jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi Sun | Jiong Lu | Qiuhua Liu | Wei Shuai | Anwei Sun | Nan Zheng | Yu Han | Gang Xiao | Jin Xuan | Meng Ni | Haoran Xu
Source: Energy Conversion and Management (article, 2022-04-01)
Field: Materials Science — Materials Chemistry

Multi-objective optimizations of solid oxide co-electrolysis with intermittent renewable power supply via multi-physics simulation and deep learning strategy Electrolysis ; Renewable energy ; Process engineering ; Power (physics) ; Oxide ; Computer science ; Materials science ; Chemistry ; Thermodynamics ; Engineering ; Physics ; Electrical engineering Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; Molten salt chemistry and electrochemical processes
Authors: Yun Zhang | Xiaojie Xu
Source: Journal of Cellular Plastics (article, 2022-02-23)
Field: Materials Science — Polymers and Plastics

Predicting mechanical performance of starch-based foam materials Variations in mixture proportions of plasticizers, additives, and crosslinking agents have significant impacts on mechanical performance of starch-based foam materials. In particular, starch/ethylene-vinyl acetate (EVA) foam materials have been developed with improved mechanical strength by optimizing the formulation. There is a lack of numerical correlations that could help analyze the effects of components and provide a predictive method for future research. In this study, we develop simple and accurate predictions for tensile strength and resilience based on mixture proportions of components for starch-based/EVA foam materials. The models constructed might be used to help design mixture proportions of starch-based foam materials. By combining optimization results from the Taguchi method and machine learning approaches, it is expected that more quantitative data can be extracted from fewer experimental trials at the same time. Materials science ; Starch ; Ultimate tensile strength ; Taguchi methods ; Composite material ; Plasticizer ; Ethylene-vinyl acetate ; Modified starch ; Resilience (materials science) ; Process engineering ; Polymer ; Copolymer Polymer Foaming and Composites ; biodegradable polymer synthesis and properties ; Polymer Nanocomposites and Properties
Authors: Sheheryar Zaidi | Michael Schaarschmidt | James Martens | Hyunjik Kim | Yee Whye Teh | Álvaro Sánchez‐González | Peter Battaglia | Razvan Pascanu | Jonathan Godwin
Source: arXiv (Cornell University) (preprint, 2022-05-31)
Field: Materials Science — Materials Chemistry

Pre-training via Denoising for Molecular Property Prediction Many important problems involving molecular property prediction from 3D structures have limited data, posing a generalization challenge for neural networks. In this paper, we describe a pre-training technique based on denoising that achieves a new state-of-the-art in molecular property prediction by utilizing large datasets of 3D molecular structures at equilibrium to learn meaningful representations for downstream tasks. Relying on the well-known link between denoising autoencoders and score-matching, we show that the denoising objective corresponds to learning a molecular force field -- arising from approximating the Boltzmann distribution with a mixture of Gaussians -- directly from equilibrium structures. Our experiments demonstrate that using this pre-training objective significantly improves performance on multiple benchmarks, achieving a new state-of-the-art on the majority of targets in the widely used QM9 dataset. Our analysis then provides practical insights into the effects of different factors -- dataset sizes, model size and architecture, and the choice of upstream and downstream datasets -- on pre-training. Computer science ; Property (philosophy) ; Generalization ; Artificial intelligence ; Noise reduction ; Machine learning ; Training (meteorology) ; Benchmark (surveying) ; Upstream (networking) ; Field (mathematics) ; Matching (statistics) ; Pattern recognition (psychology) ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Jia Qiu | Jiancong Xie | Shimin Su | Yadong Gao | Meng Han | Yuedong Yang | Kuangbiao Liao
Source: Chem (article, 2022-09-19)
Field: Chemistry — Organic Chemistry

Selective functionalization of hindered meta-C–H bond of o-alkylaryl ketones promoted by automation and deep learning Surface modification ; Carboxylation ; Steric effects ; Chemistry ; Yield (engineering) ; Triple bond ; Combinatorial chemistry ; Catalysis ; Decarboxylation ; Organic chemistry ; Double bond ; Materials science Catalytic C–H Functionalization Methods ; Carbon dioxide utilization in catalysis ; Radical Photochemical Reactions
Authors: Jiadong Dan | Xiaoxu Zhao | Shoucong Ning | Jiong Lu | Kian Ping Loh | Qian He | N. Duane Loh | Stephen J. Pennycook
Source: Science Advances (article, 2022-04-13)
Field: Materials Science — Materials Chemistry

Learning motifs and their hierarchies in atomic resolution microscopy Characterizing materials to atomic resolution and first-principles structure-property prediction are two pillars for accelerating functional materials discovery. However, we are still lacking a rapid, noise-robust framework to extract multilevel atomic structural motifs from complex materials to complement, inform, and guide our first-principles models. Here, we present a machine learning framework that rapidly extracts a hierarchy of complex structural motifs from atomically resolved images. We demonstrate how such motif hierarchies can rapidly reconstruct specimens with various defects. Abstracting complex specimens with simplified motifs enabled us to discover a previously unidentified structure in a Mo─V─Te─Nb polyoxometalate (POM) and quantify the relative disorder in a twisted bilayer MoS<sub>2</sub>. In addition, these motif hierarchies provide statistically grounded clues about the favored and frustrated pathways during self-assembly. The motifs and their hierarchies in our framework coarse-grain disorder in a manner that allows us to understand a much broader range of multiscale samples with functional imperfections and nontrivial topological phases. Structural motif ; Motif (music) ; Computer science ; Nanotechnology ; Hierarchy ; Polyoxometalate ; Computational biology ; Materials science ; Chemistry ; Biology ; Physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Yubo Huang | Zhong Xiang | Miao Qian
Source: Physical review. E (article, 2022-01-18)
Field: Materials Science — Materials Chemistry

Deep-learning-based porous media microstructure quantitative characterization and reconstruction method Microstructure characterization and reconstruction (MCR) is one of the most important components of discovering processing-structure-property relations of porous media behavior and inverse porous media design in computational materials science. Since the algorithms for describing and controlling the geometric configuration of microstructures need to solve a large number of variables and involve multiobjective conditions, the existing MCR methods have difficulty in gaining a perfect trade-off among the quantitative generation and characterization capability and the reconstruction quality. In this work, an improved 3D Porous Media Microstructure (3DPmmGAN) generative adversarial network based on deep-learning algorithm is demonstrated for high-quality microstructures generation with high controllability and high prediction accuracy. The proposed 3DPmmGAN allows the model to utilize unlabeled data for complex high-randomness microstructures end-to-end training within an acceptable time consumption. Further analysis shows that the trained network has good adaptivity for microstructures with different random geometric configurations, and can quantitatively control the generated structure according to semantic conditions, and can also quantitatively predict complex microstructure features. The key results suggest the proposed 3DPmmGAN is a powerful tool to accelerate the preparation and the initial characterization of 3D porous media, and potentially maximize the design efficiency for porous media. Characterization (materials science) ; Microstructure ; Controllability ; Porous medium ; Porosity ; Inverse problem Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Topology Optimization in Engineering
Authors: Liang Chen | Wentian Jin | Sheldon X.-D. Tan
Source: 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC) (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

Fast Thermal Analysis for Chiplet Design based on Graph Convolution Networks 2.5D chiplet-based technology promises an efficient integration technique for advanced designs with more functionality and higher performance. Temperature and related thermal optimization, heat removal are of critical importance for temperature-aware physical synthesis for chiplets. This paper presents a novel graph convolutional networks (GCN) architecture to estimate the thermal map of the 2.5D chiplet-based systems with the thermal resistance networks built by the compact thermal model (CTM). First, we take the total power of all chiplets as an input feature, which is a global feature. This additional global information can overcome the limitation that the GCN can only extract local information via neighborhood aggregation. Second, inspired by convolutional neural networks (CNN), we add skip connection into the GCN to pass the global feature directly across the hidden layers with the concatenation operation. Third, to consider the edge embedding feature, we propose an edge-based attention mechanism based on the graph attention networks (GAT). Last, with the multiple aggregators and scalers of principle neighborhood aggregation (PNA) networks, we can further improve the modeling capacity of the novel GCN. The experimental results show that the proposed GCN model can achieve an average RMSE of 0.31 K and deliver a 2.6× speedup over the fast steady-state solver of open-source HotSpot based on SuperLU. More importantly, the GCN model demonstrates more useful generalization or transferable capability. Our results show that the trained GCN can be directly applied to predict thermal maps of six unseen datasets with acceptable mean RMSEs of less than 0.67 K without retraining via inductive learning. Computer science ; Convolutional neural network ; Graph ; Feature (linguistics) ; Convolution (computer science) ; Solver ; Speedup ; Theoretical computer science ; Concatenation (mathematics) ; Algorithm ; Artificial intelligence ; Pattern recognition (psychology) ; Data mining ; Artificial neural network ; Mathematics ; Parallel computing Thermal properties of materials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Ashok Kumawat | Saikat Chattopadhyay | Kamakhya Prakash Misra | R.D.K. Misra | Priyanka Kumari
Source: Thin Solid Films (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Micro-strain governed photoluminescence emission intensity of sol-gel spin coated Eu doped ZnO thin films Photoluminescence ; Materials science ; Doping ; Crystallite ; Thin film ; Band gap ; Analytical Chemistry (journal) ; Sol-gel ; Optics ; Nanotechnology ; Optoelectronics ; Chemistry ; Metallurgy ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Ga2O3 and related materials
Authors: Yichun Su | Guoqiang Yuan | Jinliang Hu | Wenhao Feng | Qingyu Zeng | Yang‐Yi Liu | Huan Pang
Source: Chemical Synthesis (article, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Recent progress in strategies for preparation of metal-organic frameworks and their hybrids with different dimensions As a new kind of organic-inorganic hybrid porous material, metal-organic frameworks (MOFs) exhibit a wide application prospect in gas storage and separation, catalysis and sensing due to their characteristics of large specific surface area, high porosity and coordination unsaturation. As more and more types of MOFs were reported, the synthetic strategies of MOFs-based materials have become a hot research topic. According to the morphological dimension, MOFs can be roughly divided into one-dimensional, two-dimensional and three-dimensional structures. Herein, we summarize the synthetic methods and principles of MOFs from multi-dimensional perspectives, and explore the growth mechanism of MOFs with different morphologies based on dynamic and thermodynamic tuning. Finally, based on the above summaries, the challenges and opportunities of MOFs in the future are discussed. Metal-organic framework ; Nanotechnology ; Materials science ; Porosity ; Degree of unsaturation ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry ; Polymer chemistry Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; X-ray Diffraction in Crystallography
Authors: Ehsan Kianfar | Saade Abdalkareem Jasim | Hamzah H. Kzar | R. Sivaraman | Muhaned Zaidi | Ola Kamal A. Alkadir | Fatima Safaa Fahim | Muhsin J. Jweeg | Ahmed Kareem Obaid Aldulaim
Source: Egyptian Journal of Chemistry (review, 2022-05-27)
Field: Materials Science — Materials Chemistry

Engineered Nanomaterials, Plants, Plant Toxicity and Biotransformation: A review Interaction between engineered nanomaterials and plants is important; Because plants have direct contact with water, soil, and therefore the atmosphere, the potential pathway for higher species to encounter these nanomaterials is thru the organic phenomenon that plants form the most ring and source of. the aim of the article, Plant Toxicity and Biotransformation, is to boost our understanding of a number of the interactions of engineered nanomaterials with plants, including their toxicity to plants and biotransformation or biodegradation of nanomaterials within the plant system. Mechanisms of nanomaterial toxicity to plants and biological access to nanomaterials aren't yet well understood. it's clear that in these circumstances, further evaluations of the interaction of nanomaterials and plants, likewise because the development of latest methods for characterizing nanomaterials in vivo, are necessary so as to create sustainable use of nanotechnology. Biotransformation ; Toxicity ; Nanomaterials ; Biochemical engineering ; Chemistry ; Environmental chemistry ; Nanotechnology ; Materials science ; Engineering ; Biochemistry Nanoparticles: synthesis and applications
Authors: Mustafa Anwar | Muhammed Ali | Uneeb Masood Khan | Muhammad Hassan | Asif Hussain Khoja | Andanastuti Muchtar
Source: Materials (review, 2022-03-30)
Field: Materials Science — Materials Chemistry

A Review of X-ray Photoelectron Spectroscopy Technique to Analyze the Stability and Degradation Mechanism of Solid Oxide Fuel Cell Cathode Materials Nondestructive characterization of solid oxide fuel cell (SOFC) materials has drawn attention owing to the advances in instrumentation that enable in situ characterization during high-temperature cell operation. X-ray photoelectron spectroscopy (XPS) is widely used to investigate the surface of SOFC cathode materials because of its excellent chemical specificity and surface sensitivity. The XPS can be used to analyze the elemental composition and oxidation state of cathode layers from the surface to a depth of approximately 5-10 nm. Any change in the chemical state of the SOFC cathode at the surface affects the migration of oxygen ions to the cathode/electrolyte interface via the cathode layer and causes performance degradation. The objective of this article is to provide a comprehensive review of the adoption of XPS for the characterization of SOFC cathode materials to understand its degradation mechanism in absolute terms. The use of XPS to confirm the chemical stability at the interface and the enrichment of cations on the surface is reviewed. Finally, the strategies adopted to improve the structural stability and electrochemical performance of the LSCF cathode are also discussed. X-ray photoelectron spectroscopy ; Cathode ; Materials science ; Degradation (telecommunications) ; Oxide ; Fuel cells ; Solid oxide fuel cell ; Mechanism (biology) ; Chemical engineering ; X-ray ; Chemistry ; Physical chemistry ; Optics ; Electronic engineering ; Metallurgy ; Electrode ; Anode ; Physics ; Engineering Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Tim Hsu | Tuan Anh Pham | Nathan Keilbart | Stephen E. Weitzner | James Chapman | Penghao Xiao | S. Roger Qiu | Xiao Dong Chen | Brandon C. Wood
Source: npj Computational Materials (article, 2022-07-15)
Field: Materials Science — Materials Chemistry

Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy Abstract Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN (Atomistic Line Graph Neural Network) encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed. Dihedral angle ; Interpretability ; Graph ; Artificial neural network ; Encoding (memory) ; Representation (politics) ; Graph drawing ; Molecular geometry ; Computer science ; Algorithm ; Topology (electrical circuits) ; Biological system ; Theoretical computer science ; Mathematics ; Physics ; Combinatorics ; Artificial intelligence ; Molecule ; Hydrogen bond ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Mingkang Sun | Grzegorz Szczepaniak | Sajjad Dadashi‐Silab | Ting‐Chih Lin | Tomasz Kowalewski | Krzysztof Matyjaszewski
Source: Macromolecular Chemistry and Physics (article, 2022-11-09)
Field: Chemistry — Organic Chemistry

Cu‐Catalyzed Atom Transfer Radical Polymerization: The Effect of Cocatalysts Abstract Atom transfer radical polymerization (ATRP) is one of the most powerful methods to prepare well‐defined (co)polymers. Cu‐catalyzed ATRP methods are most commonly used because of the excellent control and tunable catalytic activities via ligand functionalization. This minireview summarizes the development of Cu‐catalyzed ATRP in the presence of cocatalysts, which are used to regenerate Cu I complex activators during polymerizations. Fundamentals of Cu‐based ATRP catalysts are first introduced, followed by the discussion of different types of cocatalysts in different Cu‐catalyzed ATRP methods. Recent developments of photochemical cocatalysts for oxygen‐tolerant ATRP and ATRP using long‐wavelength irradiation are highlighted, which significantly expand the applications of Cu‐catalyzed ATRP. Methods to study the properties of cocatalysts and their roles in Cu‐catalyzed ATRP are discussed, with an outlook for the future development of cocatalysts. Atom-transfer radical-polymerization ; Catalysis ; Chemistry ; Polymer chemistry ; Polymerization ; Polymer ; Photochemistry ; Combinatorial chemistry ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications ; Click Chemistry and Applications
Authors: Maria João Oliveira | Ana Dalot | Elvira Fortunato | Rodrigo Martins | Hugh J. Byrne | Ricardo Franco | Hugo Águas
Source: Discover Materials (review, 2022-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Microfluidic SERS devices: brightening the future of bioanalysis A new avenue has opened up for applications of surface-enhanced Raman spectroscopy (SERS) in the biomedical field, mainly due to the striking advantages offered by SERS tags. SERS tags provide indirect identification of analytes with rich and highly specific spectral fingerprint information, high sensitivity, and outstanding multiplexing potential, making them very useful in in vitro and in vivo assays. The recent and innovative advances in nanomaterial science, novel Raman reporters, and emerging bioconjugation protocols have helped develop ultra-bright SERS tags as powerful tools for multiplex SERS-based detection and diagnosis applications. Nevertheless, to translate SERS platforms to real-world problems, some challenges, especially for clinical applications, must be addressed. This review presents the current understanding of the factors influencing the quality of SERS tags and the strategies commonly employed to improve not only spectral quality but the specificity and reproducibility of the interaction of the analyte with the target ligand. It further explores some of the most common approaches which have emerged for coupling SERS with microfluidic technologies, for biomedical applications. The importance of understanding microfluidic production and characterisation to yield excellent device quality while ensuring high throughput production are emphasised and explored, after which, the challenges and approaches developed to fulfil the potential that SERS-based microfluidics have to offer are described. Nanotechnology ; Microfluidics ; Multiplex ; Bioanalysis ; Bioconjugation ; Computer science ; Surface-enhanced Raman spectroscopy ; Biochemical engineering ; Materials science ; Raman spectroscopy ; Engineering ; Bioinformatics ; Raman scattering ; Biology ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Karel Asselman | Dries Vandenabeele | Nick Pellens | Nikolaus Doppelhammer | Christine E. A. Kirschhock | Eric Breynaert
Source: Chemistry of Materials (article, 2022-11-22)
Field: Chemistry — Inorganic Chemistry

Structural Aspects Affecting Phase Selection in Inorganic Zeolite Synthesis A guideline for zeolite phase selection in inorganic synthesis media is proposed, based on a systematic exploration of synthesis from inorganic media using liquid Na<sup>+</sup>, K<sup>+</sup>, and Cs<sup>+</sup> aluminosilicate. Although the Si/Al ratio of the zeolites is a continuous function of the synthesis conditions, boundaries between topologies are sharp. The here-derived phase selection criterion relates the obtained zeolite topology to the Si/Al ratio imposed by the synthesis medium. For a given Si/Al ratio, the framework with the highest occupation of topologically available cation sites is favored. The large number of published zeolite syntheses supporting the observation provides strong indication that the concept is applicable in a larger context. The proposed criterion explains how minor variations in the composition of inorganic synthesis media induce the commonly occurring, abrupt changes in topology. It highlights underlying reasons causing the strict demarcation of stability fields of the as-synthesized zeolites experimentally observed in inorganic synthesis. Zeolite ; Aluminosilicate ; Context (archaeology) ; Topology (electrical circuits) ; Phase (matter) ; Selection (genetic algorithm) ; Materials science ; Chemical engineering ; Chemistry ; Inorganic chemistry ; Computer science ; Mathematics ; Catalysis ; Organic chemistry ; Geology ; Engineering ; Combinatorics Zeolite Catalysis and Synthesis ; Chemical Synthesis and Characterization ; Crystal Structures and Properties