jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jessica Tseng | Jean Chien | Eric Wai Ming Lee
Source: Journal of Micro/Nanopatterning Materials and Metrology (article, 2024-12-27)
Field: Materials Science — Surfaces, Coatings and Films

Advanced defect recognition on scanning electron microscope images: a two-stage strategy based on deep convolutional neural networks for hotspot monitoring We focus on detecting defects during hotspot monitoring using scanning electron microscope (SEM) images from the after-development inspection (ADI) phase in semiconductor manufacturing. A primary challenge lies in the significant imbalance between defective and defect-free samples, complicating binary classification. To address this, data augmentation and the synthetic minority over-sampling technique (SMOTE) are applied to generate additional samples for the minority class. A two-stage learning approach is then proposed: first, deep convolutional neural networks are trained on SMOTE-synthesized data to develop a pre-trained defect classification model. Subsequently, this model is fine-tuned with raw, augmented data to enhance its performance on targeted data. Extensive experiments demonstrate that this method improves efficiency, reduces computational costs, and outperforms traditional techniques. The results show that integrating SMOTE-based augmentation with a two-stage learning process mitigates data imbalance and improves defect detection accuracy. This proposed methodology offers a robust solution for automating SEM image inspections during hotspot monitoring, addressing the scalability challenges posed by increasing design complexity and emerging defect types in advanced semiconductor processes. Convolutional neural network ; Hotspot (geology) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Stage (stratigraphy) ; Geology ; Paleontology ; Seismology Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Grzegorz Brus
Source: Journal of Physics Conference Series (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Combining physical modeling and machine learning for micro-scale modeling of a fuel cell electrode Abstract Microscale modeling plays a critical role in fuel cell development, offering deep insights into the microscale transport phenomena and electrochemical reactions. This level of detail is essential for optimizing the performance of a single fuel cell, enabling the precise design and improvement of materials and structures at the microscale and consequently enhancing the overall efficiency of a stack. Here, we show a comprehensive transition from white-box models, characterized by their reliance on physical laws, to black-box models exemplified by neural networks, which excel in pattern recognition from provided data without necessitating a clear understanding of the underlying processes. This spectrum encompasses the inherent challenges and merits of both methodologies. While white-box models are recognized for their reliability due to their foundation in mathematical equations that describe physical phenomena, they often require the integration of empirical parameters and are susceptible to experimental errors, much like their black-box counterparts. The core novelty in this study lies in the synergistic integration of these two paradigms, specifically tailored for enhancing the predictive accuracy in solid oxide fuel cell modeling. In this approach, the neural network is employed to replace different parts of the mathematical model, from refining empirical parameters in the electrochemical model to replacing the entire electrochemical model. The adjustment of parameters is conducted by an evolutionary strategy based on the outputs of the mathematical model. The results underscore the superiority of the gray box in achieving higher prediction accuracy and in minimizing the requisite data volume for network training. This presented approach not only bridges the gap between the deterministic clarity of white-box models and the data-driven insights of black-box models but also strategically distributes the computational load between them, thereby offering a promising solution to the prevalent challenges in solid oxide fuel cell modeling. Microscale chemistry ; Black box ; Computer science ; Artificial neural network ; Artificial intelligence ; Multiscale modeling ; Scale (ratio) ; Mathematical model ; Machine learning Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Mostafa Siavashi | Morteza Dardel | Mohammad Hadi Pashaei
Source: Journal of Energy Storage (article, 2024-11-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mathematical modeling and artificial neural network analysis of nano-scroll capacitors for energy storage Artificial neural network ; Scroll ; Capacitor ; Nano- ; Energy storage ; Computer science ; Artificial intelligence ; Engineering ; Electrical engineering ; Mechanical engineering ; Physics ; Thermodynamics ; Chemical engineering ; Voltage Supercapacitor Materials and Fabrication ; Advanced Memory and Neural Computing ; Advanced Sensor and Energy Harvesting Materials
Authors: Axel Vaillant | Grégory Beurier | Denis Cornet | Lauriane Rouan | Denis Vile | Cyrille Violle | François Vasseur
Source: BMC Plant Biology (article, 2024-11-20)
Field: Chemistry — Analytical Chemistry

NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user's choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology. Phenomics ; Biology ; Trait ; Context (archaeology) ; Arabidopsis thaliana ; Phenotypic trait ; Variety (cybernetics) ; Biological system ; Computer science ; Computational biology ; Artificial intelligence ; Phenotype ; Genomics ; Genetics ; Gene Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Metabolomics and Mass Spectrometry Studies
Authors: Tiantian Cheng | Weizhi Meng | Man Luo | Jiachi Xian | Wenjin Luo | Weijun Wang | Fangyu Yue | Johnny C. Ho | Chenhui Yu | Junhao Chu
Source: Small (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Advancements and Challenges in the Integration of Indium Arsenide and Van der Waals Heterostructures (Small 48/2024) Van der Waals Heterostructures In article number 2403129, Man Luo, Johnny C. Ho, Chenhui Yu, and co-workers review advancements in integrating low-dimensional InAs-based materials with van der Waals systems. They propose innovative approaches such as selective area growth, remote epitaxy, multifunctional applications, and incorporating deep learning into first-principles calculations to overcome challenges and drive transformative progress in the field. van der Waals force ; Indium ; Heterojunction ; Materials science ; Gallium arsenide ; Indium arsenide ; Nanotechnology ; Engineering physics ; Optoelectronics ; Condensed matter physics ; Physics ; Quantum mechanics ; Molecule 2D Materials and Applications ; Inorganic Chemistry and Materials ; Quantum Dots Synthesis And Properties
Authors: Brianne R. King | Kiera H. Sumida | Jessica L. Caruso | David Baker | Jesse G. Zalatan
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-04-19)
Field: Chemistry — Inorganic Chemistry

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially-relevant non-native functions. For the Fe(II)/αKG enzyme tP4H, we performed site-saturation mutagenesis with both the wild-type and stabilized design variant and screened for activity increases in a non-native C-H hydroxylation reaction. We observed substantially larger increases in non-native activity for variants obtained from the stabilized scaffold compared to those from the wild-type enzyme. ProteinMPNN is user-friendly and widely-accessible, and straightforward structural criteria were sufficient to obtain stabilized, catalytically-functional variants of the Fe(II)/αKG enzymes tP4H and GriE. Our work suggests that stabilization by computational sequence redesign could be routinely implemented as a first step in directed evolution campaigns for novel biocatalysts. Heme ; Function (biology) ; Enzyme ; Computer science ; Chemistry ; Biochemistry ; Biology ; Cell biology Metal-Catalyzed Oxygenation Mechanisms ; Metalloenzymes and iron-sulfur proteins ; Porphyrin Metabolism and Disorders
Authors: Mahdieh Grailoo | Tooraj Nikoubin | Jose Nunez‐Yanez
Source:  (article, 2024-10-29)
Field: Materials Science — Materials Chemistry

Hardware Acceleration of Molecular Property Graph Prediction on a Heterogeneous Edge Platform Molecular property prediction is a critical task in computer-aided drug discovery workflows. In this context, representing molecules as graph structures has become a common approach, leading to the development of graph-based neural networks (GNNs). A prominent type of GNN are the graph convolution network (GCN) that incorporates a number of graph convolution layers followed by fully connected layers in the form of multi layer perceptrons (MLP). Addressing the need for enhanced efficiency, this paper introduces an innovative approach that maps different computation types to specialized accelerators in a heterogeneous edge platform. The proposed system integrates an FPGA accelerator for the sparse operations present in the GCN layer and an Edge TPU for ordinary dense operations in the fully connected layer. The FPGA accelerator is described using High-Level Synthesis (HLS) and optimized to handle the sparsity found on the graph structure and node features while the Edge TPU deploys a systolic array of MAC units optimized for dense matrix multiplication. This hardware/software co-designed GCN+MLP architecture utilizes Pynq and TensorFlow Lite Runtime functions, executed on a multi-core ARM CPU available in an AMD/Xilinx Zynq ultrascale+ device, in conjunction with the Edge TPU and programmable logic. Our results demonstrate performance improvements, achieving speedups of up to 47x compared to traditional software implementations. Computer science ; Property (philosophy) ; Acceleration ; Enhanced Data Rates for GSM Evolution ; Hardware acceleration ; Graph ; Distributed computing ; Theoretical computer science ; Software ; Artificial intelligence ; Programming language ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Sajad Jaberi | Gholamreza Moradi | S.A. Alavi Fazel
Source: Lubrication Science (article, 2024-01-07)
Field: Materials Science — Surfaces, Coatings and Films

Laboratory measuring of contact angles for liquid/solid surface and analysis with artificial neural network Abstract In this study, the advancing and receding contact angles of three types of pure liquid on four different surfaces have been measured. The contact angles of the droplet have been measured by analysing the captured photo by tilted plate method. Droplet behaviour has been modelled by two experimental equations. Additionally, the data has been modelled by an artificial neural network using genetic algorithm and a hyperbolic tangent activation function. The input parameters are density, molecular weight of pure liquid and solid, viscosity and surface tension of pure liquid, roughness of the solid surface and the two outputs are advancing and receding contact angles of the droplet. Number of 81 data points were used for training, 27 data for validation and 28 data for testing. The topography of {7,7,2} for artificial neural network has been proposed. The resulting RMS errors were 8%, 8% and 7% for training, validation and testing, respectively. Artificial neural network ; Contact angle ; Tangent ; Surface roughness ; Surface tension ; Surface (topology) ; Hyperbolic function ; Surface finish ; Materials science ; Viscosity ; Biological system ; Computer science ; Geometry ; Mathematics ; Artificial intelligence ; Thermodynamics ; Physics ; Composite material Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Fluid Dynamics and Heat Transfer
Authors: Timo Sachsenberg | Lindsay K. Pino | Marie A. Brunet | Isabell Bludau | Oliver Kohlbacher | Juan Antonio Vizcaíno | Wout Bittremieux
Source: Bioinformatics Advances (article, 2024-12-26)
Field: Chemistry — Spectroscopy

Perspectives in computational mass spectrometry: recent developments and key challenges <b>Summary</b>: Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabolomics, lipidomics, glycomics, and beyond. As the field continues to evolve, rapid advancements in instrumentation, acquisition strategies, machine learning, and scalable computing have reshaped the landscape of computational MS. This perspective reviews recent developments and highlights key challenges, including data harmonization, statistical confidence estimation, repository-scale analysis, multi-omics integration, and privacy in clinical MS. We also discuss the increasing importance of machine learning and the need to build corresponding literacy within the community. Finally, we reflect on the role of the Computational Mass Spectrometry (CompMS) Community of Special Interest of the International Society for Computational Biology in supporting collaboration, innovation, and knowledge exchange. With MS-based technologies now central to both basic and translational research, continued investment in robust and reproducible computational methods will be essential to realize their full potential. Grand Challenges ; Key (lock) ; Field (mathematics) ; Cornerstone ; Computational model Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Agha Ndukwe | Nelson Azolibe | Kooffreh Okon | Promise Christopher | Michael T. Collins | Chibuike Christopher Ozoh | Paul Obasi | Collins Eze | Augustus Ezem | C. Thomas | Chijioke Ogbodo
Source: Acta periodica technologica (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Prediction of hardness of palm inter-fruitlet membrane reinforced high-density polyethylene-waste (HDPEw) composites This study concerns the prediction of the hardness of reinforced high-density polyethylene waste (HDPEw) composites. The locally sourced palm inter-fruitlet membrane served as the reinforcing (filler) material while the Yoghurt Can wastes constituted the polymer matrix. The palm inter-fruitlet membrane, used for the study were pulverized and sieved to fine particle sizes. All filler particles passed through a mesh of 250 ?m. Consequently, the filler sample was characterized using DTA, TGA, and FTIR techniques whereas SEM was used to study the morphology of the produced composite. Different weight-percentage compositions of the filler were used to produce the examined samples with the following formulations: 100 % LDPEw, 6wt.%, 12wt.%, 18wt.%, and 24wt.% filler composites using the compression moulding method. On the other hand, hardness, flexural, tensile, and impact strengths were conducted to understand the mechanical behaviour of the produced composites. Multiple regression and artificial neural networks were used to predict the experimental hardness values in consideration of other independent variables like composite formulations, tensile, flexural, and impact strengths. The result of the TGA analysis showed the weight loss and degradation of the organic constituents in the filler while the DTA study revealed a variety of thermal occurrences and transitions indicating dehydration, phase change, and filler disintegration. The maximum hardness value of 76.67 HV was recorded for the composite with 24 wt.% filler while the composite formulation with 12 wt.% filler had the highest flexural and impact strengths of 41.87 MPa and 0.4979 J/mm2 respectively. The composite composition with 18 wt.% filler gave the highest tensile strength of 39.04 MPa. The unequal distribution of the filler within the HDPEw matrix was revealed by the SEM micrographs. The more uniformly dispersed composites with 12 and 18 wt.% fillers were seen to have improved mechanical properties whereas the reverse was the case for the 24 wt.% filler composite formulation which was found to exhibit directional reinforcement zones. The mean squared error assessment of the predicted hardness values indicated that predictions by multiple regression were more accurate than those that were obtained by ANN. This outcome could be caused by the relative linearity of the examined variables. Composite material ; Palm ; Materials science ; Polyethylene ; Pulp and paper industry ; Engineering ; Physics Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution
Authors: Muskan Agarwal | Kanwarpartap Singh Gill | Priyanshi Aggarwal | Ramesh Singh Rawat | G Sunil
Source:  (article, 2024-07-26)
Field: Chemistry — Analytical Chemistry

Enzyme Classification through Exploratory Data Analysis and Sequential Neural Network Modeling This research introduces a comprehensive approach for enzyme classification, merging Exploratory Data Analysis (EDA) with a Neural Network (NN) model. Leveraging a dataset of 858,777 labeled amino acid sequences, originating from ten organisms, the model predicts enzyme classes in a test set of 253,146 samples while excluding sequences above a set length. Preprocessing involves omitting rare amino acids (X, U, B, Z) and accounting for amino acids (B, Z) exclusive to the training set. The Neural Network architecture, comprising an embedding layer, bidirectional LSTM layers, and a dense output layer, yields promising results with 79% accuracy on the test set after 20 epochs. A detailed classification report and confusion matrix showcase the model's efficacy across 20 enzyme classes. This study advances enzyme classification methodologies, demonstrating the significance of EDA and NN integration in bioinformatics and molecular biology. Future directions involve exploring additional features and optimization strategies for continued model enhancement. Computer science ; Artificial neural network ; Artificial intelligence ; Exploratory data analysis ; Data mining ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Artificial Intelligence in Healthcare ; Machine Learning in Bioinformatics
Authors: Jiaqi Yu | Ke Li | Zhenghui Tian | Yang Qu | Lingrong Meng | Guofeng Wang
Source: Journal of Alloys and Compounds (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Near-infrared nonlinear WLEDs with high stability through atomic-level regulation and photosensitive resin encapsulating by 3D printing Infrared ; 3D printing ; Materials science ; Optoelectronics ; Chemistry ; Chemical engineering ; Nanotechnology ; Composite material ; Optics ; Physics Phase-change materials and chalcogenides ; Quantum Dots Synthesis And Properties ; Nonlinear Optical Materials Studies
Authors: F. Takano | Masaki HIRATSUKA | Kazuaki Z. Takahashi
Source: Scientific Reports (article, 2024-10-13)
Field: Materials Science — Materials Chemistry

Distinguish microphase-separated structures of diblock copolymers using local order parameters The microphase-separated structures of block copolymers are inherently highly ordered local structures, commonly characterized by differences in domain width and curvature. By focusing on diblock copolymers, we propose local order parameters (LOPs) that accurately distinguish between adjacent microphase-separated structures on the phase diagram. We used the Molecular Assembly structure Learning package for Identifying Order parameters (MALIO) to evaluate the structure classification performance of 186 candidate LOPs. MALIO calculates the numerical values of all candidate LOPs for the input microphase-separated structures to create a dataset, and then performs supervised machine learning to select the best LOPs quickly and systematically. We evaluated the robustness of the selected LOPs in terms of classification accuracy against variations in miscibility and fraction of block. The minimum local area size required for LOPs to achieve their classification performances is closely related to the characteristic sizes of the microphase-separated structures. The proposed LOPs are potentially applicable over a large area on the phase diagram. Materials science ; Copolymer ; Phase diagram ; Computer science ; Phase (matter) ; Polymer ; Physics ; Composite material Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Seiki Saitô | Shingo Sato | Hiroaki Nakamura | C. Takahashi | K. Sawada | K. Hoshino | Masahiro Kobayashi | Masahiro Hasuo
Source: Japanese Journal of Applied Physics (article, 2024-08-13)
Field: Materials Science — Materials Chemistry

Deep learning model for predicting the spatial distribution of binding energy from atomic configurations Abstract Understanding plasma-material interaction is crucial for achieving steady-state operation of magnetic confinement fusion devices. Kinetic Monte Carlo (kMC) simulation is a powerful tool for investigating the motion of atoms in the plasma facing materials under the influence of this interaction. To predict trapping sites and migration energies necessary for kMC simulations, we developed a deep learning model based on pix2pix for predicting the spatial distribution of binding energy. Results show that the model can reproduce spatial distributions similar to the true values. However, larger errors occur in regions with steep value gradients. Atomic model ; Distribution (mathematics) ; Statistical physics ; Energy (signal processing) ; Materials science ; Biological system ; Chemical physics ; Chemistry ; Physics ; Atomic physics ; Mathematics ; Quantum mechanics ; Mathematical analysis ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography