jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Salim Sazzed
Source: arXiv (Cornell University) (preprint, 2024-02-07)
Field: Materials Science — Materials Chemistry

Tracing and Segmentation of Molecular Patterns in 3-Dimensional Cryo-ET/EM Density Maps Through Algorithmic Image Processing and Deep Learning-Based Techniques Understanding the structures of biological macromolecules is highly important as they are closely associated with cellular functionalities. Comprehending the precise organization of actin filaments is crucial because they form the dynamic cytoskeleton, which offers structural support to cells and connects the cell’s interior with its surroundings. However, determining the precise organization of actin filaments is challenging due to the poor quality of cryo-electron tomography (cryo-ET) images, which suffer from low signal-to-noise (SNR) ratios and the presence of missing wedge, as well as diverse shape characteristics of actin filaments. To address these formidable challenges, the primary component of this dissertation focuses on developing sophisticated computational techniques for tracing actin filaments. In particular, three novel methodologies have been developed: i) BundleTrac, for tracing bundle-like actin filaments found in Stereocilium, ii) Spaghetti Tracer, for tracing filaments that move individually with loosely cohesive movements, and iii) Struwwel Tracer, for tracing randomly orientated actin filaments in the actin network. The second component of the dissertation introduces a convolutional neural network (CNN) based segmentation model to determine the location of protein secondary structures, such as helices and β-sheets, in medium-resolution (5-10Å) 3-dimensional cryo-electron microscopy (cryo-EM) images. This methodology later evolved into a tool named DeepSSETracer. The final component of the dissertation presents a novel algorithm, cylindrical fit measure, to estimate image/structure match at helix regions in medium-resolution cryo-EM images. Overall, my dissertation has made significant contributions to addressing critical research challenges in structural biology by introducing various computational methods and tools. Tracing ; Artificial intelligence ; Segmentation ; Computer science ; Image processing ; Deep learning ; Image segmentation ; Image (mathematics) ; Computer vision ; Pattern recognition (psychology) Machine Learning in Materials Science
Authors: Marina Leite
Source: ECS Meeting Abstracts (article, 2024-08-09)
Field: Materials Science — Materials Chemistry

(Invited) Forecasting Hybrid Perovskites’ Dynamic Response through Machine Learning Machine learning (ML) can significantly accelerate the discovery of materials for clean energy. Hybrid perovskites (an emerging class of materials for photovoltaics and light-emitting diodes) encompass an enormous chemical composition space, resulting in a hyperparameter space that is nearly impossible to be interrogated via traditional trial-and-error methods. Thus, there has been a pressing need within the materials research community to identify ML models that can be implemented to inform the physical and chemical behavior of the perovskites. Here, we apply distinct ML models (mostly based on recurrent neural networks) to classify and forecast physical properties encompassing electrical conductivity, photoluminescence response, the power conversion efficiency of photovoltaic devices, etc. Specifically, we use automated, in situ optical measurements to predict the optical response of a series of perovskites for 50+ hours, upon materials’ exposure to moisture. We quantitatively compare linear regression, echo state network, and seasonal auto-regressive integrated moving average with eXogenous regressor algorithms and achieve accuracy of &gt;90% for the latter. Demonstrating the ability of this model to capture non-linear features. Our high-throughput measurements and ML-assisted data analysis exemplifies the potential of ML to diagnose and forecast hybrid perovskites’ response with a variety of chemical compositions. Hyperparameter ; Machine learning ; Computer science ; Artificial neural network ; Artificial intelligence ; Photovoltaics ; Photovoltaic system ; Materials science ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Hanlei Zhang | Jincheng Bai | Xiabo Chen | Can Li | Chuan‐Jian Zhong | Jiye Fang | Guangwen Zhou
Source: arXiv (Cornell University) (preprint, 2024-09-25)
Field: Materials Science — Surfaces, Coatings and Films

Deep-Learning Recognition of Scanning Transmission Electron Microscopy: Quantifying and Mitigating the Influence of Gaussian Noises Scanning transmission electron microscopy (STEM) is a powerful tool to reveal the morphologies and structures of materials, thereby attracting intensive interests from the scientific and industrial communities. The outstanding spatial (atomic level) and temporal (ms level) resolutions of the STEM techniques generate fruitful amounts of high-definition data, thereby enabling the high-volume and high-speed analysis of materials. On the other hand, processing of the big dataset generated by STEM is time-consuming and beyond the capability of human-based manual work, which urgently calls for computer-based automation. In this work, we present a deep-learning mask region-based neural network (Mask R-CNN) for the recognition of nanoparticles imaged by STEM, as well as generating the associated dimensional analysis. The Mask R-CNN model was tested on simulated STEM-HAADF results with different Gaussian noises, particle shapes and particle sizes, and the results indicated that Gaussian noise has determining influence on the accuracy of recognition. By applying Gaussian and Non-Local Means filters on the noise-containing STEM-HAADF results, the influences of noises are largely mitigated, and recognition accuracy is significantly improved. This filtering-recognition approach was further applied to experimental STEM-HAADF results, which yields satisfying accuracy compared with the traditional threshold methods. The deep-learning-based method developed in this work has great potentials in analysis of the complicated structures and large data generated by STEM-HAADF. Transmission electron microscopy ; Gaussian ; Artificial intelligence ; Transmission (telecommunications) ; Scanning transmission electron microscopy ; Materials science ; Scanning electron microscope ; Electron ; Statistical physics ; Pattern recognition (psychology) ; Computer science ; Optics ; Physics ; Nanotechnology ; Telecommunications ; Nuclear physics Electron and X-Ray Spectroscopy Techniques
Authors: Jia Liu | Qais Qasaimeh
Source: Pan Pacific Symposium (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Physics-informed Machine Learning for Solder Design and Reliability Prediction for Electronics ABSTRACT We propose a physics-informed machine learning framework for solder materials design and reliability prediction for electronics. It includes a comprehensive set of factors that impact solder reliability and can work as a solder-agnostic framework for solder design and reliability prediction. This framework is built on a deep modular artificial neural network (ANN), with its structure imitating the relationships among elements and processes in electronics manufacturing, and its ANN modules model the elements and processes in electronic product manufacturing. It includes physics understandings of solder materials and their performance and provides a suitable machine learning framework with interpretability in understanding the solder design and reliability in electronics. A preliminary case study showed the superiority of the proposed framework in reliability prediction accuracy and interpretability. Reliability (semiconductor) ; Interpretability ; Artificial neural network ; Electronics ; Modular design ; Soldering ; Solder paste ; Set (abstract data type) Machine Learning in Materials Science ; Electronic Packaging and Soldering Technologies ; Model Reduction and Neural Networks
Authors: Bruno C. L. Rodrigues | Vinícius V. Santana | Sandris Murins | Idelfonso B. R. Nogueira
Source: arXiv (Cornell University) (preprint, 2024-02-19)
Field: Materials Science — Materials Chemistry

Molecule Generation and Optimization for Efficient Fragrance Creation This research introduces a Machine Learning-centric approach to replicate olfactory experiences, validated through experimental quantification of perfume perception. Key contributions encompass a hybrid model connecting perfume molecular structure to human olfactory perception. This model includes an AI-driven molecule generator (utilizing Graph and Generative Neural Networks), quantification and prediction of odor intensity, and refinery of optimal solvent and molecule combinations for desired fragrances. Additionally, a thermodynamic-based model establishes a link between olfactory perception and liquid-phase concentrations. The methodology employs Transfer Learning and selects the most suitable molecules based on vapor pressure and fragrance notes. Ultimately, a mathematical optimization problem is formulated to minimize discrepancies between new and target olfactory experiences. The methodology is validated by reproducing two distinct olfactory experiences using available experimental data. Biochemical engineering ; Chemistry ; Computer science ; Business ; Engineering Photochromic and Fluorescence Chemistry ; Advanced Polymer Synthesis and Characterization ; Luminescence and Fluorescent Materials
Authors: Cheng Hu | Zhi‐Jie Zheng | Yue Zhang | Feifei Chen | Alei Zhang | Kequan Chen | Peicheng Luo
Source: Process Biochemistry (article, 2024-12-05)
Field: Materials Science — Materials Chemistry

Enhancing thermostability of lysine hydroxylase via a semi-rational design Thermostability ; Rational design ; Lysine ; Chemistry ; Biochemistry ; Enzyme ; Biology ; Genetics ; Amino acid Enzyme Structure and Function ; Protein Structure and Dynamics ; Amino Acid Enzymes and Metabolism
Authors: Patxi Fernandez-Zelaia | Saket Thapliyal | Rangasayee Kannan | Peeyush Nandwana | Yukinori Yamamoto | Vincent Paquit | Andrzej Nycz | Michael Kirka
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Denoising Diffusion Probabilistic Models for Generative Alloy Design Computer science ; Probabilistic logic ; Generative grammar ; Machine learning ; Artificial intelligence ; Flexibility (engineering) ; Mathematics Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Thermography and Photoacoustic Techniques
Authors: Yuhao Xie | Zilong Wang | Qiang Chen | Heshan Tang | Jie Huang | Pei Liang
Source: Analytical Methods (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Enhancing substance identification by Raman spectroscopy using deep neural convolutional networks with an attention mechanism Raman spectroscopy is widely used for substance identification, providing molecular information from various components along with noise and instrument interference. Consequently, identifying components based on Raman spectra remains challenging. In this study, we collected Raman spectral data of 474 hazardous chemical substances using a portable Raman spectrometer, resulting in a dataset of 59 468 spectra. Our research employed a deep neural convolutional network based on the ResNet architecture, incorporating an attention mechanism called the SE module. By enhancing the weighting of certain spectral features, the performance of the model was significantly improved. We also investigated the classification predictive performance of the model under small-sample conditions, facilitating the addition of new hazardous chemical categories for future deployment on mobile devices. Additionally, we explored the features extracted by the convolutional neural network from Raman spectra, considering both Raman intensity and Raman shift aspects. We discovered that the neural network did not solely rely on intensity or shift for substance classification, but rather effectively combined both aspects. This research contributes to the advancement of Raman spectroscopy applications for hazardous chemical identification, particularly in scenarios with limited data availability. The findings shed light on the significance of spectral features in the model's decision-making process and have implications for broader applications of deep learning techniques in Raman spectroscopy-based substance identification. Raman spectroscopy ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Computer science ; Artificial neural network ; Identification (biology) ; Pattern recognition (psychology) ; Biological system ; Materials science ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Even Marius Nordhagen | Henrik Andersen Sveinsson | Anders Malthe‐Sørenssen
Source: arXiv (Cornell University) (preprint, 2024-01-05)
Field: Materials Science — Materials Chemistry

Tailoring Frictional Properties of Surfaces Using Diffusion Models This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic surfaces with frictional properties determined by molecular dynamics simulations, which trained the DDPM to predict surface structures from desired frictional outcomes. Unlike traditional trial-and-error and numerical optimization methods, our approach directly yields surface designs meeting specified frictional criteria with high accuracy and efficiency. This advancement in material surface engineering demonstrates the potential of machine learning in reducing the iterative nature of surface design processes. Our findings not only provide a new pathway for precise surface property tailoring but also suggest broader applications in material science where surface characteristics are critical. Surface (topology) ; Probabilistic logic ; Generative grammar ; Diffusion ; Computer science ; Materials science ; Artificial intelligence ; Mathematics ; Physics ; Geometry Machine Learning in Materials Science ; Adhesion, Friction, and Surface Interactions ; Model Reduction and Neural Networks
Authors: Antti Pihlajamäki | María Francisca Matus | Sami Malola | Hannu Häkkinen
Source: Advanced Materials (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

GraphBNC: Machine Learning‐Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins (Adv. Mater. 47/2024) Machine Learning for Nano-Bio Interfaces In article number 2407046, Antti Pihlajamäki, María Francisca Matus, Sami Malola, and Hannu Häkkinen report a method based on graph theory and neural networks (GraphBNC) to predict atom-scale interactions between ligand-stabilized gold nanoclusters and proteins. The method uses dynamic data from molecular dynamics simulations, making GraphBNC particularly useful when empirical evidence is scarce. Nanoclusters ; Materials science ; Metal ; Nanotechnology ; Composite material ; Biomedical engineering ; Metallurgy ; Engineering Nanocluster Synthesis and Applications ; Computational Drug Discovery Methods ; Graphene and Nanomaterials Applications
Authors: Xia Huang | Xinlu Cheng | Hong Zhang
Source: The Journal of Chemical Physics (article, 2024-11-08)
Field: Chemistry — Spectroscopy

Rate coefficients for C and O2 reactive collisions relevant to interstellar clouds from QCT and machine learning The chemical reactions between certain interstellar molecules are exothermic in nature and barrierless in the entrance channel, allowing these reactions to occur rapidly even at low astronomical temperatures, e.g., C and O2 interaction. Obtaining detailed rovibrational transition parameters for the reaction between C and O2, such as state-selected rate coefficients, is crucial for studying the associated atmospheric and astronomical environments. Hence, this work presents an approach that combines quasi-classical trajectory calculations with machine learning techniques based on Neural Network (NN) and Gaussian Process Regression (GPR) to determine state-selected rate coefficients. Employing this approach, we significantly reduced the computational requirements while simultaneously obtaining the accurate state-selected reaction cross sections and rate coefficients for the collision of C and O2. Both the NN-based and GPR-based models established in this work accurately predict the results calculated from explicit numerical calculations in the explored temperature range of 50-1500 K, achieving a coefficient of determination R2 > 0.96. Most importantly, the current work provides the most comprehensive dataset of rovibrational rate coefficients of v = 0-4, j = 0-70 → v' = 0-15 for the astrophysical modeling of the C-O2 collision system. Interstellar cloud ; Physics ; Interstellar medium ; Astrophysics Spectroscopy and Laser Applications ; Laser-induced spectroscopy and plasma ; Spectroscopy and Chemometric Analyses
Authors: Amjad Fakhri Kamarulzaman | Nursyam Dzuha Haris | Hazleen Anuar | Siti Fauziah Toha | Yakubu Adekunle Alli | Mohd Romainor Manshor
Source: Key engineering materials (article, 2024-02-23)
Field: Materials Science — Biomaterials

Predicting the Tensile Strength of 4D Printed PLA/EPO/Lignin Biocomposites Using Machine Learning The allure of 4D printing and machine learning (ML) for various applications is unquestionable, and researchers are striving hard to improve their performance. In this work, machine learning has been applied to predict the tensile strength of the 4D printed materials. The study investigated the reinforcement of polylactic acid (PLA) filament with lignin from oil palm empty fruit bunches (OPEFB) in the presence of epoxidized palm oil (EPO) as 4D printable filament. The alkaline extraction method was carried out used sodium hydroxide (NaOH), followed by precipitation with mineral acids utilizing one-factor-at-a-time (OFAT). Thereafter, the tensile strength of the 4D printed material was evaluated by tensile testing machine followed by machine learning prediction in which convolutional neural network (CNN) was adopted. The morphology of the 4D printed materials was determined by scanning electron microscope (SEM). The SEM micrograph of the tensile test of biocomposites revealed layer-by-layer formation of the filaments on the printed unfilled PLA biocomposite indicating lower inter-filament bonding. In the first trial, the actual result of the experiment was evaluated to be 24.44 MPa while the CNN prediction was 25.53 MPa. In the second attempt, the actual result of the experiment was 31.61 MPa whereas the prediction from CNN was 27.55 MPa. The coefficient of determination value obtained from CNN prediction is 0.12662. The current study indicates that machine learning is an important tool to optimize and/or predict the properties of 4D printing materials. Ultimate tensile strength ; Lignin ; Materials science ; Composite material ; Organic chemistry ; Chemistry biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Polymer composites and self-healing
Authors: Shuwen Wang | Hong Gu | Yan Peng
Source:  (article, 2024-06-16)
Field: Chemistry — Analytical Chemistry

Visualization and interpretation of origin identification of Panax notoginseng samples using neural networks The combination of deep learning with terahertz spectroscopy technology has been widely used in the analysis and identification of traditional Chinese medicinal materials. However, the decision-making process inside neural networks is often seen as a black box, making it difficult to understand and explain. This ’black box’ problem limits its application in some traditional Chinese medicinal material analysis and identification areas. Here, we propose an interpretable neural network algorithm for identifying the origin of Panax notoginseng samples and visualizing its process. We first use Convolutional Neural Networks (CNN) to classify the origin of the terahertz spectral data of Panax notoginseng samples, with an accuracy of up to $95 \%$. Furthermore, the model uses t-distributed stochastic neighborhood embedding (t-SNE) to visualize high-dimensional spectral data and analyzes the neural network to investigate how it processes input spectral data to achieve origin classification. Finally, applying explainable artificial intelligence (XAI) technology to explain the decision-making process of CNN, we found that the frequency, amplitude, relative proportion, peak area, and full width at half maximum (FWHM) of terahertz absorption peaks are all related to their respective characteristics of origin. These features play an important role in the origin recognition of neural networks, explaining the basis of neural networks in identifying the source of Panax notoginseng samples. Our algorithm not only improves the credibility and interpretability of neural networks for identifying the origin of Panax notoginseng, but also promotes the development of traditional Chinese medicine analysis and identification. Panax notoginseng ; Visualization ; Identification (biology) ; Interpretation (philosophy) ; Computer science ; Artificial neural network ; Data visualization ; Artificial intelligence ; Computational biology ; Natural language processing ; Biology ; Medicine ; Botany ; Pathology Spectroscopy and Chemometric Analyses
Authors: Jonathan Balasingham | Viktor Zamaraev | Vitaliy Kurlin
Source: Research Square (preprint, 2024-02-14)
Field: Materials Science — Materials Chemistry

Accelerating Material Property Prediction using Generically Complete Isometry Invariants <title>Abstract</title> Periodic material or crystal property prediction using machine learning has grown popular in recent years as it provides a computationally efficient replacement for classical simulation methods. A crucial first step for any of these algorithms is the representation used for a periodic crystal. While similar objects like molecules and proteins have a finite number of atoms and their representation can be built based upon a finite point cloud interpretation, periodic crystals are unbounded in size, making their representation more challenging. In the present work, we adapt the Pointwise Distance Distribution (PDD), a continuous and generically complete isometry invariant for periodic point sets, as a representation for our learning algorithm. The PDD distinguished all (more than 660 thousand) periodic crystals in the Cambridge Structural Database as purely periodic sets of points without atomic types. We develop a transformer model with a modified self-attention mechanism that combines PDD with compositional information via a spatial encoding method. This model is tested on the crystals of the Materials Project and Jarvis-DFT databases and shown to produce accuracy on par with state-of-the-art methods while being several times faster in both training and prediction time. Property (philosophy) ; Isometry (Riemannian geometry) ; Mathematics ; Pure mathematics ; Philosophy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Welding Techniques and Residual Stresses