jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suvrat Chowdhary | Robert F. Schmidt | Anil Kumar Sahoo | Tiemo tom Dieck | Thomas Hohmann | Boris Schade | Kerstin Brademann-Jock | Andreas F. Thünemann | Roland R. Netz | Michael Gradzielski | Beate Koksch
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Biomaterials

Rational design of amphiphilic fluorinated peptides: evaluation of self-assembly properties and hydrogel formation Advanced peptide-based nanomaterials composed of self-assembling peptides (SAPs) are of emerging interest in pharmaceutical and biomedical applications. The introduction of fluorine into peptides, in fact, offers unique opportunities to tune their biophysical properties and intermolecular interactions. In particular, the degree of fluorination plays a crucial role in peptide engineering as it can be used to control the characteristics of fluorine-specific interactions and, thus, peptide conformation and self-assembly. Here, we designed and explored a series of amphipathic peptides by incorporating the fluorinated amino acids (2<i>S</i>)-4-monofluoroethylglycine (MfeGly), (2<i>S</i>)-4,4-difluoroethylglycine (DfeGly) and (2<i>S</i>)-4,4,4-trifluoroethylglycine (TfeGly) as hydrophobic components. This approach enabled studying the impact of fluorination on secondary structure formation and peptide self-assembly on a systematic basis. We show that the interplay between polarity and hydrophobicity, both induced differentially by varying degrees of side chain fluorination, does affect peptide folding significantly. A greater degree of fluorination promotes peptide fibrillation and subsequent formation of physical hydrogels in physiological conditions. Molecular simulations revealed the key role played by electrostatically driven intra-chain and inter-chain contact pairs that are modulated by side chain fluorination and give insights into the different self-organization behaviour of selected peptides. Our study provides a systematic report about the distinct features of fluorinated oligomeric peptides with potential applications as peptide-based biomaterials. Peptide ; Amphiphile ; Self-healing hydrogels ; Rational design ; Self-assembly ; Chemistry ; Intermolecular force ; Side chain ; Combinatorial chemistry ; Biophysics ; Nanotechnology ; Materials science ; Organic chemistry ; Biochemistry ; Molecule ; Copolymer ; Polymer ; Biology Supramolecular Self-Assembly in Materials ; Fluorine in Organic Chemistry ; Polydiacetylene-based materials and applications
Authors: Guanding Mei | Weigao Wang | Dan Wu | Phil Surman | Kai Wang | Wallace C. H. Choy | Xiaochuan Yang | Wenwei Xu | Xiao Wei Sun
Source: IEEE photonics journal (article, 2022-03-15)
Field: Materials Science — Materials Chemistry

Full-Color Quantum Dot Light-Emitting Diodes Based on Microcavities Full-color display is a primary challenge for the commercialization of quantum dots (QDs). In this study, we utilize the spectral narrowing phenomenon of microcavities to fabricate the red, green and blue quantum dot light-emitting diodes (QLEDs) with a single QD layer. This work theoretically analyses the role of microcavities in adjusting the emitting color of QLEDs. By enhanced microcavity and properly chosen spacer thickness, the spectral selectivity shifts, realizing the full-color-tunability of QLEDs. The tunable experimental spectra of microcavity QLEDs are observed, in excellent agreement with our theoretical design. Benefiting from the spectral narrowing of microcavity and the narrow spectra of QDs, a high color purity with full width at half maximum (FWHM) of 18 to 25 nm is realized, leading to a color gamut ratio of 104.8&#x0025; compared to National Television System Committee (NTSC) standard. The light extraction is also enhanced by constructive interference and the Purcell effect in the microcavity. Moreover, in the fabrication of red, green, and blue pixels, patterning the transparent cathode has better feasibility and lower damage relative to patterning the light-emitting layer. Optoelectronics ; Quantum dot ; Gamut ; Materials science ; Light-emitting diode ; Full width at half maximum ; Diode ; Optics ; Electroluminescence ; Spectral width ; Layer (electronics) ; Physics ; Wavelength ; Nanotechnology Quantum Dots Synthesis And Properties ; Semiconductor Quantum Structures and Devices ; Photonic Crystals and Applications
Authors: Cristian Buendía‐Atencio | Gilles Pieffet | Vaneza Paola Lorett-Velásquez
Source: Journal of Chemical Education (article, 2022-04-13)
Field: Chemistry — Physical and Theoretical Chemistry

WERNER: A Card Game for Reinforcement Learning of Inorganic Chemistry Nomenclature Identifying and naming inorganic compounds is sometimes a challenge for many first-year chemistry and engineering students; however, these difficulties can be overcome after extensive practice using homework sets based on naming inorganic compounds or applying inorganic nomenclature rules. Werner is a card game in honor of Professor Alfred Werner for his great contributions to inorganic chemistry and is intended as a didactic tool for reinforcing the learning of inorganic chemistry nomenclature. By playing the game, students practice and apply inorganic nomenclature rules. During the game, they get to identify compounds such as acids, bases, and salts as well as practice the systematic rules of the new chemical elements’ symbols and names. In the classroom, the game was used immediately after explaining the basic nomenclature rules. Overall, the students found the game engaging and enjoyable, and they responded favorably to its use as an educational didactic tool. Nomenclature ; Chemical nomenclature ; Honor ; Chemistry ; Computer science ; Mathematics education ; Psychology ; Organic chemistry ; Taxonomy (biology) Various Chemistry Research Topics ; Science Education and Pedagogy ; Problem and Project Based Learning
Authors: Xiaomei Wang | Cen Shi | Jingwen Guan | Yemeng Chen | Yigong Xu | Juan Diwu | Shuao Wang
Source: Chinese Chemical Letters (article, 2022-04-11)
Field: Chemistry — Inorganic Chemistry

The development of molecular and nano actinide decorporation agents Context (archaeology) ; Chemistry ; Actinide ; Transuranium element ; Nuclear chemistry ; Biology Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Nuclear materials and radiation effects
Authors: Buwei Wang | Qian Fan | Yunliang Yue
Source: Journal of Physics Condensed Matter (article, 2022-02-21)
Field: Materials Science — Materials Chemistry

Study of crystal properties based on attention mechanism and crystal graph convolutional neural network The prediction of crystal properties has always been limited by huge computational costs. In recent years, the rise of machine learning methods has gradually made it possible to study crystal properties on a large scale. We propose an attention mechanism-based crystal graph convolutional neural network, which builds a machine learning model by inputting crystallographic information files and target properties. In our research, the attention mechanism is introduced in the crystal graph convolutional neural network (CGCNN) to learn the local chemical environment, and node normalization is added to reduce the risk of overfitting. We collect structural information and calculation data of about 36 000 crystals and examine the prediction performance of the models for the formation energy, total energy, bandgap, and Fermi energy of crystals in our research. Compared with the CGCNN, it is found that the accuracy (ACCU) of the predicted properties can be further improved to varying degrees by the introduction of the attention mechanism. Moreover, the total magnetization and bandgap can be classified under the same neural network framework. The classification ACCU of wide bandgap semiconductor crystals with a bandgap threshold of 2.3 eV reaches 93.2%, and the classification ACCU of crystals with a total magnetization threshold of 0.5 <i>μ</i><sub>B</sub>reaches 88.8%. The work is helpful to realize large-scale prediction and classification of crystal properties, accelerating the discovery of new functional crystal materials. Crystal (programming language) ; Convolutional neural network ; Band gap ; Artificial neural network ; Graph ; Normalization (sociology) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Ga2O3 and related materials
Authors: Jiandong Deng | Guozhu Jia
Source: Chemical Physics (article, 2022-01-17)
Field: Materials Science — Materials Chemistry

An interpretable hybrid Machine learning prediction of dielectric constant of alkali halide crystals Dielectric ; Particle swarm optimization ; Chemistry ; Crystal (programming language) ; Halide ; Alkali metal ; Polarization (electrochemistry) ; Machine learning ; Computational physics ; Statistical physics ; Materials science ; Computer science ; Optoelectronics ; Physical chemistry ; Physics ; Inorganic chemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Ivonne Sgura | Luca Mainetti | Francesco Negro | Maria Grazia Quarta | Benedetto Bozzini
Source: Journal of Computational Science (article, 2022-11-11)
Field: Materials Science — Polymers and Plastics

Deep-learning based parameter identification enables rationalization of battery material evolution in complex electrochemical systems Computer science ; Battery (electricity) ; Artificial intelligence ; Deep learning ; Context (archaeology) ; Identification (biology) ; Throughput ; Process (computing) ; Power (physics) Conducting polymers and applications ; Advanced Battery Technologies Research ; Transition Metal Oxide Nanomaterials
Authors: Mohammad Tohidi Vahdat | Kumar Varoon Agrawal | Giovanni Pizzi
Source: Machine Learning Science and Technology (article, 2022-10-19)
Field: Materials Science — Materials Chemistry

Machine-learning accelerated identification of exfoliable two-dimensional materials Abstract Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by a weak binding energy that, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers. Computer science ; Random forest ; Machine learning ; Identification (biology) ; Artificial intelligence ; Artificial neural network ; Classifier (UML) ; Focus (optics) ; Deep learning ; Variety (cybernetics) ; Physics Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Xuanchen Liu | Kayoung Park | Magnus So | Shota Ishikawa | Takeshi Terao | Kazuhiko Shinohara | Chiyuri Komori | Naoki Kimura | Gen Inoue | Yoshifumi Tsuge
Source: Journal of Power Sources Advances (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

3D generation and reconstruction of the fuel cell catalyst layer using 2D images based on deep learning The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs. Tortuosity ; Microstructure ; Materials science ; Porosity ; Chemical engineering ; Composite material ; Engineering Machine Learning in Materials Science ; Advanced Neural Network Applications ; Fuel Cells and Related Materials
Authors: Lei Zhang | Mu He
Source: Journal of Applied Physics (article, 2022-02-09)
Field: Materials Science — Materials Chemistry

Unsupervised machine learning for solar cell materials from the literature Machine learning and data-driven methods have been adopted for material science research in recent years; yet, the textual data are not fully embraced by the materials and physics community. In this work, we aim to make the computers unsupervisedly learn the latent information on the solar cell materials based on the textual data with minimal human intervention and perform solar cell materials predictions. An unsupervised machine learning model is constructed by automatically extracting the information from the materials literature database using word embeddings, which successfully establishes the hidden relationships between the materials formulas and their photovoltaic applications. Uncommon solar cell materials predicted by the natural language processing (NLP)-based machine learning method are further evaluated via the first-principles methods to reveal the optoelectronic properties of the predicted candidate, demonstrating the validity of the NLP-assisted machine learning model. This study highlights the text-based machine learning methods for solar cell materials and calls for a wide deployment of the NLP methods for the materials research. Artificial intelligence ; Computer science ; Machine learning ; Natural language processing ; Unsupervised learning ; Solar cell ; Engineering Machine Learning in Materials Science ; Topic Modeling ; Computational Drug Discovery Methods
Authors: Xingjun Liu | Qinghua Peng | Shaobin Pan | Jingtao Du | Shuiyuan Yang | Jiajia Han | Yong Lu | Jinxin Yu | Cuiping Wang
Source: Metals (article, 2022-05-05)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Prediction of Microstructures and Young’s Modulus of Biomedical Multi-Component β-Ti Alloys Recently, the development of β-titanium (Ti) alloys with a low Young’s modulus as human implants has been the trend of research in biomedical materials. However, designing β-titanium alloys by conventional experimental methods is too costly and inefficient. Therefore, it is necessary to propose a method that can efficiently and reliably predict the microstructures and the mechanical properties of biomedical titanium alloys. In this study, a machine learning prediction method is proposed to accelerate the design of biomedical multi-component β-Ti alloys with low moduli. Prediction models of microstructures and Young’s moduli were built at first. The performances of the models were improved by introducing new experimental data. With the help of the models, a Ti–13Nb–12Ta–10Zr–4Sn (wt.%) alloy with a single β-phase microstructure and Young’s modulus of 69.91 GPa is successfully developed. This approach could also be used to design other advanced materials. Titanium alloy ; Microstructure ; Materials science ; Modulus ; Component (thermodynamics) ; Elastic modulus ; Young's modulus ; Titanium ; Alloy ; Metallurgy ; Computer science ; Composite material ; Thermodynamics Titanium Alloys Microstructure and Properties ; Orthopaedic implants and arthroplasty ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Gaoyang Li | Yuting Guo | Takuya Mabuchi | Donatas Surblys | Taku Ohara | Takashi Tokumasu
Source: Journal of Molecular Liquids (article, 2022-01-10)
Field: Materials Science — Materials Chemistry

Prediction of the adsorption properties of liquid at solid surfaces with molecular scale surface roughness via encoding-decoding convolutional neural networks Convolutional neural network ; Visualization ; Deep learning ; Surface roughness ; Encoding (memory) ; Materials science ; Adsorption ; Surface finish ; Nanoscopic scale ; Scale (ratio) ; Decoding methods ; Computer science ; Artificial neural network ; Artificial intelligence ; Molecular dynamics ; Biological system ; Computational science ; Algorithm ; Nanotechnology ; Chemistry ; Physics ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies ; Theoretical and Computational Physics
Authors: Ibrahim Dubdub | Zaid Alhulaybi
Source: Polymers (article, 2022-12-24)
Field: Materials Science — Materials Chemistry

Catalytic Pyrolysis of PET Polymer Using Nonisothermal Thermogravimetric Analysis Data: Kinetics and Artificial Neural Networks Studies This paper presents the catalytic pyrolysis of a constant-composition mixture of zeolite β and polyethylene terephthalate (PET) polymer by thermogravimetric analysis (TGA) at different heating rates (2, 5, 10, and 20 K/min). The thermograms showed only one main reaction and shifted to higher temperatures with increasing heating rate. In addition, at constant heating rate, they moved to lower temperatures of pure PET pyrolysis when a catalyst was added. Four isoconversional models, namely, Kissinger−Akahira−Sunose (KAS), Friedman, Flynn−Wall−Qzawa (FWO), and Starink, were applied to obtain the activation energy (Ea). Values of Ea acquired by these models were very close to each other with average value of Ea = 154.0 kJ/mol, which was much lower than that for pure PET pyrolysis. The Coats−Redfern and Criado methods were employed to set the most convenient solid-state reaction mechanism. These methods revealed that the experimental data matched those obtained by different mechanisms depending on the heating rate. Values of Ea obtained by these two models were within the average values of 157 kJ/mol. An artificial neural network (ANN) was utilized to predict the remaining weight fraction using two input variables (temperature and heating rate). The results proved that ANN could predict the experimental value very efficiently (R2 > 0.999) even with new data. Thermogravimetric analysis ; Kinetics ; Pyrolysis ; Catalysis ; Materials science ; Polymer ; Artificial neural network ; Chemical engineering ; Chemistry ; Organic chemistry ; Composite material ; Computer science ; Artificial intelligence ; Engineering Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Chemical Thermodynamics and Molecular Structure
Authors: Jong Youl Choi | Pei Zhang | Kshitij Mehta | Andrew E. Blanchard | Massimiliano Lupo Pasini
Source: Journal of Cheminformatics (article, 2022-10-17)
Field: Materials Science — Materials Chemistry

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter. Scalability ; HOMO/LUMO ; Convolutional neural network ; Computer science ; Graph ; Training (meteorology) ; Theoretical computer science ; Artificial intelligence ; Molecule ; Computational science ; Chemistry ; Physics ; Database ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Feng Zhao | Chenhui Lei | Qingkun Zhao | Huiya Yang | Guoping Ling | Jiabin Liu | Haofei Zhou | Hongtao Wang
Source: Materials Today Communications (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Predicting the property contour-map and optimum composition of Cu-Co-Si alloys via machine learning Materials science ; Phase diagram ; Precipitation ; Phase (matter) ; Microstructure ; Alloy ; Dimension (graph theory) ; Thermodynamics ; Metallurgy ; Mathematics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties