jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zheng Shu | Bowen Wang | Xiangyue Cui | Xuefei Yan | Hejin Yan | Huaxian Jia | Yongqing Cai
Source: Chemical Engineering Journal (article, 2022-11-09)
Field: Materials Science — Materials Chemistry

High-performance thermoelectric monolayer γ-GeSe and its group-IV monochalcogenide isostructural family Isostructural ; Group (periodic table) ; Thermoelectric effect ; Monolayer ; Materials science ; Thermoelectric materials ; Crystallography ; Chemistry ; Nanotechnology ; Physics ; Thermodynamics ; Organic chemistry ; Crystal structure Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Melih Yilmaz | William E. Fondrie | Wout Bittremieux | Sewoong Oh | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-02-09)
Field: Chemistry — Spectroscopy

<i>De novo</i> mass spectrometry peptide sequencing with a transformer model Abstract Tandem mass spectrometry is the only high-throughput method for analyzing the protein content of complex biological samples and is thus the primary technology driving the growth of the field of proteomics. A key outstanding challenge in this field involves identifying the sequence of amino acids—the peptide—responsible for generating each observed spectrum, without making use of prior knowledge in the form of a peptide sequence database. Although various machine learning methods have been developed to address this de novo sequencing problem, challenges that arise when modeling tandem mass spectra have led to complex models that combine multiple neural networks and post-processing steps. We propose a simple yet powerful method for de novo peptide sequencing, Casanovo, that uses a transformer framework to map directly from a sequence of observed peaks (a mass spectrum) to a sequence of amino acids (a peptide). Our experiments show that Casanovo achieves state-of-the-art performance on a benchmark dataset using a standard cross-species evaluation framework which involves testing with spectra with never-before-seen peptide labels. Casanovo not only achieves superior performance but does so at a fraction of the model complexity and inference time required by other methods. Peptide ; Tandem mass spectrometry ; Computer science ; Mass spectrometry ; Peptide sequence ; Benchmark (surveying) ; Proteogenomics ; Computational biology ; Proteomics ; Inference ; Transformer ; Protein sequencing ; Artificial intelligence ; Chemistry ; Biology ; Chromatography ; Genomics ; Biochemistry ; Genome ; Physics ; Gene Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: Keerthi Chadalavada | Krithika Anbazhagan | Adama Ndour | Sunita Choudhary | William E. Palmer | Jamie R. Flynn | Srikanth Mallayee | Sharada Pothu | Kodukula V. S. V. Prasad | Padmakumar Varijakshapanikar | Chris S. Jones | Jana Kholová
Source: Sensors (article, 2022-05-12)
Field: Chemistry — Analytical Chemistry

NIR Instruments and Prediction Methods for Rapid Access to Grain Protein Content in Multiple Cereals Achieving global goals for sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require instantaneous access to information on food-source quality at key points of agri-food systems. Although laboratory analysis and benchtop NIR spectrometers are regularly used to quantify grain quality, these do not suit all end users, for example, stakeholders in decentralized agri-food chains that are typical in emerging economies. Therefore, we explored benchtop and portable NIR instruments, and the methods that might aid these particular end uses. For this purpose, we generated NIR spectra for 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, and sorghum) with a standard benchtop NIR spectrometer (DS2500, FOSS) and a novel portable NIR-based instrument (HL-EVT5, Hone). We explored classical deterministic methods (via winISI, FOSS), novel machine learning (ML)-driven methods (via Hone Create, Hone), and a convolutional neural network (CNN)-based method for building the calibrations to predict grain protein out of the NIR spectra. All of the tested methods enabled us to build relevant calibrations out of both types of spectra (i.e., R<sup>2</sup> ≥ 0.90, RMSE ≤ 0.91, RPD ≥ 3.08). Generally, the calibration methods integrating the ML techniques tended to enhance the prediction capacity of the model. We also documented that the prediction of grain protein content based on the NIR spectra generated using the novel portable instrument (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R<sup>2</sup> = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the presented findings lay the foundations for the expanded use of NIR spectroscopy in agricultural research, development, and trade. Spectrometer ; Calibration ; Grain quality ; Convolutional neural network ; Computer science ; Environmental science ; Biotechnology ; Machine learning ; Mathematics ; Agronomy ; Statistics ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Behnam Ahmadikia | Leyun Wang | M. Arul Kumar | Irene J. Beyerlein
Source: Acta Materialia (article, 2022-11-27)
Field: Materials Science — Materials Chemistry

Grain boundary slip – twin transmission in titanium Materials science ; Slip (aerodynamics) ; Grain boundary ; Critical resolved shear stress ; Curvature ; Crystal plasticity ; Transmission electron microscopy ; Crystal twinning ; Lüders band ; Plasticity ; Condensed matter physics ; Shear (geology) ; Slip line field ; Ultimate tensile strength ; Composite material ; Crystallography ; Geometry ; Dislocation ; Microstructure ; Nanotechnology ; Physics ; Thermodynamics ; Mathematics Microstructure and mechanical properties ; Titanium Alloys Microstructure and Properties ; Advanced Welding Techniques Analysis
Authors: Wei‐Chun Chou | Yi‐Hsien Cheng | Jim E. Riviere | Nancy A. Monteiro‐Riviere | Wolfgang G. Kreyling | Zhoumeng Lin
Source: Particle and Fibre Toxicology (article, 2022-07-08)
Field: Materials Science — Biomaterials

Development of a multi-route physiologically based pharmacokinetic (PBPK) model for nanomaterials: a comparison between a traditional versus a new route-specific approach using gold nanoparticles in rats This study suggests that traditional route-to-route extrapolation approaches for PBPK modeling of small molecules are not applicable to NPs. Therefore, multi-route PBPK models for NPs should be developed using route-specific data. This novel PBPK-based web interface serves as a foundation for extrapolating to other NPs and to humans to facilitate biodistribution estimation, safety, and risk assessment of NPs. Physiologically based pharmacokinetic modelling ; Biodistribution ; Pharmacokinetics ; Bioanalysis ; Nanomedicine ; Monte Carlo method ; Colloidal gold ; Biological system ; Nanotechnology ; Nanoparticle ; Chemistry ; Computer science ; Pharmacology ; Materials science ; Mathematics ; Medicine ; In vitro Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Antibiotics Pharmacokinetics and Efficacy
Authors: Pushpa Raj Paudel | Michael Skowrons | Drona Dahal | Raj Kishen Radha Krishnan | Björn Lüssem
Source: Advanced Theory and Simulations (article, 2022-02-20)
Field: Materials Science — Polymers and Plastics

The Transient Response of Organic Electrochemical Transistors Abstract A fast response of organic electrochemical transistors (OECTs) to electrical or chemical changes is essential for a widespread acceptance of this technology. However, finding design rules for fast switching OECTs is complicated by the fact that current transient device models are highly simplified and rely on a 1D approximation of the device that neglects details of the ion and hole concentration inside the transistor channel. To improve the understanding of transient processes limiting the speed of OECTs, a 2D drift‐diffusion model is presented and experimentally validated. It is shown that switching is strongly influenced by lateral ion currents that are neglected in previous models. A consistent treatment of these currents leads to a dependency of the time constants on the applied drain potential and a complex dependency of the response time constants on the detailed device geometry. In addition to an improved understanding of the transient response of OECTs, the results discussed here highlight the challenges in properly characterizing switching time constants of OECTs, and reinforce the necessity to ensure that switching is measured between two steady‐state conditions, and not between transient states. Transient (computer programming) ; Transient response ; Transistor ; Limiting ; Time constant ; Dependency (UML) ; Response time ; Materials science ; Switching time ; Transient analysis ; Steady state (chemistry) ; Channel (broadcasting) ; Constant (computer programming) ; Optoelectronics ; Mechanics ; Chemistry ; Computer science ; Physics ; Voltage ; Electrical engineering ; Engineering ; Mechanical engineering Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Analytical Chemistry and Sensors
Authors: Xingqin Fu | Yuejun Zhang | Xu Jia | Yongji Wang | Tingting Chen
Source: Molecules (review, 2022-02-14)
Field: Chemistry — Organic Chemistry

Research Progress on Typical Quaternary Ammonium Salt Polymers Quaternary ammonium salt polymers, a kind of polyelectrolyte with a quaternary ammonium group, are widely used in traditional and emerging industries due to their good water-solubility, adjustable cationicity and molecular weight, high efficiency and nontoxicity. In this paper, firstly, the properties and several synthesis methods of typical quaternary ammonium salt monomers were introduced. Secondly, the research progress on the synthesis of polymers was summarized from the perspective of obtaining products with high molecular weight, narrow molecular weight distribution and high monomer conversion, and special functional polymers. Thirdly, the relationships between the structures and properties of the polymer were analyzed from the perspectives of molecular weight, charge density, structural stability, and microstructural regulation of the polymer chain unit. Fourthly, typical examples of quaternary ammonium salt polymers in the application fields of water treatment, daily chemicals, petroleum exploitation, papermaking, and textile printing and dyeing were listed. Finally, constructive suggestions were put forward on developing quaternary ammonium salt polymers with high molecular weights, strengthening the research on the relationships between the structures and their properties and pinpointing relevant application fields. Polymer ; Salt (chemistry) ; Ammonium ; Polyelectrolyte ; Monomer ; Materials science ; Solubility ; Quaternary ; Chemical engineering ; Chemistry ; Polymer chemistry ; Organic chemistry ; Geology Advanced Polymer Synthesis and Characterization ; Antimicrobial agents and applications ; biodegradable polymer synthesis and properties
Authors: Dongyang Wang | Jiamin Ding | Xiaojuan Dai | Lanyi Xiang | Dekai Ye | Zihan He | Fengjiao Zhang | Seok‐Heon Jung | Jin‐Kyun Lee | Chong‐an Di | Daoben Zhu
Source: Advanced Materials (article, 2022-10-28)
Field: Materials Science — Materials Chemistry

Triggering <i>ZT</i> to 0.40 by Engineering Orientation in One Polymeric Semiconductor Breaking the thermoelectric (TE) trade-off relationship is an important task for maximizing the TE performance of polymeric semiconductors. Existing efforts have focused on designing high-mobility semiconductors and achieving ordered molecular doping, ignoring the critical role of the molecular orientation during TE conversion. Herein, the achievement of ZT to 0.40 is reported by fine-tuning the molecular orientation of one diketopyrrolopyrrole (DPP)-based polymer (DPP-BTz). Films with bimodal molecular orientation yield superior doping efficiency by increasing the lamellar spacing and achieve increased splitting between the Fermi energy and the transport energy to enhance the thermopower. These factors contribute to the simultaneous improvement in the Seebeck coefficient and electrical conductivity in an unexpected manner. Importantly, the bimodal film exhibits a maximum power factor of up to 346 µW m<sup>-1</sup> K<sup>-2</sup> , >400% higher than that of unimodal films. These results demonstrate the great potential of molecular orientation engineering in polymeric semiconductors for developing state-of-the-art organic TE (OTE) materials. Materials science ; Semiconductor ; Seebeck coefficient ; Thermoelectric effect ; Doping ; Lamellar structure ; Thermoelectric materials ; Organic semiconductor ; Optoelectronics ; Nanotechnology ; Condensed matter physics ; Thermal conductivity ; Composite material ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Lihua Zhu | Yilun Sun | Huaze Zhu | Guoliang Chai | Zhiqing Yang | Congxiao Shang | Hengqiang Ye | Bing Hui Chen | Anna Kroner | Zhengxiao Guo
Source: ACS Catalysis (article, 2022-06-22)
Field: Chemistry — Organic Chemistry

Effective Ensemble of Pt Single Atoms and Clusters over the (Ni,Co)(OH)<sub>2</sub> Substrate Catalyzes Highly Selective, Efficient, and Stable Hydrogenation Reactions A Pt–Ni–Co catalyst was synthesized with Pt single atoms and atomic clusters (SAACs) dispersed over (Ni,Co)(OH)2 nanoparticles on a carbon matrix, which leads to high catalytic activity, up to 100% conversion, and selectivity in the hydrogenation of nitroaromatics under moderate conditions (H2 ∼ 1.0 MPa and ≤40 °C). A synergistically coordinated ensemble effect of the Pt SAACs is identified with the strongly polarized Pt single atoms preferentially adsorbing the −NO2 and the Pt clusters adsorbing and homolytically dissociating H2 molecules, and the H species then readily move to the adsorbed −NO2 group, overcoming a much reduced energy barrier on the (Ni,Co)(OH)2, enhancing the reaction rate by ca. 50 times. The approach not only reveals the coordinated ensemble catalysis mechanism of SAACs but also provides a strategy of developing highly efficient and selective catalysts by fine tuning of the electronic microenvironment from single atoms to atomic clusters co-located over a multimetallic substrate. The demonstrated case for nitroarenes can be readily applied for other species containing −NO2 or other easily hydrogenated groups (such as C═C, C≡N, and C═O). Catalysis ; Selectivity ; Substrate (aquarium) ; Chemistry ; Adsorption ; Nanoparticle ; Molecule ; Crystallography ; Materials science ; Physical chemistry ; Nanotechnology ; Organic chemistry Nanomaterials for catalytic reactions ; Advanced Photocatalysis Techniques ; Copper-based nanomaterials and applications
Authors: Beatriu Domingo‐Tafalla | Eugenia Martínez‐Ferrero | Federico Franco | Emilio Palomares-Gil
Source: Molecules (review, 2022-02-06)
Field: Materials Science — Materials Chemistry

Applications of Carbon Dots for the Photocatalytic and Electrocatalytic Reduction of CO2 The photocatalytic and electrocatalytic conversion of CO<sub>2</sub> has the potential to provide valuable products, such as chemicals or fuels of interest, at low cost while maintaining a circular carbon cycle. In this context, carbon dots possess optical and electrochemical properties that make them suitable candidates to participate in the reaction, either as a single component or forming part of more elaborate catalytic systems. In this review, we describe several strategies where the carbon dots participate, both with amorphous and graphitic structures, in the photocatalysis or electrochemical catalysis of CO<sub>2</sub> to provide different carbon-containing products of interest. The role of the carbon dots is analyzed as a function of their redox and light absorption characteristics and their complementarity with other known catalytic systems. Moreover, detailed information about synthetic procedures is also reviewed. Photocatalysis ; Carbon fibers ; Catalysis ; Nanotechnology ; Electrochemistry ; Materials science ; Context (archaeology) ; Amorphous carbon ; Amorphous solid ; Chemistry ; Electrode ; Organic chemistry Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Covalent Organic Framework Applications
Authors: Ziyang Fu | Weiyi Liu | Chen Huang | Tao Mei
Source: Nanomaterials (review, 2022-08-26)
Field: Materials Science — Materials Chemistry

A Review of Performance Prediction Based on Machine Learning in Materials Science With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area. Field (mathematics) ; Process (computing) ; Computer science ; Material Design ; Engineering design process ; Key (lock) ; Biochemical engineering ; Data science ; Management science ; Systems engineering ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mineral Processing and Grinding
Authors: Chenru Duan | Aditya Nandy | Heather J. Kulik
Source: Annual Review of Chemical and Biomolecular Engineering (review, 2022-03-23)
Field: Materials Science — Materials Chemistry

Machine Learning for the Discovery, Design, and Engineering of Materials Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (<i>a</i>) the discovery of new materials through large-scale enumerative screening, (<i>b</i>) the design of materials through identification of rules and principles that govern materials properties, and (<i>c</i>) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Computer science ; Throughput ; Identification (biology) ; Cover (algebra) ; Scale (ratio) ; Machine learning ; Data science ; Systems engineering ; Artificial intelligence ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yafei Wang | Bonita Goh | Phalgun Nelaturu | Thien C. Duong | Najlaa Hassan | Raphaelle David | Michael Moorehead | Santanu Chaudhuri | Adam Creuziger | Jason Hattrick‐Simpers | Dan J. Thoma | Kumar Sridharan | Adrien Couet
Source: Advanced Science (article, 2022-05-07)
Field: Materials Science — Metals and Alloys

Integrated High‐Throughput and Machine Learning Methods to Accelerate Discovery of Molten Salt Corrosion‐Resistant Alloys Insufficient availability of molten salt corrosion-resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for molten salt applications and develop fundamental understanding of corrosion in these environments, here an integrated approach is presented using a set of high-throughput (HTP) alloy synthesis, corrosion testing, and modeling coupled with automated characterization and machine learning. By using this approach, a broad range of CrFeMnNi alloys are evaluated for their corrosion resistances in molten salt simultaneously demonstrating that corrosion-resistant alloy development can be accelerated by 2 to 3 orders of magnitude. Based on the obtained results, a sacrificial protection mechanism is unveiled in the corrosion of CrFeMnNi alloys in molten salts which can be applied to protect the less unstable elements in the alloy from being depleted, and provided new insights on the design of high-temperature molten salt corrosion-resistant alloys. Corrosion ; Molten salt ; Alloy ; Metallurgy ; Materials science ; Salt (chemistry) ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Linggang Zhu | Jian Zhou | Zhimei Sun
Source: The Journal of Physical Chemistry Letters (review, 2022-04-28)
Field: Materials Science — Materials Chemistry

Materials Data toward Machine Learning: Advances and Challenges Machine learning (ML) is believed to have enabled a paradigm shift in materials research, and in practice, ML has demonstrated its power in speeding up the cost-efficient discovery of new materials and autonomizing materials laboratories. In this Perspective, current research progress in materials data which are the backbones of ML are reviewed, focusing on high-throughput data generation, standardized data storage, and data representation. More importantly, the challenging issues in materials data that should be overcome to unlock the full potential of ML in materials research and development, including classic 5V (volume, velocity, variety, veracity, and value) issues, 3M (multicomponent, multiscale, and multistage) challenges, co-mining of experimental and computational data, and materials data toward transferable/explainable ML or causal ML, are discussed. Computer science ; Data science ; Volume (thermodynamics) ; Variety (cybernetics) ; Perspective (graphical) ; Representation (politics) ; Machine learning ; Throughput ; Data mining ; Artificial intelligence Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Jonathan M. Hestroffer | Marie‐Agathe Charpagne | Marat I. Latypov | Irene J. Beyerlein
Source: Computational Materials Science (article, 2022-11-12)
Field: Materials Science — Materials Chemistry

Graph neural networks for efficient learning of mechanical properties of polycrystals Microstructure ; Convolutional neural network ; Artificial intelligence ; Computer science ; Graph ; Pattern recognition (psychology) ; Computation ; Artificial neural network ; Materials science ; Algorithm ; Machine learning ; Theoretical computer science ; Composite material Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Hydrogen embrittlement and corrosion behaviors in metals