jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sourav Mondal | Alessandro Lunghi
Source: npj Computational Materials (article, 2023-07-11)
Field: Materials Science — Materials Chemistry

Spin-phonon decoherence in solid-state paramagnetic defects from first principles Abstract Paramagnetic defects in diamond and hexagonal boron nitride possess a combination of spin and optical properties that make them prototypical solid-state qubits. Despite the coherence of these spin qubits being critically limited by spin-phonon relaxation, a full understanding of this process is not yet available. Here we apply ab initio spin dynamics simulations to this problem and quantitatively reproduce the experimental temperature dependence of spin relaxation time and spin coherence time. We demonstrate that low-frequency two-phonon modulations of the zero-field splitting are responsible for spin relaxation and decoherence, and point to the nature of vibrations in 2-dimensional materials as the culprit for their shorter coherence time. These results provide an interpretation to spin-phonon decoherence in solid-state paramagnetic defects, offer a strategy to correctly interpret experimental results, and pave the way for the accelerated design of spin qubits. Quantum decoherence ; Paramagnetism ; Condensed matter physics ; Phonon ; Spin (aerodynamics) ; Coherence (philosophical gambling strategy) ; Qubit ; Spin engineering ; Relaxation (psychology) ; Physics ; Quantum mechanics ; Spin polarization ; Quantum Diamond and Carbon-based Materials Research ; Electronic and Structural Properties of Oxides ; High-pressure geophysics and materials
Authors: Zhiyuan Liu | Sihang Li | Yanchen Luo | Fei Hao | Yixin Cao | Kenji Kawaguchi | Xiang Wang | Tat‐Seng Chua
Source:  (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception — a critical ability of human professionals in comprehending molecules’ topological structures. To bridge this gap, we propose MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter. MolCA enables an LM (i.e., Galactica) to understand both text- and graph-based molecular contents via the cross-modal projector. Specifically, the cross-modal projector is implemented as a Q-Former to connect a graph encoder’s representation space and an LM’s text space. Further, MolCA employs a uni-modal adapter (i.e., LoRA) for the LM’s efficient adaptation to downstream tasks. Unlike previous studies that couple an LM with a graph encoder via cross-modal contrastive learning, MolCA retains the LM’s ability of open-ended text generation and augments it with 2D graph information. To showcase its effectiveness, we extensively benchmark MolCA on tasks of molecule captioning, IUPAC name prediction, and molecule-text retrieval, on which MolCA significantly outperforms the baselines. Computer science ; Modal ; Adapter (computing) ; Graph ; Theoretical computer science ; Encoder ; Natural language processing ; Pairwise comparison ; Artificial intelligence Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Ryota Kitani | Shinya Iwata
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Verification of Interpretability of Phase-Resolved Partial Discharge Using a CNN With SHAP Deep neural networks can be used to distinguish partial discharge (PD) signals despite their complexity. This study analyzes the appropriateness of interpreting phase-resolved partial discharge (PRPD) signals using a convolutional neural network (CNN) through the Shapley additive explanation (SHAP) method. The generated PRPD signals were accumulated by applying AC voltage to four types of electrodes with a polyethylene sheet, followed by their conversion into scattered images to construct a classification model, CNN. The SHAP values for each pixel in the test images were then calculated. The result indicated that the pixels around the 0 V line retained high absolute SHAP values in every label, and the average of the summation of absolute SHAP values over all labels and all test images, which indicates the weight of each pixel, shows a similar tendency. Additionally, insight tests of the two CNN models were conducted, and the results showed that some structural defects could be detected by visualizing the SHAP values for each pixel. Finally, the verification of parameter-and-data vulnerability showed that SHAP has sufficient endurance against some types of instability in the data and model. Although the SHAP method lacks a perfect causal model because of its origin, the results imply that in appropriate use cases, weights on classifications of PD signals could be described by SHAP’s interpretability. Interpretability ; Pixel ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Artificial neural network ; Algorithm High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Power Transformer Diagnostics and Insulation
Authors: Seid Reza Falsafi | Fuat Topuz | Zahra Esfandiari | Aslı Can Karaça | Seid Mahdi Jafari | Hadis Rostamabadi
Source: Food Chemistry X (article, 2023-10-06)
Field: Materials Science — Biomaterials

Recent trends in the application of protein electrospun fibers for loading food bioactive compounds Electrospun fibers (EFs) have emerged as promising one-dimensional materials for a myriad of research/commercial applications due to their outstanding structural and physicochemical features. Polymers of either synthetic or natural precursors are applied to design EFs as carriers for bioactive compounds. For engineering food systems, it is crucial to exploit polymers characterized by non-toxicity, non-immunogenicity, biocompatibility, slow/controllable biodegradability, and structural integrity. The unique attributes of protein-based biomaterials endow a wide diversity of desirable features to EFs for meeting the requirements of advanced food/biomedical applications. In this review paper, after an overview on electrospinning, different protein materials (plant- and animal-based) as biodegradable/biocompatible building blocks for designing EFs will be highlighted. The potential application of protein-based EFs in loading bioactive compounds with the intention to inspire interests in both academia and industry will be summarized. This review concludes with a discussion of prevailing challenges in using protein EFs for the bioactive vehicle development. Biocompatible material ; Biocompatibility ; Nanotechnology ; Electrospinning ; Natural polymers ; Biodegradation ; Materials science ; Biochemical engineering ; Polymer ; Chemistry ; Engineering ; Biomedical engineering ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Silk-based biomaterials and applications ; Nanocomposite Films for Food Packaging
Authors: Leila T. Alexander | Janani Durairaj | Andriy Kryshtafovych | Luciano A. Abriata | Yusupha Bayo | Gira Bhabha | Cécile Breyton | Simon G. Caulton | James Chen | Séraphine Degroux | Damian C. Ekiert | Benedikte S. Erlandsen | Peter L. Freddolino | Dominic Gilzer | Chris Greening | Jonathan M. Grimes | Rhys Grinter | Manickam Gurusaran | Marcus D. Hartmann | Charlie J. Hitchman | J.R. Keown | Ashleigh Kropp | Petri Kursula | Andrew L. Lovering | Bruno Lemaître | Andrea Lia | Shiheng Liu | Maria Logotheti | Shuze Lu | Sigurbjörn Markússon | Mitchell D. Miller | G. Minasov | Hartmut H. Niemann | Felipe Opazo | G.N. Phillips | Owen R. Davies | Samuel Rommelaere | Mónica Rosas‐Lemus | Pietro Roversi | K.J.F. Satchell | Nathan Smith | Mark A. Wilson | Kuan‐Lin Wu | Xian Xia | Han Xiao | Wenhua Zhang | Z. Hong Zhou | Krzysztof Fidelis | Maya Topf | John Moult | Torsten Schwede
Source: Proteins Structure Function and Bioinformatics (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

Protein target highlights in <scp>CASP15</scp> : Analysis of models by structure providers We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the submitted predictions. While the overall ability to predict three-dimensional protein structures continues to impress, reproducing uncommon features not previously observed in experimental structures is still a challenge. Furthermore, instances with conformational flexibility and large multimeric complexes highlight the need for novel scoring strategies to better emphasize biologically relevant structural regions. Looking ahead, closer integration of computational and experimental techniques will play a key role in determining the next challenges to be unraveled in the field of structural molecular biology. Flexibility (engineering) ; Computational biology ; Key (lock) ; Computer science ; Protein structure prediction ; Data science ; Protein structure ; Biology ; Biochemistry ; Mathematics Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Congwei Xie | Evgenii Tikhonov | Dongdong Chu | Mengfan Wu | Ivan A. Kruglov | Shilie Pan | Zhihua Yang
Source: Science China Materials (article, 2023-10-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A prediction-driven database to enable rapid discovery of nonlinear optical materials Metastability ; Materials science ; Nonlinear optical ; Second-harmonic generation ; Band gap ; Nonlinear optics ; Nonlinear system ; Crystal (programming language) ; Work (physics) ; Laser ; Optoelectronics ; Computer science ; Optics ; Physics ; Thermodynamics ; Quantum mechanics Crystal Structures and Properties ; Nonlinear Optical Materials Research ; Photorefractive and Nonlinear Optics
Authors: Paula Carrillo-Rodríguez | Frode Selheim | Maria Hernandez-Valladares
Source: Cancers (review, 2023-01-16)
Field: Chemistry — Spectroscopy

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them. Proteomics ; Workflow ; Computer science ; Proteome ; Relevance (law) ; Selection (genetic algorithm) ; Quantitative proteomics ; Biomarker discovery ; Data science ; Computational biology ; Bioinformatics ; Artificial intelligence ; Chemistry ; Biology ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Risheng Pei | Zhuocheng Xie | Sangbong Yi | Sandra Korte‐Kerzel | Julien Guénolé | Talal Al‐Samman
Source: Scripta Materialia (article, 2023-03-17)
Field: Materials Science — Biomaterials

Atomistic insights into the inhomogeneous nature of solute segregation to grain boundaries in magnesium Grain boundary ; Materials science ; Atom probe ; Atomic units ; Magnesium ; Context (archaeology) ; Molecular dynamics ; Texture (cosmology) ; Atom (system on chip) ; Condensed matter physics ; Crystallography ; Chemical physics ; Microstructure ; Metallurgy ; Physics ; Computational chemistry ; Chemistry Magnesium Alloys: Properties and Applications ; Advanced Materials Characterization Techniques ; Aluminum Alloy Microstructure Properties
Authors: Yilin Mao | He Li | Yu Wang | Kai Fan | Jiazhi Shen | Jie Zhang | Xiao Han | Yujie Song | Caihong Bi | Litao Sun | Zhaotang Ding
Source: Frontiers in Plant Science (article, 2023-02-02)
Field: Chemistry — Analytical Chemistry

Low temperature response index for monitoring freezing injury of tea plant Freezing damage has been a common natural disaster for tea plantations. Quantitative detection of low temperature stress is significant for evaluating the degree of freezing injury to tea plants. Traditionally, the determination of physicochemical parameters of tea leaves and the investigation of freezing damage phenotype are the main approaches to detect the low temperature stress. However, these methods are time-consuming and laborious. In this study, different low temperature treatments were carried out on tea plants. The low temperature response index (LTRI) was established by measuring seven low temperature-induced components of tea leaves. The hyperspectral data of tea leaves was obtained by hyperspectral imaging and the feature bands were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE). The LTRI and seven indexes of tea plant were modeled by partial least squares (PLS), support vector machine (SVM), random forests (RF), back propagation (BP) machine learning methods and convolutional neural networks (CNN), long short-term memory (LSTM) deep learning methods. The results indicated that: (1) the best prediction model for the seven indicators was LTRI-UVE-CNN (R<sup>2 =</sup> 0.890, RMSEP=0.325, RPD=2.904); (2) the feature bands screened by UVE algorithm were more abundant, and the later modeling effect was better than CARS and SPA algorithm; (3) comparing the effects of the six modeling algorithms, the overall modeling effect of the CNN model was better than other models. It can be concluded that out of all the combined models in this paper, the LTRI-UVE-CNN was a promising model for predicting the degree of low temperature stress in tea plants. Hyperspectral imaging ; Support vector machine ; Convolutional neural network ; Feature (linguistics) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Partial least squares regression ; Biological system ; Mathematics ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote Sensing and Land Use
Authors: Fei Tan | Xiaoming Mo | Shiwei Ruan | Tianying Yan | Peng Xing | Pan Gao | Wei Xu | Weixin Ye | Yong‐Quan Li | Xiuwen Gao | Tianxiang Liu
Source: Foods (article, 2023-09-28)
Field: Chemistry — Analytical Chemistry

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R<sup>2</sup> of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R<sup>2</sup> of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits. Hyperspectral imaging ; Partial least squares regression ; Titratable acid ; Principal component analysis ; Near-infrared spectroscopy ; Fusion ; Support vector machine ; Artificial intelligence ; Sensor fusion ; Mathematics ; Pattern recognition (psychology) ; Chemistry ; Computer science ; Food science ; Optics ; Statistics ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Meng Xu | Qundao Xu | Rongchuan Gu | Songyou Wang | Cai‐Zhuang Wang | Kai‐Ming Ho | Zhongrui Wang | Ming Xu | Xiangshui Miao
Source: Advanced Functional Materials (article, 2023-08-01)
Field: Materials Science — Materials Chemistry

Tailoring Mid‐Gap States of Chalcogenide Glass by Pressure‐Induced Hypervalent Bonding Towards the Design of Electrical Switching Materials Abstract Phase change memory (PCM) and ovonic threshold switching (OTS) materials using chalcogenide glass are essential elements in advanced 3D memory chips. The mid‐gap states, induced by the disorder and defects in the glass, are the physical mechanisms of the electrical switching behavior, while the origin of these trap states is still under debate and the medium‐range clusters that break the global octet rule, such as over‐coordinated atoms, are known to be responsible in various glass. Here, it is discovered that a large fraction of over‐coordinated clusters fails to generate mid‐gap states, which are probably caused by hypervalent bonding, a multi‐centered covalent bond participated by delocalized lone‐pair electrons. This is confirmed by the pressure‐driven simulations of amorphous GeSe models, in which it is found that octahedral motifs and hypervalent bonds prevent the over‐coordinated medium‐range clusters from providing excessive electrons. In practical applications, compatible dopants can be used to change the number of hypervalent bonds, thus controlling the number of mid‐gap states and consequently the performance of PCM and OTS materials. These results reveal the origin of mid‐gap states in chalcogenide glasses, enabling extensive control in the development of pioneering electrical switching materials. Hypervalent molecule ; Materials science ; Chalcogenide ; Amorphous solid ; Chalcogenide glass ; Band gap ; Delocalized electron ; Lone pair ; Dopant ; Electron ; Nanotechnology ; Condensed matter physics ; Chemical physics ; Optoelectronics ; Crystallography ; Doping ; Metallurgy ; Molecule Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Perovskite Materials and Applications
Authors: Zhou Zeng | Pan Guo | Ruoshuang Zhang | Zhirui Zhao | Jiankang Bao | Qian Wang | Zheng Xu
Source: Polymers (review, 2023-02-24)
Field: Materials Science — Materials Chemistry

Review of Aging Evaluation Methods for Silicone Rubber Composite Insulators Silicone rubber insulation material is widely used for the external insulation of power systems. During the continuous service of a power grid, it will be seriously aged due to the influence of high voltage electric fields and harsh climate environments, which will reduce its insulation performance and service life and cause transmission line failure. How to evaluate the aging performance of silicone rubber insulation materials scientifically and accurately is a hot and difficult issue in the industry. Starting from the composite insulator, which is the most widely used insulating device of silicone rubber insulation materials, this paper expounds the aging mechanism of silicone rubber materials, analyzes the applicability and effectiveness of various existing aging tests and evaluation methods, especially discusses the magnetic resonance detection methods emerging in recent years, and finally summarizes the characterization and evaluation technology of the aging state of silicone rubber insulation materials. Silicone rubber ; Materials science ; Accelerated aging ; Service life ; Natural rubber ; Composite material ; Silicone ; Power transmission ; Insulator (electricity) ; Forensic engineering ; Power (physics) ; Engineering High voltage insulation and dielectric phenomena ; NMR spectroscopy and applications ; Power Transformer Diagnostics and Insulation
Authors: Kübra Kaygisiz | Lena Rauch‐Wirth | Arghya Dutta | Xiaoqing Yu | Yuki Nagata | Tristan Bereau | Jan Münch | Christopher V. Synatschke | Tanja Weil
Source: Nature Communications (article, 2023-08-23)
Field: Materials Science — Biomaterials

Data-mining unveils structure–property–activity correlation of viral infectivity enhancing self-assembling peptides Gene therapy via retroviral vectors holds great promise for treating a variety of serious diseases. It requires the use of additives to boost infectivity. Amyloid-like peptide nanofibers (PNFs) were shown to efficiently enhance retroviral gene transfer. However, the underlying mode of action of these peptides remains largely unknown. Data-mining is an efficient method to systematically study structure-function relationship and unveil patterns in a database. This data-mining study elucidates the multi-scale structure-property-activity relationship of transduction enhancing peptides for retroviral gene transfer. In contrast to previous reports, we find that not the amyloid fibrils themselves, but rather µm-sized β-sheet rich aggregates enhance infectivity. Specifically, microscopic aggregation of β-sheet rich amyloid structures with a hydrophobic surface pattern and positive surface charge are identified as key material properties. We validate the reliability of the amphiphilic sequence pattern and the general applicability of the key properties by rationally creating new active sequences and identifying short amyloidal peptides from various pathogenic and functional origin. Data-mining-even for small datasets-enables the development of new efficient retroviral transduction enhancers and provides important insights into the diverse bioactivity of the functional material class of amyloids. Computational biology ; Enhancer ; Transduction (biophysics) ; Amyloid (mycology) ; Infectivity ; Peptide ; Gene ; Mode of action ; Amyloid fibril ; Function (biology) ; Amphiphile ; Chemistry ; Biology ; Cell biology ; Biophysics ; Gene expression ; Biochemistry ; Amyloid β ; Genetics ; Virus ; Medicine ; Disease Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery ; Lipid Membrane Structure and Behavior