garnered considerable interest in materials predictive tasks with low model errors when dealing with large materials data. However, deep learning models suffer in the small data regime that is common in materials science. Here we develop the AtomSets framework, which utilizes universal compositional and structural descriptors extracted from pre-trained graph network deep learning models with standard multi-layer perceptrons to achieve consistently high model accuracy for both small compositional data (&lt;400) and large structural data (&gt;130,000). The AtomSets models show lower errors than the graph network models at small data limits and other non-deep-learning models at large data limits. They also transfer better in a simulated materials discovery process where the targeted materials have property values out of the training data limits. The models require minimal domain knowledge inputs and are free from feature engineering. The presented AtomSets model framework can potentially accelerate machine learning-assisted materials design and discovery with less data restriction. Transfer of learning ; Computer science ; Data science ; Artificial intelligence Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Xingang Zhao | Kun Zhou | Bangyu Xing | Ruoting Zhao | Shulin Luo | Tianshu Li | Yuanhui Sun | Guangren Na | Jiahao Xie | Xiaoyu Yang | Xinjiang Wang | Xiaoyu Wang | Xin He | Jian Lv | Yuhao Fu | Lijun Zhang
Source: Science Bulletin (article, 2021-06-15)
Field: Materials Science — Materials Chemistry

JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics Materials informatics ; Computer science ; Automation ; Informatics ; Exploit ; Artificial intelligence ; Python (programming language) ; Data science ; Software engineering ; Systems engineering ; Health informatics ; Engineering ; Engineering informatics ; Programming language Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Jiaji Wang | Chen Wang | Jian‐Sheng Fan | Y. L. Mo
Source: Journal of Computational Physics (article, 2021-10-18)
Field: Materials Science — Metals and Alloys

A deep learning framework for constitutive modeling based on temporal convolutional network Constitutive equation ; Convolutional neural network ; Computer science ; Deep learning ; Convolution (computer science) ; Dimension (graph theory) ; Kernel (algebra) ; Algorithm ; Stress (linguistics) ; Artificial intelligence ; Structural engineering ; Artificial neural network ; Engineering ; Mathematics ; Finite element method Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Concrete Corrosion and Durability
Authors: Taiwo W. Quadri | Lukman O. Olasunkanmi | Omolola E. Fayemi | Ekemini D. Akpan | Chandrabhan Verma | El‐Sayed M. Sherif | K.F. Khaled | Eno E. Ebenso
Source: Coordination Chemistry Reviews (review, 2021-07-06)
Field: Materials Science — Materials Chemistry

Quantitative structure activity relationship and artificial neural network as vital tools in predicting coordination capabilities of organic compounds with metal surface: A review Quantitative structure–activity relationship ; Corrosion ; Chemistry ; Artificial neural network ; Support vector machine ; Metal ; Artificial intelligence ; Biochemical engineering ; Molecular descriptor ; Biological system ; Function (biology) ; Machine learning ; Combinatorial chemistry ; Nanotechnology ; Computer science ; Organic chemistry ; Stereochemistry ; Engineering ; Materials science Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Xiaowang Wang | Shuozhi Xu | Wu-Rong Jian | Xiangguo Li | Yanqing Su | Irene J. Beyerlein
Source: Computational Materials Science (article, 2021-03-02)
Field: Materials Science — Materials Chemistry

Generalized stacking fault energies and Peierls stresses in refractory body-centered cubic metals from machine learning-based interatomic potentials Peierls stress ; Glide plane ; Condensed matter physics ; Stacking fault ; Interatomic potential ; Asymmetry ; Dislocation ; Materials science ; Cubic crystal system ; Slip (aerodynamics) ; Stacking-fault energy ; Density functional theory ; Physics ; Chemistry ; Molecular dynamics ; Computational chemistry ; Dislocation creep ; Thermodynamics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ming‐Hsien Chan | Bo-Gu Chen | Loan Thi Ngo | Wen‐Tse Huang | Chien‐Hsiu Li | Ru‐Shi Liu | Michael Hsiao
Source: Pharmaceutics (review, 2021-11-05)
Field: Materials Science — Materials Chemistry

Natural Carbon Nanodots: Toxicity Assessment and Theranostic Biological Application This review outlines the methods for preparing carbon dots (CDs) from various natural resources to select the process to produce CDs with the best biological application efficacy. The oxidative activity of CDs mainly involves photo-induced cell damage and the destruction of biofilm matrices through the production of reactive oxygen species (ROS), thereby causing cell auto-apoptosis. Recent research has found that CDs derived from organic carbon sources can treat cancer cells as effectively as conventional drugs without causing damage to normal cells. CDs obtained by heating a natural carbon source inherit properties similar to the carbon source from which they are derived. Importantly, these characteristics can be exploited to perform non-invasive targeted therapy on human cancers, avoiding the harm caused to the human body by conventional treatments. CDs are attractive for large-scale clinical applications. Water, herbs, plants, and probiotics are ideal carbon-containing sources that can be used to synthesize therapeutic and diagnostic CDs that have become the focus of attention due to their excellent light stability, fluorescence, good biocompatibility, and low toxicity. They can be applied as biosensors, bioimaging, diagnosis, and treatment applications. These advantages make CDs attractive for large-scale clinical application, providing new technologies and methods for disease occurrence, diagnosis, and treatment research. Nanotechnology ; Biocompatibility ; Carbon fibers ; Materials science Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Heavy Metal Exposure and Toxicity
Authors: Ian A. Nicholls | Kerstin Golker | Gustaf D. Olsson | Subramanian Suriyanarayanan | Jesper G. Wiklander
Source: Polymers (review, 2021-08-24)
Field: Chemistry — Analytical Chemistry

The Use of Computational Methods for the Development of Molecularly Imprinted Polymers Recent years have witnessed a dramatic increase in the use of theoretical and computational approaches in the study and development of molecular imprinting systems. These tools are being used to either improve understanding of the mechanisms underlying the function of molecular imprinting systems or for the design of new systems. Here, we present an overview of the literature describing the application of theoretical and computational techniques to the different stages of the molecular imprinting process (pre-polymerization mixture, polymerization process and ligand-molecularly imprinted polymer rebinding), along with an analysis of trends within and the current status of this aspect of the molecular imprinting field. Molecularly imprinted polymer ; Molecular imprinting ; Imprinting (psychology) ; Polymerization ; Nanotechnology ; Polymer ; Materials science ; Molecular recognition ; Computer science ; Chemistry ; Molecule ; Organic chemistry ; Catalysis ; Selectivity Analytical chemistry methods development ; Microfluidic and Capillary Electrophoresis Applications ; Analytical Chemistry and Chromatography
Authors: Aurica P. Chiriac | Alina Gabriela Rusu | Loredana E. Niţă | Vlad Mihai Chiriac | Iordana Neamţu | Alina Sandu
Source: Pharmaceutics (review, 2021-04-28)
Field: Materials Science — Biomaterials

Polymeric Carriers Designed for Encapsulation of Essential Oils with Biological Activity The article reviews the possibilities of encapsulating essential oils EOs, due to their multiple benefits, controlled release, and in order to protect them from environmental conditions. Thus, we present the natural polymers and the synthetic macromolecular chains that are commonly used as networks for embedding EOs, owing to their biodegradability and biocompatibility, interdependent encapsulation methods, and potential applicability of bioactive blend structures. The possibilities of using artificial intelligence to evaluate the bioactivity of EOs-in direct correlation with their chemical constitutions and structures, in order to avoid complex laboratory analyses, to save money and time, and to enhance the final consistency of the products-are also presented. Encapsulation (networking) ; Biochemical engineering ; Biocompatibility ; Nanotechnology ; Computer science ; Chemistry ; Materials science ; Engineering ; Organic chemistry biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization ; Polymer composites and self-healing
Authors: Kazuo Takimiya | Kirill Bulgarevich | Mamatimin Abbas | Shingo Horiuchi | Takuya Ogaki | Kohsuke Kawabata | Abduleziz Ablat
Source: Advanced Materials (article, 2021-07-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

“Manipulation” of Crystal Structure by Methylthiolation Enabling Ultrahigh Mobility in a Pyrene‐Based Molecular Semiconductor Control and prediction of crystal structures of molecular semiconductors are considered challenging, yet they are crucial for rational design of superior molecular semiconductors. It is here reported that through methylthiolation, one can rationally control the crystal structure of pyrene derivatives as molecular semiconductors; 1,6-bis(methylthio)pyrene keeps a similar sandwich herringbone structure to that of parent pyrene, whereas 1,3,6,8-tetrakis(methylthio)pyrene (MT-pyrene) takes a new type of brickwork structure. Such changes in these crystal structures are explained by the alteration of intermolecular interactions that are efficiently controlled by methylthiolation. Single crystals of MT-pyrene are evaluated as the active semiconducting material in single-crystal field-effect transistors (SC-FETs), which show extremely high mobility (32 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> on average) operating at the drain and gate voltages of -5 V. Moreover, the band-like transport and very low trap density are experimentally confirmed for the MT-pyrene SC-FETs, testifying that the MT-pyrene is among the best molecular semiconductors for the SC-FET devices. Materials science ; Semiconductor ; Pyrene ; Nanotechnology ; Organic semiconductor ; Optoelectronics ; Organic chemistry Organic and Molecular Conductors Research ; Molecular Junctions and Nanostructures ; Organic Electronics and Photovoltaics
Authors: Rodrigo P. Carvalho | Cleber F. N. Marchiori | Daniel Brandell | C. Moysés Araújo
Source: Energy storage materials (article, 2021-10-25)
Field: Materials Science — Materials Chemistry

Artificial intelligence driven in-silico discovery of novel organic lithium-ion battery cathodes Organic electrode materials (OEMs) combine key sustainability and versatility properties with the potential to enable the realisation of the next generation of truly green battery technologies. However, for OEMs to become a competitive alternative, challenging issues related to energy density, rate capability and cycling stability need to be overcome. In this work, we have developed and applied an alternative yet systematic methodology to accelerate the discovery of suitable cathode-active OEMs by interplaying artificial intelligence (AI) and quantum mechanics. This AI-kernel has allowed a high-throughput screening of a huge library of organic molecules, leading to the discovery of 459 novel promising OEMs with candidates offering the potential to achieve theoretical energy densities superior to 1000 W h kg−1. Moreover, the machinery accurately identified common molecular functionalities that lead to such higher-voltage electrodes and pointed out an interesting donor-accepter-like effect that may drive the future design of cathode-active OEMs. Original equipment manufacturer ; Materials science ; Battery (electricity) ; Nanotechnology ; Cathode ; Energy storage ; Computer science ; Process engineering ; Systems engineering ; Biochemical engineering ; Electrical engineering ; Engineering ; Physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Molecular Junctions and Nanostructures
Authors: Yifan Wang | Tai-Ying Chen | Dionisios G. Vlachos
Source: Journal of Chemical Information and Modeling (article, 2021-10-25)
Field: Materials Science — Materials Chemistry

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. Bayesian optimization ; Computer science ; Interfacing ; Engineering optimization ; Python (programming language) ; Multidisciplinary design optimization ; Visualization ; Bayesian probability ; Optimization problem ; Artificial intelligence ; Machine learning ; Programming language ; Algorithm ; Multidisciplinary approach Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Sidan Tian | Qiang Yue | Chenchen Liu | Mengyang Li | Mingming Yin | Yuting Gao | Fanling Meng | Ben Zhong Tang | Liang Luo
Source: Journal of the American Chemical Society (article, 2021-06-28)
Field: Chemistry — Organic Chemistry

Complete Degradation of a Conjugated Polymer into Green Upcycling Products by Sunlight in Air The rapid development of digital society and artificial intelligence has triggered explosive demands for specialty plastics, especially conjugated polymers that are instrumental for flexible electronics and smart devices. The recycling and degradation of postconsumer conjugated polymers have become more important than ever to reduce the pressure to the environment. Here we report the discovery of an environmentally self-degradable conjugated polymer poly(deca-4,6-diynedioic acid), or PDDA. PDDA is stable in the dark or without oxygen when used as a functional material. However, when exposed to sunlight and air after the service life, PDDA disintegrates rapidly and fully decomposes through photooxidation in a week, yielding biocompatible, value-added succinic acid as a major degradation product. The complete degradation of PDDA into green upcycling products by sunlight in air, without leaving any microplastics, not only renders a pioneering paradigm of environmentally self-degradable conjugated polymers but also inspires developing effective strategies to completely degrade postconsumer conjugated polymers in a natural environment. Conjugated system ; Polymer ; Degradation (telecommunications) ; Chemistry ; Biodegradation ; Sunlight ; Atmospheric pressure ; Polymer degradation ; Nanotechnology ; Environmentally friendly ; Chemical engineering ; Organic chemistry ; Materials science ; Computer science ; Ecology Polydiacetylene-based materials and applications ; Microplastics and Plastic Pollution ; Conducting polymers and applications
Authors: D. Hu | Siqi Wang | Cheng Zhang | Pengshu Yi | Pingkai Jiang | Xingyi Huang
Source: Nano Research (article, 2021-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrathin MXene-aramid nanofiber electromagnetic interference shielding films with tactile sensing ability withstanding harsh temperatures Ultrathin and flexible electromagnetic shielding materials hold great potential in civil and military applications. Despite tremendous research efforts, the development of advanced shielding materials is still needed to provide additional functionalities for various artificial-intelligence-driven systems, such as tactile sensing ability. Herein, a layering design strategy is proposed to fabricate ultrathin Ti3C2Tx MXene-aramid nanofiber (MA) films by a layer-by-layer assembling process. Compared to that of randomly mixed films, the designed MA films exhibited a higher EMI shielding efficiency at an ultrathin thickness of 9 µm, which increased from 26.4 to 40.7 dB, owing to the additional multiple-interface scattering mechanism. Importantly, the novel MA films displayed strong EMI shielding ability even after heating/cooling treatments within a wide temperature range of −196 to 300 °C. Moreover, the same material displayed a tensile strength of 124.1 ± 2.7 MPa and a toughness of 6.3 ± 1.1 MJ·m−3, which are approximately 9.1 times and 45 times higher than those of pure MXene films, respectively. The MA film is also capable of detecting tactile signals via the triboelectric effect. A 2 × 4 tactile sensor array was developed to achieve an accurate signal catching capability. Therefore, in addition to the shielding performance, the manifestation of tactile perception by the MA films offers exciting opportunities in the fields of soft robotics and human-machine interactions. Electromagnetic shielding ; Materials science ; EMI ; Electromagnetic interference ; Optoelectronics ; Composite material ; Nanofiber ; Layer (electronics) ; Toughness ; Nanotechnology ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Jyotsnaa Pillai | Tanvi Chincholkar | Ruhi Dixit | Manoj Pandey
Source: World Journal of Surgical Oncology (review, 2021-10-28)
Field: Chemistry — Spectroscopy

A systematic review of proteomic biomarkers in oral squamous cell cancer Prospective use of these markers in the clinical setting will enable early detection, prediction of response to treatment, improvement in treatment selection, and early detection of tumor recurrence for disease monitoring. However, most of these markers for OSCC are yet to be validated. Medicine ; Cancer ; Biomarker ; Biomarker discovery ; Squamous cell cancer ; Surgical oncology ; Disease ; Oncology ; Proteomics ; Head and neck cancer ; Internal medicine ; Systematic review ; Bioinformatics ; MEDLINE ; Biology Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; Head and Neck Cancer Studies
Authors: S. S. Sekhon | Prabhsharan Kaur | Jin‐Soo Park
Source: Renewable and Sustainable Energy Reviews (article, 2021-05-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From coconut shell biomass to oxygen reduction reaction catalyst: Tuning porosity and nitrogen doping Porosity ; Catalysis ; Nitrogen ; Biomass (ecology) ; Shell (structure) ; Doping ; Materials science ; Chemical engineering ; Oxygen ; Reduction (mathematics) ; Pulp and paper industry ; Chemistry ; Composite material ; Organic chemistry ; Engineering ; Mathematics ; Agronomy ; Biology Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Advancements in Battery Materials