jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abigail Hsu
Source:  (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

Quantifying Exam Stress Progressions Using Electrodermal Activity and Machine Learning Stress can have detrimental effects on students, causing decreased exam scores and compromised mental health levels. However, few studies have examined reactions to practical cognitive stressors using physiological signals. This study quantified stress progressions during exams using Electrodermal Activity (EDA) signals from the PhysioNet database “A Wearable Exam Stress Dataset for Predicting Cognitive Performance in Real-World Settings” and applied machine learning methods. The EDA signals were first segmented into three exam periods: start, middle, end. Then, four machine learning models - Linear Discriminant Analysis, Classification and Regression Tree, Support Vector Machine (SVM), and K-Nearest Neighbor - were utilized to classify these segments. The SVM model demonstrated the best performance, with an accuracy score of 75%, precision score of 83%, recall score of 75%, and a F1 score of 73%. This study ultimately revealed that exam stress peaked during the start and end of the exam, while subsiding during the middle. These results can be applied to construct wearable devices for stress management, which could bring more quantifiable data into the mental health field. Computer science ; Stress (linguistics) ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design
Authors: Yongxian Wu | Haixin Wei | Qiang Zhu | Ray Luo
Source: The Journal of Physical Chemistry Letters (article, 2023-10-02)
Field: Materials Science — Materials Chemistry

Grid-Robust Efficient Neural Interface Model for Universal Molecule Surface Construction from Point Clouds Molecular surfaces play a pivotal role in elucidating the properties and functions of biological complexes. While various surfaces have been proposed for specific scenarios, their widespread adoption faces challenges due to limited efficiency stemming from hand-crafted modeling designs. In this work, we proposed a general framework that incorporates both the point cloud concept and neural networks. The use of matrix multiplication in this framework enables efficient implementation across diverse platforms and libraries. We applied this framework to develop the GENIUSES (<b>G</b>rid-robust <b>E</b>fficient <b>N</b>eural <b>I</b>nterface for <b>U</b>niversal <b>S</b>olvent-<b>E</b>xcluded <b>S</b>urface) model for constructing SES. GENIUSES demonstrates high accuracy and efficiency across data sets with varying conformations and complexities. Compared to the classical implementation of SES in the AMBER software package, our framework achieved a 26-fold speedup while retaining ∼95% accuracy when ported to the GPU platform using CUDA. Greater speedups can be obtained in large-scale systems. Importantly, our model exhibits robustness against variations in the grid spacing. We have integrated this infrastructure into AMBER to enhance accessibility for research in drug screening and related fields, where efficiency is of paramount importance. Computer science ; Grid ; Robustness (evolution) ; Point cloud ; Speedup ; Computational science ; Cloud computing ; CUDA ; Artificial neural network ; Interface (matter) ; Distributed computing ; Porting ; Computer engineering ; Software ; Theoretical computer science ; Artificial intelligence ; Parallel computing Machine Learning in Materials Science ; Protein Structure and Dynamics ; Parallel Computing and Optimization Techniques
Authors: Yijue Wang | Nidhibahen Shah | Ahmed Soliman | Dan Guo | Sanguthevar Rajasekaran
Source: The Journal of Physical Chemistry A (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

KE: A Knowledge Enhancing Framework for Machine Learning Models Machine learning models are widely used in science and engineering to predict the properties of materials and solve complex problems. However, training large models can take days and fine-tuning hyperparameters can take months, making it challenging to achieve optimal performance. To address this issue, we propose a <i>Knowledge Enhancing (KE)</i> algorithm that enhances knowledge gained from a lower capacity model to a higher capacity model, enhancing training efficiency and performance. We focus on the problem of predicting the bandgap of an unknown material and present a theoretical analysis and experimental verification of our algorithm. Our experiments show that the performance of our knowledge enhancement model is improved by at least 10.21% compared to current methods on OMDB datasets. We believe that our generic idea of knowledge enhancement will be useful for solving other problems and provide a promising direction for future research. Hyperparameter ; Computer science ; Machine learning ; Artificial intelligence ; Focus (optics) Machine Learning in Materials Science ; Machine Learning and Data Classification ; Neural Networks and Applications
Authors: Md Hasan-Ur Rahman | Vidya Bommanapally | Dilanga Abeyrathna | Md Ashaduzzman | Manoj Tripathi | Mahzuzah Zahan | Mahadevan Subramaniam | Venkataramana Gadhamshetty
Source:  (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Optical Detection of Multilayer Hexagonal Boron Nitride for Enhanced Characterization and Analysis Biofilms are ubiquitous in aqueous environments, exerting significant influence on diverse surfaces, including metals prone to microbiologically influenced corrosion (MIC). This multifaceted phenomenon demands interdisciplinary collaborations to combat its far-reaching implications. In this context, our research delves into the intricate characterization of twodimensional (2D) materials, particularly hexagonal boron nitride (hBN), which is crucial for advancing corrosion prevention coatings. The nanoscale dimensions of 2D materials pose challenges in microstructural analysis and defect identification, necessitating labor-intensive traditional techniques. To address these complexities, we utilized two unsupervised machine learning models, namely, (a) K-means clustering, and (b) Gaussian Mixture Model (GMM), which enabled clear differentiation between multilayer hBN (MLhBN) and cracks. Our approach will streamline the characterization process and facilitate the extraction of thin layers with enhanced accuracy. Characterization (materials science) ; Hexagonal boron nitride ; Materials science ; Context (archaeology) ; Corrosion ; Cluster analysis ; Nanotechnology ; Identification (biology) ; Nanoscopic scale ; Computer science ; Artificial intelligence ; Metallurgy ; Archaeology Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Ga2O3 and related materials
Authors: Arailym Serikbay | Mehdi Bagheri | Amin Zollanvari
Source: IEEE Access (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Stacked Ensemble Deep Learning for Outdoor Insulator Surface Condition Classification: A Profound Study on Water Droplets Insulators are vital protection and isolation barriers used in power transmission systems. To prevent unexpected failures caused by severe weather conditions, it is important to develop intelligent insulator fault prognosis systems. To this end, this study has focused to examine and analyze normal (clean) and contaminated (wet) insulators through a new technique that can act as a catalyst for possible future solutions to the data-driven classification of insulator surface conditions. In particular, a novel stacked ensemble learning based on six pretrained deep convolutional neural networks (CNNs), which are used as level-0 generalizers in the stacked structure, is proposed. As the level-1 generalizer that is used in the stacked structure, a majority vote is considered among selected subsets of level-0 generalizers. In other words, training the level-1 generalizer is equivalent to selecting the best subset of pretrained CNNs for classifying a clean insulator surface from those sprayed with water droplets. Since a wetted insulator surface can enhance the electric field intensity and may lead to flashover when combined with other contaminants (dust, soil, cement, etc.), water droplets are considered a type of contamination in our study. Considering a tradeoff between performance and model complexity in training the level-1 generalizer points to two combinations of pretrained CNNs, namely, EfficientNetB2-ResNet50-Xception, and MobileNet-DenseNet121-Xception. Our empirical results show that (i) both combinations lead to better performance when compared with individual pretrained models, and (ii) the latter combination leads to a considerably lower complexity (~39% less parameters) at the expense of ~9% reduction in accuracy. Insulator (electricity) ; Arc flash ; Computer science ; Convolutional neural network ; Artificial intelligence ; Deep learning ; Environmental science ; Pattern recognition (psychology) ; Materials science ; Optoelectronics High voltage insulation and dielectric phenomena ; Advanced Neural Network Applications ; Infrastructure Maintenance and Monitoring
Authors: Yongjun Tan
Source: Corrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control (article, 2023-09-29)
Field: Materials Science — Materials Chemistry

An overview of progresses and challenges of electrochemically integrated multi-electrode arrays for probing localised corrosion in complex environmental conditions Since the electrochemically integrated multi-electrode array, often referred to as the wire beam electrode, was initially developed for corrosion studies in 1990s, remarkable progresses have been made in its applications for probing and monitoring various forms of localised corrosion that are difficult-to-measure by conventional methods. This paper provides a brief overview of diverse and innovative applications of variously designed and constructed electrode array probes for measuring localised corrosion under complex environmental conditions with particular focus on the discussion of challenges and obstacles that prevent wider applications of electrode arrays. Discussion on future perspectives have included suggestions on the innovative design and fabrication of multi-scale, three-dimensional, complex shaped and patterned electrode arrays for simulating microstructural features of heterogeneous alloys and thoughts on the future integration of corrosion monitoring data and data analytics, machine learning and artificial intelligence for future prediction, modelling and prevention of corrosion in industrial applications. Corrosion ; Materials science ; Electrode ; Nanotechnology ; Metallurgy ; Chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Ming Jiang | Kavin Ammigan | George Lolov | Frédérique Pellemoine | Dong Liu
Source: Journal of Nuclear Materials (article, 2023-09-09)
Field: Materials Science — Materials Chemistry

Porosity evolution in proton irradiated microfine-grained POCO graphite Porosity ; Irradiation ; Materials science ; Proton ; Beamline ; Graphite ; Scanning electron microscope ; Nuclear graphite ; Fluence ; Composite material ; Beam (structure) ; Analytical Chemistry (journal) ; Nuclear physics ; Optics ; Chemistry ; Physics Graphite, nuclear technology, radiation studies ; Radiation Shielding Materials Analysis ; Graphene research and applications
Authors: Haoxuan Liu | Haile Yan | Ying Zhao | Nan Jia | Shuai Tang | Daoyong Cong | Bo Yang | Zongbin Li | Yudong Zhang | Claude Esling | Xiang Zhao | Liang Zuo
Source: Computational Materials Science (article, 2023-12-22)
Field: Materials Science — Materials Chemistry

Machine learning informed tetragonal ratio c/a of martensite Tetragonal crystal system ; Martensite ; Generalization ; Materials science ; Ab initio ; Relation (database) ; Work (physics) ; Computer science ; Thermodynamics ; Machine learning ; Mathematics ; Crystallography ; Data mining ; Chemistry ; Physics ; Crystal structure ; Mathematical analysis ; Metallurgy Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Aleksander E. P. Durumeric | Gregory A. Voth
Source: The Journal of Chemical Physics (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Using classifiers to understand coarse-grained models and their fidelity with the underlying all-atom systems Bottom-up coarse-grained (CG) molecular dynamics models are parameterized using complex effective Hamiltonians. These models are typically optimized to approximate high dimensional data from atomistic simulations. However, human validation of these models is often limited to low dimensional statistics that do not necessarily differentiate between the CG model and said atomistic simulations. We propose that classification can be used to variationally estimate high dimensional error and that explainable machine learning can help convey this information to scientists. This approach is demonstrated using Shapley additive explanations and two CG protein models. This framework may also be valuable for ascertaining whether allosteric effects at the atomistic level are accurately propagated to a CG model. Parameterized complexity ; Computer science ; Fidelity ; Atom (system on chip) ; Molecular dynamics ; High fidelity ; Machine learning ; Artificial intelligence ; Algorithm ; Statistical physics ; Chemistry ; Physics ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Ivan Lobzenko | Tomohito Tsuru | Hideki Mori | Daisuke MATSUNAKA | Yoshinori Shiihara
Source: MATERIALS TRANSACTIONS (article, 2023-09-24)
Field: Materials Science — Materials Chemistry

Implementation of Atomic Stress Calculations with Artificial Neural Network Potentials Atomic stress, utilized in molecular mechanics and molecular dynamics, is valuable in analyzing complex phenomena such as heat transfer, crack propagation and void growth. However, traditional modeling techniques designed for large-scale systems may lack the precision achievable through first-principles calculations. To overcome this limitation, we propose an approach based on artificial neural network (ANN) potentials to compute atomic stress. A crucial aspect of this method is the use of central force decomposition to derive the atomic stress tensor of the ANN potential, ensuring compliance with the balance between linear and angular momentum. By comparing atomic stress calculations for surface systems in Fe and Al using the ANN and embedded-atom (EAM) potentials, we demonstrate that the ANN potential accurately reproduces the stress oscillations near the surface layer predicted by first-principles calculations. This scheme allows us to evaluate atomic stress with nearly the same accuracy as first-principles calculations, even in large-scale models with complex geometries and defect structures. Atomic units ; Artificial neural network ; Stress (linguistics) ; Molecular dynamics ; Materials science ; Atom (system on chip) ; Cauchy stress tensor ; Continuum mechanics ; Embedded atom model ; Statistical physics ; Physics ; Computer science ; Classical mechanics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Nan Wu | Yingjie Li | Cong Hao | Steve Dai | Cunxi Yu | Yuan Xie
Source: arXiv (Cornell University) (preprint, 2023-03-14)
Field: Materials Science — Materials Chemistry

Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic minimization, datapath synthesis, malicious logic identification, etc. Mostly, conventional reasoning approaches leverage structural hashing and functional propagation, suffering from limited scalability and inefficient usage of modern computing power. In response, we propose a novel symbolic reasoning framework exploiting graph neural networks (GNNs) and GPU acceleration to reason high-level functional blocks from gate-level netlists, namely Gamora, which offers high reasoning performance w.r.t exact reasoning algorithms, strong scalability to BNs with over 33 million nodes, and generalization capability from simple to complex designs. To further demonstrate the capability of Gamora, we also evaluate its reasoning performance after various technology mapping options, since technology-dependent optimizations are known to make functional reasoning much more challenging. Experimental results show that (1) Gamora reaches almost 100% and over 97% reasoning accuracy for carry-save-array (CSA) and Booth-encoded multipliers, respectively, with up to six orders of magnitude speedups compared to the state-of-the-art implementation in the ABC framework; (2) Gamora maintains high reasoning accuracy (&gt;92%) in finding functional modules after complex technology mapping, upon which we comprehensively analyze the impacts on Gamora reasoning from technology mapping. Computer science ; Scalability ; Leverage (statistics) ; Datapath ; Theoretical computer science ; Graph ; Artificial intelligence ; Parallel computing Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Hoan D. Nguyen | Wei Wang | Xupei Yao | Kwesi Sagoe‐Crentsil | Wenhui Duan
Source: Journal of Materials Science (article, 2023-06-01)
Field: Materials Science — Materials Chemistry

Porosity-Modulus Mapping enhanced nanomechanical analysis of heterogeneous materials Materials science ; Porosity ; Microstructure ; Composite material ; Modulus ; Solid mechanics ; Nanoscopic scale ; Nanotechnology Corrosion Behavior and Inhibition ; Force Microscopy Techniques and Applications ; Concrete and Cement Materials Research
Authors: Alberto Hernández | Tim Mueller
Source: Physical Review Materials (article, 2023-05-19)
Field: Materials Science — Materials Chemistry

Generalizability of functional forms for interatomic potential models discovered by symbolic regression In recent years, great progress has been made in the use of machine-learning algorithms to develop interatomic potential models. Machine-learned potential models are typically orders of magnitude faster than density functional theory but also orders of magnitude slower than physics-derived models such as the embedded atom method (EAM). In our previous work, we used symbolic regression to develop fast, accurate, and transferrable interatomic potential models for copper with functional forms that resemble those of the EAM. To determine the extent to which the success of these forms was specific to copper, here, we explore the generalizability of these models to other face-centered cubic transition metals and analyze their out-of-sample performance on several material properties. We found that these forms work particularly well on elements that are chemically similar to copper. When compared with optimized Sutton-Chen models, which have similar complexity, the functional forms discovered using symbolic regression perform better across all elements considered, except gold, where they have a similar performance. They perform similarly to a moderately more complex embedded atom form on properties on which they were trained, and they are more accurate on average on other properties. We attribute this improved generalized accuracy to the relative simplicity of the models discovered using symbolic regression. The genetic programming models are found to outperform other models from the literature $\ensuremath{\sim}50%$ of the time in a variety of property predictions, with $\ensuremath{\sim}\frac{1}{10}\mathrm{th}$ the model complexity on average. We discuss the implications of these results to the broader application of symbolic regression to the development of potentials and highlight how models discovered for one element can be used to seed searches for different elements. Generalizability theory ; Symbolic regression ; Atom (system on chip) ; Regression ; Regression analysis ; Computer science ; Simplicity ; Interatomic potential ; Genetic programming ; Materials science ; Artificial intelligence ; Algorithm ; Statistical physics ; Machine learning ; Mathematics ; Molecular dynamics ; Physics ; Computational chemistry ; Statistics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Protein Structure and Dynamics
Authors: Prerna Sharma | V. R. Prasath Kumar
Source: Innovative Infrastructure Solutions (article, 2023-11-24)
Field: Materials Science — Polymers and Plastics

Amelioration of sandwich panels by replacing polyurethane foam with coconut husk and study on computational prediction using ANN and LR Polyurethane ; Husk ; Flexural strength ; Compressive strength ; Materials science ; Composite material ; Absorption of water ; Polyol ; Structural engineering ; Engineering Natural Fiber Reinforced Composites ; Polymer composites and self-healing ; Masonry and Concrete Structural Analysis
Authors: Jamie D. Gravell | Jaehyun Cho | Seung‐Joon Lee | Sylvie Aubry | Ill Ryu
Source: Materialia (article, 2023-08-13)
Field: Materials Science — Materials Chemistry

Prediction of dislocation - grain boundary interactions in FCC aluminum bicrystals using a modified continuum criterion and machine learning methods Materials science ; Grain boundary ; Dislocation ; Parametric statistics ; Statistical physics ; Slip (aerodynamics) ; Gaussian ; Condensed matter physics ; Microstructure ; Thermodynamics ; Metallurgy ; Mathematics ; Physics ; Composite material Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Aluminum Alloy Microstructure Properties
Authors: Arne De Temmerman | Matthias De Ryck | Tom Hellemans | Mathias Verbeke
Source:  (article, 2023-07-18)
Field: Chemistry — Analytical Chemistry

Infrared hyperspectral analysis for non-invasive, inline fat content determination in bakery products In the food industry, an accurate determination of the quantity of ingredients is of key importance. On the one hand, because this is prescribed by European regulations, which require a precise quantity indication on the food labeling. On the other hand, because small variations in the fat quantity and type produce a different taste experience for the consumer. Since current automated production lines for bakery products often cause fluctuations in the fat concentration from the target value, accurate and timely ingredient concentration measurements are required for quality control. However, conventional wet chemistry-based methods are a manual, destructive form of testing that require a considerable amount of time, typically several hours to days, to generate an outcome. To this end, this paper investigates the potential of machine learning-based regression using features extracted from hyperspectral imaging for the non-invasive and inline prediction of fat and moisture concentration in bakery products. While the conventional testing method is still advantageous in terms of accuracy, the results show that the proposed hyperspectral imaging approach is a promising alternative due to its real-time and non-destructive nature, offering the possibility to inspect larger quantities of products. Hyperspectral imaging ; Food products ; Computer science ; Quality (philosophy) ; Ingredient ; Process engineering ; Environmental science ; Artificial intelligence ; Food science ; Engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality