jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xinyi Zhao | Yuai Duan | Zihan Liu | Hua Geng | Yaping Li | Zhongfeng Li | Tianyu Han
Source: Chinese Chemical Letters (article, 2024-11-06)
Field: Materials Science — Materials Chemistry

Mapping sweat pores for biometric identification based on a donor-acceptor hydrophilic fluorescent probe Fluorescence-based imaging applications have been benefiting greatly from donor-acceptor (D-A)/donor- π -acceptor (D- π -A) fluorescent probes owing to their intramolecular charge transfer (ICT) nature and self-assembly behavior. In this study, we design and synthesize a hydrophilic D-A fluorescent probe, namely CHBA, which would self-assemble into interlaced textures down to nanoscale but disassemble by trace amount of water in fingertip area. Upon finger-pressing, it enables fingerprint imaging and covers level-1/2/3 fingerprint information, wherein the sweat pores can be mapped in both bright field model and fluorescence mode, capable of naked-eye-based similarity analysis for personal identity verification (PIV). Spectroscopic analysis and morphology study show that the working mechanism can be attributed to the selective water-erosion effect on the solid-liquid interphase under physical contact. The sweat pore information can be digitized by polar coordinate conversion, further allowing machine-learning-based analysis for PIV application. The final PIV accuracy reaches 100% for all the involved machine-learning models, with no erroneous judgements. A prototype of PIV system is constructed by integrating CHBA with artificial intelligence hardware, wherein the sweat pore imaging, data processing and the decision-making could be run in parallel, suggesting high feasibility in real-world application. We introduce the design and synthesis of a hydrophilic donor-acceptor luminescent material capable of sweat pore imaging. It covers level-1/2/3 biometric information through self-assemble/disassemble mechanism and produces a clear distribution map for fingerprint sweat pores, enabling a prototype of machine-learning-based personal identity verification platform. Fluorescence ; Biometrics ; Identification (biology) ; SWEAT ; Chemistry ; Acceptor ; Computer science ; Artificial intelligence ; Medicine ; Biology ; Optics ; Physics ; Internal medicine Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications ; Molecular Sensors and Ion Detection
Authors: Wei Nan | Wenhui Ji | Yanping Yuan | Jidan Zhang | Yong Sun
Source: Journal of Analytical and Applied Pyrolysis (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Thermal behavior, kinetics, and gas evolution characteristics for the pyrolysis of unused and UV-aged GFRP Pyrolysis ; Kinetics ; Fibre-reinforced plastic ; Thermal ; Materials science ; Chemical engineering ; Composite material ; Thermodynamics ; Engineering ; Physics Thermal and Kinetic Analysis ; Polymer crystallization and properties ; Fiber-reinforced polymer composites
Authors: Shih-Kuang Lee | Sun-Ting Tsai | Sharon C. Glotzer
Source: The Journal of Chemical Physics (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Classification of complex local environments in systems of particle shapes through shape symmetry-encoded data augmentation Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for better order parameters in complex systems that are challenging to study using traditional approaches. However, the application of machine learning to self-assembly on systems of particle shapes is still underexplored. To address this gap, we propose a simple, physics-agnostic, yet powerful approach that involves training a multilayer perceptron (MLP) as a local environment classifier for systems of particle shapes, using input features such as particle distances and orientations. Our MLP classifier is trained in a supervised manner with a shape symmetry-encoded data augmentation technique without the need for any conventional roto-translations invariant symmetry functions. We evaluate the performance of our classifiers on four different scenarios involving self-assembly of cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with varying aspect ratios, and truncated shapes with different degrees of truncation. The proposed training process and data augmentation technique are both straightforward and flexible, enabling easy application of the classifier to other processes involving particle orientations. Our work thus presents a valuable tool for investigating self-assembly processes on systems of particle shapes, with potential applications in structure identification of any particle-based or molecular system where orientations can be defined. Classifier (UML) ; Computer science ; Artificial intelligence ; Particle system Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Machine Learning in Materials Science
Authors: Yulian T. Manchev | Paul L. A. Popelier
Source: The Journal of Physical Chemistry A (article, 2024-10-11)
Field: Materials Science — Materials Chemistry

Modeling Many-Body Interactions in Water with Gaussian Process Regression We report a first-principles water dimer potential that captures many-body interactions through Gaussian process regression (GPR). Modeling is upgraded from previous work by using a custom kernel function implemented through the KeOps library, allowing for much larger GPR models to be constructed and interfaced with the next-generation machine learning force field FFLUX. A new synthetic water dimer data set, called WD24, is used for model training. The resulting models can predict 90% of dimer geometries within chemical accuracy for a test set and in a simulation. The curvature of the potential energy surface is captured by the models, and a successful geometry optimization is completed with a total energy error of just 2.6 kJ mol<sup>-1</sup>, from a starting structure where water molecules are separated by nearly 4.3 Å. Dimeric modeling of a flexible, noncrystalline system with FFLUX is shown for the first time. Kriging ; Gaussian process ; Regression ; Regression analysis ; Process (computing) ; Computer science ; Statistics ; Environmental science ; Gaussian ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Gaussian Processes and Bayesian Inference
Authors: Kamron Fazel | Nima Karimitari | Tanooj Shah | Christopher Sutton | Ravishankar Sundararaman
Source: Journal of Computational Chemistry (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Improving the reliability of machine learned potentials for modeling inhomogeneous liquids The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids-including the density response, surface tension and size-dependent cavitation free energies-for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials. Molecular dynamics ; Surface tension ; Range (aeronautics) ; Work (physics) ; Statistical physics ; Water model ; Interatomic potential ; Computer science ; Materials science ; Mechanics ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry Machine Learning in Materials Science ; Nuclear Physics and Applications ; Electrostatics and Colloid Interactions
Authors: Micah P. Prange | Niranjan Govind | Panos Stinis | Eugene S. Ilton | Amanda A. Howard
Source: The Journal of Physical Chemistry A (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å<sup>-1</sup> from the first 6 Å<sup>-1</sup> (≈100 eV) of the absorption spectrum of Cu<sup>2+</sup> substitutional defects in the Fe<sup>3+</sup> mineral hematite (α-Fe<sub>2</sub>O<sub>3</sub>). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the <i>same</i> conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. To encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available. Extended X-ray absorption fine structure ; XANES ; Surface-extended X-ray absorption fine structure ; Spectral line ; Spectroscopy ; Absorption (acoustics) ; TRACE (psycholinguistics) ; Scattering ; Impurity ; Absorption spectroscopy ; Physics ; Computational physics ; Materials science ; Computer science ; Optics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced X-ray and CT Imaging
Authors: Miroslav Černý | Petr Šesták
Source: Crystals (article, 2024-01-12)
Field: Materials Science — Materials Chemistry

Segregation of Phosphorus and Silicon at the Grain Boundary in Bcc Iron via Machine-Learned Force Fields The study of the effects of impurity on grain boundaries is a critical aspect of materials science, particularly when it comes to understanding and controlling the properties of materials for specific applications. One of the related key issues is the segregation preference of impurity atoms in the grain boundary region. In this paper, we employed the on-the-fly machine learning to generate force fields, which were subsequently used to calculate the segregation energies of phosphorus and silicon in bcc iron containing the ∑5(310)[001] grain boundary. The generated force fields were successfully benchmarked using ab initio data. Our further calculations considered impurity atoms at a number of possible interstitial and substitutional segregation sites. Our predictions of the preferred sites agree with the experimental observations. Planar concentration of impurity atoms affects the segregation energy and, moreover, can change the preferred segregation sites. Grain boundary ; Impurity ; Silicon ; Materials science ; Phosphorus ; Chemical physics ; Condensed matter physics ; Boundary (topology) ; Ab initio ; Engineering physics ; Crystallography ; Metallurgy ; Chemistry ; Physics ; Microstructure Microstructure and mechanical properties ; Fusion materials and technologies ; Microstructure and Mechanical Properties of Steels
Authors: Ümit Işıkdağ | Gebrai̇l Bekdaş | Yaren Aydın | Sudi Apak | Junhee Hong | Zong Woo Geem
Source: Sustainability (article, 2024-12-08)
Field: Materials Science — Materials Chemistry

Adaptive Neural Architecture Search Using Meta-Heuristics: Discovering Fine-Tuned Predictive Models for Photocatalytic CO2 Reduction This study aims to contribute to the reduction of carbon dioxide and the production of hydrogen through an investigation of the photocatalytic reaction process. Machine learning algorithms can be used to predict the hydrogen yield in the photocatalytic carbon dioxide reduction process. Although regression-based approaches provide good results, the accuracy achieved with classification algorithms is not very high. In this context, this study presents a new method, Adaptive Neural Architecture Search (NAS) using metaheuristics, to improve the capacity of ANNs in estimating the hydrogen yield in the photocatalytic carbon dioxide reduction process through classification. The NAS process was carried out with a tool named HyperNetExplorer, which was developed with the aim of finding the ANN architecture providing the best prediction accuracy through changing ANN hyperparameters, such as the number of layers, number of neurons in each layer, and the activation functions of each layer. The nature of the NAS process in this study was adaptive, since the process was accomplished through optimization algorithms. The ANNs discovered with HyperNetExplorer demonstrated significantly higher prediction performance than the classical ML algorithms. The results indicated that the NAS helped to achieve better performance in the estimation of the hydrogen yield in the photocatalytic carbon dioxide reduction process. Heuristics ; Reduction (mathematics) ; Architecture ; Photocatalysis ; Computer science ; Artificial neural network ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics ; Chemistry ; Catalysis ; Geography Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies
Authors: Aatish Kandaswamy | Sebastian P. Schwaminger
Source: Crystals (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions Crystallization plays a crucial role in defining the quality and functionality of products across various industries, including pharmaceutical, food and beverage, and chemical manufacturing. The process’s efficiency and outcome are significantly influenced by solute–solvent interactions, which determine the crystalline product’s purity, size, and morphology. These attributes, in turn, impact the product’s efficacy, safety, and consumer acceptance. Traditional methods of optimizing crystallization conditions are often empirical, time-consuming, and less adaptable to complex chemical systems. This research addresses these challenges by leveraging machine learning techniques to predict and optimize solute–solvent interactions, thereby enhancing crystallization outcomes. This review provides a novel approach to understanding and controlling crystallization processes by integrating supervised, unsupervised, and reinforcement learning models. Machine learning not only improves product the quality and manufacturing efficiency but also contributes to more sustainable industrial practices by minimizing waste and energy consumption. Crystallization ; Solvent ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Organic chemistry Crystallization and Solubility Studies
Authors: Aulia Al-Jihad Safhadi | Teuku Rizky Noviandy | Irvanizam Irvanizam | Rivansyah Suhendra | Taufiq Karma | Rinaldi Idroes
Source: Infolitika Journal of Data Science (article, 2024-05-23)
Field: Chemistry — Spectroscopy

Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds The identification of chemical compounds in essential oils is crucial in industries such as pharmaceuticals, perfumery, and food. Kovats Retention Index (RI) values are essential for compound identification using gas chromatography-mass spectrometry (GC-MS). Traditional RI determination methods are time-consuming, labor-intensive, and susceptible to experimental variability. Recent advancements in data science suggest that artificial intelligence (AI) can enhance RI prediction accuracy and efficiency. However, the full potential of AI, particularly artificial neural networks (ANN), in predicting RI values remains underexplored. This study develops a backpropagation neural network (BPNN) model to predict the Kovats RI values of essential oil compounds using five molecular descriptors: ATSc1, VCH-7, SP-1, Kier1, and MLogP. We trained the BPNN on a dataset of 340 essential oil compounds and optimized it through hyperparameter tuning. We show that the optimized BPNN model, with an epoch count of 100, a learning rate of 0.1, a hidden layer size of 10 neurons, and the ReLU activation function, achieves an R² value of 0.934 and a Root Mean Squared Error (RMSE) of 76.98. These results indicate a high correlation between predicted and actual RI values and a low average prediction error. Our findings demonstrate that BPNNs can significantly improve the efficiency and accuracy of compound identification, reducing reliance on traditional experimental methods. Backpropagation ; Kovats retention index ; Artificial neural network ; Index (typography) ; Chromatography ; Artificial intelligence ; Chemistry ; Biological system ; Computer science ; Gas chromatography ; Biology ; World Wide Web Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Yuqing Zhang | Yiyi Zhang | Jinhai Deng | Zihan Shen | Zhicheng Wang | Yilu Wu | Yuqi Hu | Chengzhi Huang | Jiagui Wu | Junbo Yang
Source: Optics Communications (article, 2024-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-volatile double-tunable vortex metalens design based on Sb2S3 using deep neural network and particle swarm optimization algorithm Particle swarm optimization ; Topological quantum number ; Algorithm ; Artificial neural network ; Vortex ; Phase (matter) ; Wavelength ; Optics ; Computer science ; Computer simulation ; Range (aeronautics) ; Flexibility (engineering) ; Materials science ; Topology (electrical circuits) ; Physics ; Artificial intelligence ; Mathematics ; Simulation Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Optical Wireless Communication Technologies
Authors: Sabreen Dar Amer | Maha Assad | Rami A. Hawileh | Ghada Karak | Hussam Safieh | Jamal Abdalla
Source: Engineered Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Ultimate Strain in Anchored CFRP Laminates using Machine Learning Anchoring carbon fibre-reinforced polymer (CFRP) laminates to concrete using CFRP spike anchors effectively mitigates the unfavorable debonding failure mode in strengthened concrete beams.However, the strain and strength enhancement resulting from anchoring CFRP laminates have not been thoroughly quantified in the literature and existing practice codes.This study investigates the prediction of ultimate strain in anchored CFRP laminates, which is critical for assessing the flexural strength of strengthened concrete beams.Statistical regression analysis and machine learning models are employed to develop a predictive equation for the ultimate strain in CFRP laminates due to anchorage, using data from prior flexural tests on concrete prisms.The study examines various parameters, including CFRP sheet width, anchor design details (such as diameter and embedment depth), number of CFRP layers and anchor-to-sheet material ratio.Linear regression models, Support Vector Regression and Decision Trees were tested and compared for their accuracy in predicting the ultimate strain in CFRP laminates.The linear regression model, with highest performance indicators, was selected.Additionally, the study provides derived predictive equations, offering a practical implication for design optimization.Finally, sets of design charts were proposed to achieve specific values of ultimate strain in CFRP-strengthened and anchored concrete beams. Fabrication ; Materials science ; Nanocomposite ; Polymer ; Nanotechnology ; Nanoparticle ; Polymer nanocomposite ; Composite material Mesoporous Materials and Catalysis ; Silicone and Siloxane Chemistry ; Diatoms and Algae Research
Authors: Zeyu Wang | Run Hu | Xiaobing Luo | Jinlong Ma
Source: Journal of Applied Physics (article, 2024-10-15)
Field: Materials Science — Materials Chemistry

Compositionally restricted atomistic line graph neural network for improved thermoelectric transport property predictions Graph neural networks (GNNs) have evolved many variants for predicting the properties of crystal materials. While most networks within this family focus on improving model structures, the significance of atomistic features has not received adequate attention. In this study, we constructed an atomistic line GNN model using compositionally restricted atomistic representations which are more elaborate set of descriptors compared to previous GNN models, and employing unit graph representations that account for all symmetries. The developed model, named as CraLiGNN, outperforms previous representative GNN models in predicting the Seebeck coefficient, electrical conductivity, and electronic thermal conductivity that are recorded in a widely used thermoelectric properties database, confirming the importance of atomistic representations. The CraLiGNN model allows optional inclusion of additional features. The supplement of bandgap significantly enhances the model performance, for example, more than 35% reduction of mean absolute error in the case of 600 K and 1019 cm−3 concentration. We applied CraLiGNN to predict the unrecorded thermoelectric transport properties of 14 half-Heusler and 52 perovskite compounds, and compared the results with first-principles calculations, showing that the model has extrapolation ability to identify the thermoelectric potential of materials. Thermoelectric effect ; Extrapolation ; Seebeck coefficient ; Thermal conductivity ; Materials science ; Artificial neural network ; Electrical resistivity and conductivity ; Graph ; Thermoelectric materials ; Computer science ; Statistical physics ; Artificial intelligence ; Thermodynamics ; Theoretical computer science ; Mathematics ; Physics ; Mathematical analysis ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials