jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yong-Fong Chang | Yung‐Chih Chen | Yu-Chen Cheng | Shu-Hong Lin | Che-Hsu Lin | Chun-Yuan Chen | Yu-Hsuan Chen | Y.-R. Lee | Jia-Wei Lin | Hsun-Wei Pao | Shih-Chieh Chang | Yiting Li | Chun-Yao Wang
Source:  (article, 2024-06-10)
Field: Chemistry — Analytical Chemistry

IR drop Prediction Based on Machine Learning and Pattern Reduction With the advances in semiconductor technology, the sizes of transistors are getting smaller, which has led to an increasingly severe impact of IR drop. Consequently, this trend has amplified the significance of IR drop analysis within the realm of chip design. However, analyzing IR drop is resource-intensive and time-consuming, since numerous simulation patterns are required to verify the power integrity of circuits. Additionally, with every engineering change order (ECO) step, a reevaluation is necessary. In this paper, we propose a machine learning-based method to predict IR drop levels and present an algorithm for reducing simulation patterns, which could reduce the time and computing resources required for IR drop analysis within the ECO flow. Experimental results show that our approach can reduce the number of patterns by approximately 50%, thereby decreasing the analysis time while maintaining accuracy. Reduction (mathematics) ; Computer science ; Drop (telecommunication) ; Machine learning ; Artificial intelligence ; Drop out ; Mathematics ; Telecommunications Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Industrial Vision Systems and Defect Detection
Authors: Xiaoyu 晓昱 Cheng 程 | Chenxue 晨雪 Xie 解 | Yulun 宇伦 Liu 刘 | Ruixue 瑞雪 Bai 白 | Nanhai 南海 Xiao 肖 | Yanbo 琰博 Ren 任 | Xilin 喜林 Zhang 张 | Hui 惠 Ma 马 | Chongyun 崇云 Jiang 蒋
Source: Chinese Physics B (article, 2024-01-30)
Field: Materials Science — Materials Chemistry

Image segmentation of exfoliated two-dimensional materials by generative adversarial network-based data augmentation Mechanically cleaved two-dimensional materials are random in size and thickness. Recognizing atomically thin flakes by human experts is inefficient and unsuitable for scalable production. Deep learning algorithms have been adopted as an alternative, nevertheless a major challenge is a lack of sufficient actual training images. Here we report the generation of synthetic two-dimensional materials images using StyleGAN3 to complement the dataset. DeepLabv3Plus network is trained with the synthetic images which reduces overfitting and improves recognition accuracy to over 90%. A semi-supervisory technique for labeling images is introduced to reduce manual efforts. The sharper edges recognized by this method facilitate material stacking with precise edge alignment, which benefits exploring novel properties of layered-material devices that crucially depend on the interlayer twist-angle. This feasible and efficient method allows for the rapid and high-quality manufacturing of atomically thin materials and devices. Generative adversarial network ; Adversarial system ; Segmentation ; Generative grammar ; Computer science ; Artificial intelligence ; Image (mathematics) ; Computer vision ; Pattern recognition (psychology) ; Materials science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides
Authors: Matthias Scheffler | Sebastian Bauer | Peter Benner | Tristan Bereau | Volker Blüm | Mario Boley | Christian Carbogno | C. Richard A. Catlow | Gerhard Dehm | Sebastian Eibl | Ralph Ernstorfer | Ádám Fekete | Lucas Foppa | Peter Fratzl | Christoph Freysoldt | Baptiste Gault | Luca M. Ghiringhelli | Sajal Kumar Giri | Anton Gladyshev | Pawan Goyal | Jason Hattrick‐Simpers | Lara Kabalan | Petr Karpov | Mohammad S. Khorrami | Christoph T. Koch | Sebastian Kokott | Thomas Kosch | Igor Kowalec | Kurt Kremer | Andreas Leitherer | Yue Li | Christian H. Liebscher | Andrew J. Logsdail | Zhongwei Lu | Phuc Luong | Andreas Marek | F. Merz | Jaber Rezaei Mianroodi | Jörg Neugebauer | Thomas A. R. Purcell | Dierk Raabe | Markus Rampp | Mariana Rossi | Jan M. Rost | Ulf Saalmann | Alaukik Saxena | Luigi Sbailò | Markus Scheidgen | Marcel Schloz | Daniel F. Schmidt | Simon Teshuva | Annette Trunschke | Ye Wei | Gerhard Weikum | R. Patrick Xian | Yiyu Yao | Meng Zhao
Source: ChemRxiv (preprint, 2024-02-23)
Field: Materials Science — Materials Chemistry

Roadmap on Data-Centric Materials Science Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. Data science ; Transformative learning ; Field (mathematics) ; Computer science ; Big data ; Nanotechnology ; Key (lock) ; Engineering ethics ; Sociology ; Engineering ; Materials science ; Data mining Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Tianbai Huang | Robert Geitner | Alexander Croy | Stefanie Gräfe
Source: ChemRxiv (preprint, 2024-04-09)
Field: Materials Science — Materials Chemistry

Tailoring Phosphine Ligands for Improved C-H Activation: Insights from Δ-Machine Learning Transition metal complexes have played crucial roles in various homogeneous catalytic processes due to their exceptional versatility. This adaptability stems not only from the central metal ions but also from the vast array of choices of the ligand spheres, which form an enormously large chemical space. For example, Rh complexes, with a well-designed ligand sphere, are known to be efficient in catalyzing the C-H activation process in alkanes. To investigate the structure-property relation of the Rh complex and identify the optimal ligand that minimizes the calculated reaction energy ΔE of an alkane C-H activation, we have applied a Δ-Machine Learning method trained on various features to study 1,743 pairs of reactants (Rh(PLP)(Cl)(CO)) and intermediates (Rh(PLP)(Cl)(CO)(H)(propyl)). Our findings demonstrate that the models exhibit robust predictive performance when trained on features derived from electron density (R2 = 0.816), and SOAPs (R2 = 0.819), a set of position-based descriptors. Leveraging the model trained on xTB-SOAPs that only depend on the xTB-equilibrium structures, we propose an efficient and accurate screening procedure to explore the extensive chemical space of bisphosphine ligands. By applying this screening procedure, we identify ten newly selected reactant-intermediate pairs with an average ΔE of 33.2 kJ mol-1, remarkably lower than the average ΔE of the original data set of 68.0 kJ mol-1. This underscores the efficacy of our screening procedure in pinpointing structures with significantly lower energy levels. Ligand (biochemistry) ; Phosphine ; Chemistry ; Catalysis ; Yield (engineering) ; Computational chemistry ; Computer science ; Thermodynamics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Anastasia Yu. Sholokhova | Dmitriy D. Matyushin | М. В. Шашков
Source: ChemRxiv (preprint, 2024-03-21)
Field: Chemistry — Spectroscopy

Quantitative structure-retention relationships for pyridinium-based ionic liquids used as gas chromatographic stationary phases: convenient software and assessment of reliability of the results Ionic liquids, i.e., organic salts with a low melting point, can be used as gas chromatographic liquid stationary phases. These stationary phases have some advantages such as peculiar selectivity, high polarity, and thermostability. Many previous works are devoted to such stationary phases. However, there are still no large enough retention data sets of structurally diverse compounds for them. Consequently, there are very few works devoted to quantitative structure-retention relationships (QSRR) for ionic liquid-based stationary phases. This work is aimed to close this gap. Three ionic liquids with substituted pyridinium cations are considered. We provide large enough data sets (123 - 158 compounds) that can be used in further works devoted to QSRR and related methods. We provide a QSRR study using this data set and demonstrate the following. The retention index for a polyethylene glycol stationary phase (denoted as RIPEG), predicted using another model, can be used as a molecular descriptor. The use of this descriptor significantly improves the accuracy of the QSRR model. Both deep learning-based and linear models were considered for RIPEG prediction. The ability to predict the retention indices for ionic liquid-based stationary phases with high accuracy is demonstrated. Particular attention is paid to the reproducibility and reliability of the QSRR study. It was demonstrated that adding/removing several compounds, small perturbations of the data set can considerably affect the results such as descriptor importance and model accuracy. These facts have to be considered in order to avoid misleading conclusions. For the QSRR research, we developed a software tool with a graphical user interface, which we called CHERESHNYA. It is intended to select molecular descriptors and construct linear equations connecting molecular descriptors with gas chromatographic retention indices for any stationary phase. The software allows the user to generate several hundred molecular descriptors (one-dimensional and two-dimensional). Among them, predicted retention indices for popular stationary phases such as polydimethylsiloxane and polyethylene glycol are used as molecular descriptors. Various methods for selecting (and assessing the importance of) molecular descriptors have been implemented, in particular the Boruta algorithm, partial least squares, genetic algorithms, L1-regularized regression (LASSO) and others. The software is free, open-source and available online. Pyridinium ; Reliability (semiconductor) ; Chromatography ; Ionic liquid ; Chemistry ; Computer science ; Thermodynamics ; Physics ; Organic chemistry Analytical Chemistry and Chromatography ; Ionic liquids properties and applications ; Inorganic and Organometallic Chemistry
Authors: Haoyi Tian | Jobin K. Joy | Dimitris C. Lagoudas
Source:  (article, 2024-01-04)
Field: Materials Science — Materials Chemistry

A Data-Driven Approach With Physics-Based Constraints for Modeling Partial Reorientation Response of Magnetic Shape Memory Alloys An Artificial Neural Network (ANN) modeling approach for predicting the magneto-mechanical behavior of magnetic shape memory alloys (MSMAs) is proposed. ANNs are trained on experimental data from Ni–Mn–Ga system MSMAs to capture the hysteresis behavior of the martensitic variant reorientation. The modeling approach is implemented for experiments with constant stress and constant field boundary conditions to show that it can be generalized. Additional physics-based constraints are introduced to the ANN during training to improve prediction of partial hysteresis cycles. The capability of the model in predicting major and minor reorientation cycles beyond the training data is shown. The effects of the physics-based constraints and limitations of the proposed data-based approach will be discussed. The proposed approach will be useful in the prediction of MSMA actuator behaviors and in serving as a foundation from which data-driven MSMA models with more complexity and capabilities can be developed. Computer science ; Data modeling ; Physics ; Computational science ; Parallel computing ; Database Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials
Authors: Hongjin Wang | Chenxing Luo | Renata M. Wentzcovitch
Source:  (preprint, 2024-09-26)
Field: Chemistry — Physical and Theoretical Chemistry

Machine learning potential for serpentines Serpentines are layered hydrous magnesium silicates (MgO·SiO2·H2O) formed through serpentinization, a geochemical process that significantly alters the physical property of the mantle. They are hard to investigate experimentally and computationally due to the complexity of natural serpentine samples and the large number of atoms in the unit cell. We developed a machine learning (ML) potential for serpentine minerals based on density functional theory (DFT) calculation with the r2SCAN meta-GGA functional for molecular dynamics simulation. We illustrate the success of this ML potential model in reproducing the high-temperature equation of states of several hydrous phases under the Earth’s subduction zone conditions, including brucite, lizardite, and antigorite. In addition, we investigate the polymorphism of antigorite with periodicity m = 13–24, which is believed to be all the naturally existent antigorite species. We found that antigorite with m larger than 21 appears more stable than lizardite at low temperatures. This machine learning potential can be further applied to investigate more complex antigorite superstructures with multiple coexisting periodic waves. Psychology ; Computer science Various Chemistry Research Topics
Authors: Panithan La-aiddee | Paramin Sangwongngam | Lunchakorn Wuttisittikulkij | Pisit Vanichchanunt
Source:  (article, 2024-07-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Generative Adversarial Network-Based Approach for Reflective-Metasurface Unit-Cell Synthesis in mmWave Bands In recent years, data-driven design approaches based on generative machine learning (ML) have been applied in the field of metasurface design. Even though generative machine learning has demonstrated remarkable capabilities in metasurface synthesis, training procedures to obtain an appropriate model remain a challenge. In this work, we present the deployment of conditional generative adversarial networks (cGAN) to design reflective standard unit cells for the use of 5G millimeter wave (mm Wave) passive metasurface reflectors. A total of 48 different configurations of cGAN were trained over a range of hyperparameters selected under proper guidelines used in the literature. The design performance of each cGAN configuration is measured by a mean absolute error (MAE) between a simulated reflection phase of a generated unit cell and a target reflection phase. The test results show that cGAN s trained by updating the discriminator one step for each single generator update tend to offer lower MAEs compared to cGANs trained by updating the discriminator three iterations for one generator update. The optimal cGAN among 48 hyperparameter configurations can generate unit cells with reflection phase resembling the desired target one. Generative adversarial network ; Computer science ; Generative grammar ; Adversarial system ; Unit (ring theory) ; Reflection (computer programming) ; Artificial intelligence ; Deep learning ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Eitan Hershkovitz | Timothy Jinsoo Yoo | Xiaofei Pu | Kaustubh Bawane | Tadachika Nakayama | Hisayuki Suematsu | Lingfeng He | Honggyu Kim
Source: Journal of the American Ceramic Society (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Unveiling and mapping polymorphs in fluorite Y <sub>2</sub> TiO <sub>5</sub> using 4D‐STEM and unsupervised machine learning Abstract Y 2 TiO 5 belongs to the Ln 2 TiO 5 (Ln = lanthanide or Y) family of ceramic materials and exhibits a range of desirable material properties such as radiation tolerance, frustrated magnetism, and large dielectric constant. However, understanding the complex crystal structure of Y 2 TiO 5 remains elusive, given that Y 2 TiO 5 can adopt multiple polymorphs such as cubic, orthorhombic, and hexagonal phases within the lattice. In this work, we report a detailed structural analysis of Y 2 TiO 5 using four‐dimensional scanning transmission electron microscopy coupled with unsupervised machine learning. The pyrochlore nanodomains, characterized by the ordered arrangement of yttrium cations on the A site of their A 2 BO 5 structure, are present within the matrix of a predominantly fluorite‐structured Y 2 TiO 5 along with a third polymorph, the hexagonal phase. The pyrochlore phase is found to form 2 nm boundary regions around hexagonal phase stacking faults, highlighting the potential influence of the hexagonal phase on the occurrence and distribution of the pyrochlore phase. Lastly, we identify a unique pyrochlore phase with asymmetric arrangement of cation ordering along a single planar direction. Our findings provide invaluable insights into the possible mechanisms stabilizing pyrochlore nanodomains within the fluorite lattice of Y 2 TiO 5 . Pyrochlore ; Materials science ; Orthorhombic crystal system ; Crystallography ; Stacking ; Fluorite ; Phase (matter) ; Yttrium ; Crystal structure ; Oxide ; Chemistry ; Physics ; Nuclear magnetic resonance Nuclear materials and radiation effects ; Advanced Condensed Matter Physics ; Electronic and Structural Properties of Oxides
Authors: Shiva Shokri | Pooria Sedigh | Mehdi Hojjati | Tsz-Ho Kwok
Source: Transactions of the Canadian Society for Mechanical Engineering (article, 2024-07-12)
Field: Materials Science — General Materials Science

Closed-loop control of surface preparation for metallizing fiber-reinforced polymer composites This study introduces a novel approach to enhance the surface properties of fiber-reinforced polymer composites through thermal spray coatings, utilizing a metal mesh as an anchor to improve coating adhesion. A critical step in this process is achieving optimal exposure of the metal mesh by sandblasting prior to coating. To address this challenge, we propose a closed-loop control system designed to inspect and blast parts effectively. Our method leverages top-view microscope images as inputs, employing a convolutional neural network (CNN) to correlate these images with the corresponding exposure levels of the metal mesh, measured via a destructive method. Upon training, the CNN model accurately estimates the exposure level solely from the top-view images, facilitating real-time feedback to guide subsequent sandblasting operations. Unlike traditional manual inspection methods, which demand expertise and experience, our automated approach streamlines the inspection process using a cost-effective portable digital microscope. Experimental findings validate the efficacy of our method in successfully discerning surface preparation status with an accuracy rate of 95% and demonstrate its practical utility in closed-loop control. Our study not only offers a robust methodology for quantifying surface preparation data but also presents a significant advancement in automating the inspection process. Moreover, the broader implications of our approach extend to various manufacturing sectors, where defect detection and closed-loop control are crucial for optimizing production efficiency and product quality. Composite material ; Materials science ; Metallizing ; Fiber ; Polymer ; Surface (topology) ; Layer (electronics) ; Geometry ; Mathematics Material Properties and Applications ; Polymer Science and Applications
Authors: Satish Pujari | V. Vikram Reddy | Rapeta Sundara Ramam | Siva Rao Subramonian
Source: Materials Research Innovations (article, 2024-08-26)
Field: Materials Science — Polymers and Plastics

Optimisation study through ANN on influence of FeCl <sub>3</sub> nanoparticles on physical, mechanical and magnetic characteristics of polymer composite In the current work, FeCl3 nanoparticles were developed and then impregnated into polymer composites by varying the weight proportions of the fibres from 20–50%. The samples were prepared using the hand-lay-up technique. The physical behaviour of the prepared samples was evaluated by conducting a moisture and absorption test. The mechanical and magnetic behaviours of the samples were evaluated by conducting tensile and VSM tests. A novel orange peel fibre composite was developed for magnetic properties. Results revealed that there is a significant enhancement in the physical, mechanical, and magnetic behaviour of the prepared composite up to 30 Vol% of fibre, and there is a significant behaviour for 50 Vol% of fibre, but there is a significant decrease in 40 Vol% of fibre. In addition to this, Artificial neural networks (ANN) were used to optimise the experimental data. Materials science ; Composite material ; Composite number ; Ultimate tensile strength ; Polymer ; Magnetic nanoparticles ; Nanoparticle ; Tensile testing ; Nanotechnology Natural Fiber Reinforced Composites ; Advanced Sensor and Energy Harvesting Materials ; Mechanical Engineering and Vibrations Research
Authors: Edgar Josué Landinez Borda | Kenneth O. Berard | Annette Lopez | Brenda M. Rubenstein
Source: Faraday Discussions (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Gaussian processes for finite size extrapolation of many-body simulations Key to being able to accurately model the properties of realistic materials is being able to predict their properties in the thermodynamic limit. Nevertheless, because most many-body electronic structure methods scale as a high-order polynomial, or even exponentially, with system size, directly simulating large systems in their thermodynamic limit rapidly becomes computationally intractable. As a result, researchers typically estimate the properties of large systems that approach the thermodynamic limit by extrapolating the properties of smaller, computationally-accessible systems based on relatively simple scaling expressions. In this work, we employ Gaussian processes to more accurately and efficiently extrapolate many-body simulations to their thermodynamic limit. We train our Gaussian processes on Smooth Overlap of Atomic Positions (SOAP) descriptors to extrapolate the energies of one-dimensional hydrogen chains obtained using two high-accuracy many-body methods: coupled cluster theory and Auxiliary Field Quantum Monte Carlo (AFQMC). In so doing, we show that Gaussian processes trained on relatively short 10-30-atom chains can predict the energies of both homogeneous and inhomogeneous hydrogen chains in their thermodynamic limit with sub-milliHartree accuracy. Unlike standard scaling expressions, our GPR-based approach is highly generalizable given representative training data and is not dependent on systems' geometries or dimensionality. This work highlights the potential for machine learning to correct for the finite size effects that routinely complicate the interpretation of finite size many-body simulations. Extrapolation ; Statistical physics ; Limit (mathematics) ; Gaussian ; Gaussian process ; Thermodynamic limit ; Physics ; Mathematics ; Statistics ; Mathematical analysis ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics