jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sarwan Ali | Tamkanat E Ali | Prakash Chourasia | Murray Patterson
Source: arXiv (Cornell University) (preprint, 2024-02-12)
Field: Chemistry — Spectroscopy

A Universal Non-Parametric Approach For Improved Molecular Sequence Analysis In the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. The classification of molecular sequences has seen widespread use of neural network-based techniques. Despite their astounding accuracy, these models often require a substantial number of parameters and more data collection. In this work, we present a novel approach based on the compression-based Model, motivated from \cite{jiang2023low}, which combines the simplicity of basic compression algorithms like Gzip and Bz2, with Normalized Compression Distance (NCD) algorithm to achieve better performance on classification tasks without relying on handcrafted features or pre-trained models. Firstly, we compress the molecular sequence using well-known compression algorithms, such as Gzip and Bz2. By leveraging the latent structure encoded in compressed files, we compute the Normalized Compression Distance between each pair of molecular sequences, which is derived from the Kolmogorov complexity. This gives us a distance matrix, which is the input for generating a kernel matrix using a Gaussian kernel. Next, we employ kernel Principal Component Analysis (PCA) to get the vector representations for the corresponding molecular sequence, capturing important structural and functional information. The resulting vector representations provide an efficient yet effective solution for molecular sequence analysis and can be used in ML-based downstream tasks. The proposed approach eliminates the need for computationally intensive Deep Neural Networks (DNNs), with their large parameter counts and data requirements. Instead, it leverages a lightweight and universally accessible compression-based model. Sequence (biology) ; Parametric statistics ; Computer science ; Computational biology ; Algorithm ; Mathematics ; Biology ; Statistics ; Genetics Analytical Chemistry and Chromatography ; Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies
Authors: Artem Glova | Mikko Karttunen
Source: arXiv (Cornell University) (preprint, 2024-06-28)
Field: Materials Science — Materials Chemistry

Learning glass transition temperatures via dimensionality reduction with data from computer simulations: Polymers as the pilot case Machine learning (ML) methods provide advanced means for understanding inherent patterns within large and complex datasets. Here, we employ the principal component analysis (PCA) and the diffusion map (DM) techniques to evaluate the glass transition temperature ($T_\mathrm{g}$) from low-dimensional representations of all-atom molecular dynamic (MD) simulations of polylactide (PLA) and poly(3-hydroxybutyrate) (PHB). Four molecular descriptors were considered: radial distribution functions (RDFs), mean square displacements (MSDs), relative square displacements (RSDs), and dihedral angles (DAs). By applying a Gaussian Mixture Model (GMM) to analyze the PCA and DM projections, and by quantifying their log-likelihoods as a density-based metric, a distinct separation into two populations corresponding to melt and glass states was revealed. This separation enabled the $T_\mathrm{g}$ evaluation from a cooling-induced sharp increase in the overlap between log-likelihood distributions at different temperatures. $T_\mathrm{g}$ values derived from the RDF and MSD descriptors using DM closely matched the standard computer simulation-based dilatometric and dynamic $T_\mathrm{g}$ values for both PLA and PHB models. This was not the case for PCA. The DM-transformed DA and RSD data resulted in $T_\mathrm{g}$ values in agreement with experimental ones. Overall, the fusion of atomistic simulations and diffusion maps complemented with the Gaussian Mixture Models presents a promising framework for computing $T_\mathrm{g}$ and studying the glass transition in a unified way across various molecular descriptors for glass-forming materials. Dimensionality reduction ; Glass transition ; Reduction (mathematics) ; Curse of dimensionality ; Polymer ; Transition (genetics) ; Computer science ; Materials science ; Statistical physics ; Process engineering ; Artificial intelligence ; Engineering ; Chemistry ; Physics ; Mathematics ; Composite material Material Dynamics and Properties ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Quinn Gallagher | Michael A. Webb
Source: ChemRxiv (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Data Efficiency of Classification Strategies for Chemical and Materials Design Active learning and design-build-test-learn strategies are increasingly employed to accelerate materials discovery and characterization. Many data-driven materials design campaigns target solutions within constrained domains such as synthesizability, stability, solubility, recyclability, and toxicity. Lack of knowledge about these constraints can hinder design efficiency by producing samples that fail to meet required thresholds. Acquiring this knowledge during the design campaign is inefficient, and effective classification of common materials constraints transcends specific design objectives. However, there is no consensus on the most data-efficient algorithm for classifying whether a material satisfies a constraint. To address this gap, we comprehensively compare the performance of 100 strategies designed to classify chemical and materials behavior. Performance is assessed across 31 classification tasks sourced from the literature in chemical and materials science. From these results, we recommend best practices for building data-efficient classifiers, showing the neural network- and random forest-based active learning algorithms are most efficient across tasks. We also show that classification task complexity can be quantified based on task metafeatures, most notably the noise-to-signal ratio. Overall, this work provides a comprehensive survey of data-efficient classification strategies, identifies attributes of top-performing strategies, and suggests avenues for further study. Computer science ; Task (project management) ; Machine learning ; Constraint (computer-aided design) ; Stability (learning theory) ; Artificial intelligence ; Random forest ; Artificial neural network ; Data mining ; Engineering ; Systems engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Broad Learning System for Investigating Corrosion Inhibition Efficiency of Heterocyclic Compounds Corrosion ; Combinatorial chemistry ; Chemistry ; Biochemical engineering ; Business ; Engineering ; Organic chemistry Synthesis of Tetrazole Derivatives
Authors: Zeyuan Liu | Sanming Du | Tiantian He | Yulong Ren | Jie Tian | Wen-Bo Chen | Chenfei Song | Yongzhen Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Investigation on Tribological Behavior and Wear Performance Prediction of Gcr15 Bearing Steel Under Different Sliding-Rolling States Tribology ; Bearing (navigation) ; Materials science ; Metallurgy ; Composite material ; Structural engineering ; Engineering ; Computer science ; Artificial intelligence Metal Alloys Wear and Properties ; Gear and Bearing Dynamics Analysis ; Mechanical stress and fatigue analysis
Authors: Jingzhi Yang | Junsen Zhao | Xin Guo | Yami Ran | Zhongheng Fu | Hongchang Qian | Lingwei Ma | Patrick Keil | J.M.C. Mol | Dawei Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Combinatorial Discovery and Computational Analysis of the Synergistic Effect of Green Amino Acid Corrosion Inhibitors Corrosion ; Drug discovery ; Chemistry ; Combinatorial chemistry ; Computational biology ; Computer science ; Biochemistry ; Biology ; Organic chemistry Corrosion Behavior and Inhibition ; Chemical Synthesis and Analysis
Authors: Scout Jarman | Z. Hampel-Arias | Adra Carr | Kevin R. Moon
Source: arXiv (Cornell University) (preprint, 2024-01-23)
Field: Chemistry — Spectroscopy

Local Background Estimation for Improved Gas Plume Identification in Hyperspectral Images Deep learning identification models have shown promise for identifying gas plumes in Longwave IR hyperspectral images of urban scenes, particularly when a large library of gases are being considered. Because many gases have similar spectral signatures, it is important to properly estimate the signal from a detected plume. Typically, a scene's global mean spectrum and covariance matrix are estimated to whiten the plume's signal, which removes the background's signature from the gas signature. However, urban scenes can have many different background materials that are spatially and spectrally heterogeneous. This can lead to poor identification performance when the global background estimate is not representative of a given local background material. We use image segmentation, along with an iterative background estimation algorithm, to create local estimates for the various background materials that reside underneath a gas plume. Our method outperforms global background estimation on a set of simulated and real gas plumes. This method shows promise in increasing deep learning identification confidence, while being simple and easy to tune when considering diverse plumes. Hyperspectral imaging ; Plume ; Spectral signature ; Identification (biology) ; Environmental science ; Computer science ; Panache ; Pattern recognition (psychology) ; Remote sensing ; Artificial intelligence ; Algorithm ; Meteorology ; Geology ; Geography Spectroscopy and Laser Applications ; Air Quality Monitoring and Forecasting ; Advanced Chemical Sensor Technologies
Authors: Kaihan Gao | Yiwei Ju | Shuai Li | Xuebing Yang | Wensheng Zhang | Guoqing Li
Source: Journal of Intelligent & Fuzzy Systems (article, 2024-01-05)
Field: Materials Science — Surfaces, Coatings and Films

PmcaNet: Pyramid multiscale channel attention network for electron microscopy image segmentation Recent advances in high-throughput electron microscopy (EM) have revolutionized the examination of microstructures by enabling fast EM image generation. However, accurately segmenting EM images remains challenging due to inherent characteristics, including low contrast and subtle grayscale variations. Moreover, as manually annotated EM images are limited, it is usually impractical to utilize deep learning techniques for EM image segmentation. To address these challenges, the pyramid multiscale channel attention network (PmcaNet) is specifically designed. PmcaNet employs a convolutional neural network-based architecture and a multiscale feature pyramid to effectively capture global context information, enhancing its ability to comprehend the intricate structures within EM images. To enable the rapid extraction of channel-wise dependencies, a novel attention module is introduced to enhance the representation of intricate nonlinear features within the images. The performance of PmcaNet is evaluated on two general EM image segmentation datasets as well as a homemade dataset of superalloy materials, regarding pixel-wise accuracy and mean intersection over union (mIoU) as evaluation metrics. Extensive experiments demonstrate that PmcaNet outperforms other models on the ISBI 2012 dataset, achieving 87.85% pixel-wise accuracy and 73.11% mean intersection over union (mIoU), while also advancing results on the Kathuri and SEM-material datasets. Computer science ; Pyramid (geometry) ; Artificial intelligence ; Segmentation ; Pattern recognition (psychology) ; Context (archaeology) ; Pixel ; Intersection (aeronautics) ; Convolutional neural network ; Feature (linguistics) ; Grayscale ; Channel (broadcasting) ; Image segmentation ; Feature extraction ; Computer vision ; Mathematics ; Cartography Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Non-Destructive Testing Techniques
Authors: Deqing Xue | Qian Zuo | Bueryi Shen | Ruihao Yuan | Zhao Shang | G.J. Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Active Learning-Assisted Design of Tini-Based Shape Memory Alloys with Improved Thermal Storage Performance Shape-memory alloy ; Materials science ; Thermal ; Computer science ; Metallurgy ; Thermodynamics ; Physics Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Shape Memory Alloy Transformations
Authors: Shaojie Zhou | Yali Mao | Guoliang Ma | Peng Xu | Chao Yuan
Source:  (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

High-Throughput Thermophysical and Structural Characterizations of Semiconductors Using Transient Thermoreflectance Pump-probe thermoreflectance (Pump-probe TR) is a non-contact technique for thermal properties characterization of materials, which is also used to analyze material quality and detect defects. For wafers, uniform thermophysical properties and thickness are critical to avoid localized thermal hotspots and device failure. Traditional spot detection methods for fitting unknown parameters are time-consuming and labor-intensive when handling large datasets. This work proposes a high-throughput method for semiconductor thermophysical and structural characterizations, integrating automatic scanning measurements with deep learning techniques. A multilayer perceptron (MLP) model processes the data, demonstrating reliability through global scanning of Au-sapphire samples, revealing variations in thermal boundary conductance (TBC). The method also detects material defects and is validated in industrial applications with GaN-on-Si samples, offering high accuracy and speed. In addition, we demonstrated the feasibility of the method for structural characterization of materials by scanning measurement of the thickness of Si thin film. This high-throughput method can reduce data processing time to the millisecond level, significantly reducing time and labor compared to traditional methods, making it particularly efficient for high-volume wafer thermophysical and structural characterization. Throughput ; Transient (computer programming) ; Materials science ; Semiconductor ; Optoelectronics ; Computer science ; Engineering physics ; Engineering ; Telecommunications Thermal properties of materials ; Silicon and Solar Cell Technologies ; Solidification and crystal growth phenomena
Authors: G.J. Zhang | Deqing Xue | Qian Zuo | Bueryi Shen | Ruihao Yuan
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Active Learning-Assisted Design of Tini-Based Shape Memory Alloys with Improved Thermal Storage Performance Shape-memory alloy ; Materials science ; Thermal ; Computer science ; Metallurgy ; Thermodynamics ; Physics Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Shape Memory Alloy Transformations
Authors: Mudassir Hussain Tahir | Tagir Kadyrov | Ihab Mohamed Moussa
Source: Materials Science and Engineering B (article, 2024-11-04)
Field: Materials Science — Materials Chemistry

Data-driven designing of dyes: Chemical space generation and dipole moment prediction Moment (physics) ; Dipole ; Chemical space ; Space (punctuation) ; Biological system ; Biochemical engineering ; Computer science ; Chemistry ; Physics ; Engineering ; Biology ; Organic chemistry ; Classical mechanics Photochromic and Fluorescence Chemistry ; Photopolymerization techniques and applications ; Chemistry and Chemical Engineering
Authors: S. D. Biradar | A. M. Sonkamble | Vaishnavi Thombare | Sayali Thopate
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2024-11-06)
Field: Chemistry — Analytical Chemistry

“AgriVision” A Flutter -Based Application for RealTime Detection of Pesticides Residues on Fruits “AgriVision” is a Flutter-based mobile application designed to address the critical issue of pesticide contamination on fruits. The application utilizes real-time image processing and machine learning techniques to detect pesticide residues on the surface of various fruits, ensuring food safety and healthstandards. By capturing and analyzing images through a smartphone camera, the app provides instant and reliablefeedback on the presence of harmful chemicals. The user-friendly interface, combined with the portability of the solution, offers farmers, consumers, and vendors a practical tool to assess fruit quality and promote safer agricultural practices. This project aims to contribute to healthier food choices and enhance consumer safety. Key Words: AgriVision , Flutter application , Real-time detection , Pesticide residues , Fruits , Agricultural technology , Pesticide detection , Mobile app , Food safety , AgriTech , Image processing , Machine learning , Smart agriculture , Fruit quality analysis. Flutter ; Pesticide ; Computer science ; Environmental science ; Toxicology ; Engineering ; Biology ; Agronomy ; Aerospace engineering Spectroscopy and Chemometric Analyses
Authors: Bahriyenur Arabacı | Rezan Bakır | Ceren Orak | Aslı Yüksel
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Integrating Experimental and Machine Learning Approaches for Predictive Analysis of Photocatalytic Hydrogen Evolution Using Cu/G-C3n4 Photocatalysis ; Hydrogen ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Chemical engineering ; Chemistry ; Engineering ; Catalysis Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Dirk Trauner | Christoph Etling
Source: Synfacts (article, 2024-01-16)
Field: Chemistry — Organic Chemistry

Predicting Late-Stage C–H Borylations of Drug Molecules Key words late-stage diversification - borylation - reaction prediction - deep learning - high-throughput experimentation Chemistry ; Stage (stratigraphy) ; Molecule ; Drug ; Stereochemistry ; Computational chemistry ; Medicinal chemistry ; Organic chemistry ; Pharmacology ; Geology Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis ; Organoboron and organosilicon chemistry
Authors: Yudha Mulyana | Muhamad Akrom | Gustina Alfa Trisnapradika
Source: IJNMT (International Journal of New Media Technology) (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Comparison of Linear and Non-Linear Machine Learning Algortima for Predicting the Effectiveness of Plant Extracts as Corrosion Inhibitors This research aims to develop a Machine Learning (ML) model that can predict the corrosion inhibitor potential of plant extracts with high accuracy. Corrosion is a serious problem in industry because it can reduce the service life of materials and cause economic losses. This research focuses on the use of green inhibitors, especially plant extracts, which are considered environmentally friendly and have high anticorrosion efficiency. The dataset used includes molecular and physicochemical features of plant extracts. The ML model development process involves data normalization, selection of linear and non-linear ML algorithms, model training with k-fold crossvalidation, and model performance evaluation using regression metrics such as MSE, RMSE, MAE, and R2. Experiments compare various ML algorithms and show that the AdaBoost Regressor (ABR) model exhibits the best prediction performance with the highest R2 value of 0.993 and a low MSE of 0.002. These results provide new insights into the potential of ML models to predict effective corrosion inhibitors from plant extracts. The ABR model had a low prediction error, indicating high accuracy in predicting corrosion inhibition efficiency. In addition, the analysis of important features shows that two features, Conc and LUMO, have a significant influence on the ABR model. This research makes an important contribution to the development of effective prediction methods in the corrosion control industry. The ABR model can serve as a basis for designing more effective and environmentally friendly corrosion inhibitor materials, as well as a reference for other researchers in developing ML models that accurately predict the corrosion inhibition efficiency of plant extracts. Corrosion ; Artificial intelligence ; Machine learning ; Computer science ; Materials science ; Metallurgy Material Properties and Failure Mechanisms
Authors: Christopher Ikechukwu Ekeocha | Ikechukwu Nelson Uzochukwu | Ini‐Ibehe Nabuk Etim | Ikenna B. Onyeachu | Emeka E. Oguzie
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Machine Learning Models and Computational Simulation Technique for Optimum Prediction of Anti-Corrosion Properties of Newly Designed Benzimidazole Compounds Benzimidazole ; Corrosion ; Computer science ; Artificial intelligence ; Machine learning ; Computational science ; Materials science ; Chemistry ; Metallurgy Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Emanuele Costa | Giuseppe Scriva | Sebastiano Pilati
Source: Machine Learning Science and Technology (article, 2024-07-09)
Field: Materials Science — Materials Chemistry

Solving deep-learning density functional theory via variational autoencoders Abstract In recent years, machine learning models, chiefly deep neural networks, have revealed suited to learn accurate energy-density functionals from data. However, problematic instabilities have been shown to occur in the search of ground-state density profiles via energy minimization. Indeed, any small noise can lead astray from realistic profiles, causing the failure of the learned functional and, hence, strong violations of the variational property. In this article, we employ variational autoencoders (VAEs) to build a compressed, flexible, and regular representation of the ground-state density profiles of various quantum models. Performing energy minimization in this compressed space allows us to avoid both numerical instabilities and variational biases due to excessive constraints. Our tests are performed on one-dimensional single-particle models from the literature in the field and, notably, on a three-dimensional disordered potential. In all cases, the ground-state energies are estimated with errors below the chemical accuracy and the density profiles are accurately reproduced without numerical artifacts. Furthermore, we show that it is possible to perform transfer learning, applying pre-trained VAEs to different potentials. Computer science ; Minification ; Artificial intelligence ; Representation (politics) ; Energy minimization ; Property (philosophy) ; Statistical physics ; Ground state ; Energy (signal processing) ; Artificial neural network ; Density functional theory ; Algorithm ; Energy functional ; Deep learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Protein Structure and Dynamics