jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tim Bücher | Hussam Amrouch
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Modeling TPU Thermal Maps Under Superlattice Thermoelectric Cooling The recent renaissance in machine learning is requesting computing power at an ever-increasing rate. In order to meet this demand, tensor processing units (TPU) are becoming popular because they hold the promise to be more efficient in terms of power usage as well as throughput compared to GPUs. This is achieved by integrating a huge number of matrix-multiply units (MMU) to perform a massive amount of parallel multiply-accumulate operations. However, this causes excessive localized hot-spots. Elevated on-chip temperatures jeopardize reliability and significantly reduce the lifetime of semiconductor devices by accelerating aging defects. Therefore, it is vital to obtain accurate thermal maps of the TPU die at the design time to ensure reliability requirements during the run-time. Due to the high power density, traditional forced air convection cooling is often insufficient. Hence, liquid cooling may seem promising, but it requires substantial maintenance effort. To overcome this challenge, novel cooling concepts need to be explored. Recent advances in thermoelectric coolers (TEC), more specifically ultra thin-film superlattice thermoelectric (TE) devices, have opened new doors to combat this obstacle. To this end, finite-element (FEM) simulations enable designers to find tradeoffs between temperature and cooling cost. However, such multi-physics simulations are extremely time-consuming, which prevents designers from exploring the available design-space. In this work, we demonstrate a neural network (NN) model that captures the effects of a superlattice TEC device mounted on top of a TPU die. The additional required cooling cost is also estimated. The commercial multi-physics tool ANSYS is employed in order to obtain high resolution data towards training the NN model. Our NN model allows to speed up the design-space exploration considerably, reducing the time required to generate a single thermal map from, on average, 45 min in ANSYS to merely 70 ms with an average prediction error of 0.26°C. Our model covers a wide range of design parameters, such as convection film coefficient, power density and superlattice TEC cooling intensity. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Open Source:</b> <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ML-CAD/TPU-Thermal-Map-Prediction.git</uri> Our framework including the generated thermal image dataset and the trained neural network models are publicly available under the above link. Superlattice ; Thermoelectric cooling ; Thermoelectric effect ; Materials science ; Thermal ; Optoelectronics ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Phase-change materials and chalcogenides
Authors: M. A. Bushmakova | Е. В. Кустова
Source: Vestnik St Petersburg University Mathematics (article, 2022-03-01)
Field: Chemistry — Spectroscopy

Modeling the Vibrational Relaxation Rate Using Machine-Learning Methods The aim of the study is to develop an efficient algorithm for solving nonequilibrium gas-dynamics problems in the detailed state-to-state vibrational and chemical kinetics approximation. Optimization of calculation of the vibrational relaxation rate using machine-learning algorithms is discussed. Since traditional methods of calculation require a large number of operations as well as time and memory resources, it is proposed to predict the relaxation rate instead of direct calculations. K-nearest neighbor and histogram gradient boosting algorithms are considered. The algorithms are trained on datasets obtained using two classical reaction-rate coefficient models: the forced harmonic oscillator model and the Schwartz–Slawsky–Herzfeld model. The trained algorithms are used to solve the problem of the spatially homogeneous relaxation of the O2–O mixture. The accuracy and computation time of different methods are compared. It is shown that the used algorithms make it possible to approximate with good accuracy the values of relaxation terms and to solve approximately the system of equations for macroparameters. Based on the obtained data, we recommend the use of machine-learning methods in the problems of nonequilibrium gas dynamics with detailed vibrational and chemical kinetics. Ways of further optimizing the considered methods are discussed. Relaxation (psychology) ; Computation ; Computer science ; Boosting (machine learning) ; Non-equilibrium thermodynamics ; Harmonic oscillator ; Algorithm ; Histogram ; Vibrational energy relaxation ; Statistical physics ; Machine learning ; Artificial intelligence ; Mathematical optimization ; Mathematics ; Thermodynamics ; Physics ; Excited state ; Quantum mechanics Spectroscopy and Laser Applications ; Phase Equilibria and Thermodynamics ; Machine Learning in Materials Science
Authors: Julien Baderot | Marion Grould | Diganta Misra | Clément Nicolas | Ali Hallal | Sergio Martinez | J. Foucher
Source: Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena (article, 2022-09-01)
Field: Materials Science — Surfaces, Coatings and Films

Application of deep-learning based techniques for automatic metrology on scanning and transmission electron microscopy images Scanning or transmission electron microscopy (SEM/TEM) are standard techniques used during Research and Development (R&amp;D) phases to study the structure and morphology of microscopic materials. Variety in object shapes and sizes are observed in such images to ensure robust micro- and nanomaterials critical dimension analysis. This way, precision and accuracy can be guaranteed during materials manufacturing processes. Such diversity and complexity in the data make it challenging to automatically extract the desired measurements of these microscopic structures. Existing tools in metrology often require many manual interactions, therefore being slow and prone to user errors. Proposed semiautomatic and automatic tools in the state-of-the-art are also limited and not designed to handle large variations across the images. Thus, the application of advanced machine or deep learning techniques could bring great efficiency in SEM/TEM image analysis and measurements for microscopic scale R&amp;D processes. In this paper, we demonstrate the feasibility of deep-learning based object detection and instance segmentation models to perform automatic and accurate metrology on microscopic images with high object variability. We also show that custom object detection models prepared using pretrained weights, finetuned on very limited custom data, can outperform detection models built using traditional methods. This is particularly useful in metrology for the semiconductor industry, where data scarcity is common. When the data are available, we observe that it can be useful to be able to generate a large number of quality annotations to use instance segmentation. This could allow the training of more complex deep learning models for particle recognition and analysis. Therefore, we propose a semiautomatic tool to help produce annotations and demonstrate its application in an instance segmentation task. Metrology ; Computer science ; Artificial intelligence ; Deep learning ; Segmentation ; Machine learning ; Object detection ; Computer vision ; Pattern recognition (psychology) ; Optics Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Image Processing Techniques and Applications
Authors: Liangyuan Ren | Shaoning Geng | Ping Jiang | Song Gao | Chu Han
Source: Calphad (article, 2022-06-27)
Field: Materials Science — Materials Chemistry

Numerical simulation of dendritic growth during solidification process using multiphase-field model aided with machine learning method Computer science ; Process (computing) ; Stability (learning theory) ; Field (mathematics) ; Constraint (computer-aided design) ; Independence (probability theory) ; Eutectic system ; Grid ; Ternary operation ; Artificial intelligence ; Algorithm ; Machine learning ; Materials science ; Mechanical engineering ; Mathematics ; Engineering Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Qingguan Song | Lei Zhang | Zeyao Mo
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Alleviating the stability–performance contradiction of cage-like high-energy-density materials by a backbone-collapse and branch-heterolysis competition mechanism Searching for advanced strategies to alleviate the inherent contradiction between stability and performance has been one of the most challenging tasks in the development of high-energy-density materials (HEDMs) for centuries. Recently, our high-throughput calculations and machine learning studies showed that cage-like HEDMs have a high probability of owning simultaneous high thermostability and high performance. To explore the physical mechanism of the data-driven prediction, quantum mechanical molecular dynamics simulations were carried out to study the early thermolysis of a series of caged HEDMs at the crystal level. Herein, an interesting competitive process between backbone-collapse and branch-heterolysis was discovered, and the process was found to significantly relate to the temperature and isotropy degree of the cage-like conformation. In the simulated storage or transport temperature range, branch-heterolysis is the predominating process. The highly isotropic cage-like conformation can delay the onset time of HEDMs, providing the reactant molecules with extra stability to suppress successive decomposition. However, in the simulated explosion temperature range, the backbone-collapse became dominant. A considerable scope of reactant molecules was initiated through backbone-collapse, which deteriorated the thermostability of the caged HEDMs and accelerated their energy release, endowing them with higher performance. The current research demonstrates cage-like conformations in alleviating the stability-performance contradiction of HEDMs and provides a theoretical guide for the rational design of novel advanced compounds. Heterolysis ; Cage ; Mechanism (biology) ; Contradiction ; Competition (biology) ; Chemical physics ; Stability (learning theory) ; Chemistry ; Computational chemistry ; Nanotechnology ; Materials science ; Physics ; Ecology ; Quantum mechanics ; Biology ; Engineering ; Computer science ; Organic chemistry MXene and MAX Phase Materials ; Diamond and Carbon-based Materials Research ; Energetic Materials and Combustion
Authors: Arnab Kabiraj | Tripti Jain | Santanu Mahapatra
Source: Patterns (article, 2022-11-14)
Field: Materials Science — Materials Chemistry

Massive Monte Carlo simulations-guided interpretable learning of two-dimensional Curie temperature Monte Carlo (MC) simulation of the classical Heisenberg model has become the de facto tool to estimate the Curie temperature (<i>T</i> <sub>C</sub>) of two-dimensional (2D) magnets. As an alternative, here we develop data-driven models for the five most common crystal types, considering the isotropic and anisotropic exchange of up to four nearest neighbors and the single-ion anisotropy. We sample the 20-dimensional Heisenberg spin Hamiltonian and conceive a bisection-based MC technique to simulate a quarter of a million materials for training deep neural networks, which yield testing <i>R</i> <sup>2</sup> scores of nearly 0.99. Since 2D magnetism has a natural tendency toward low <i>T</i> <sub>C</sub>, learning-from-data is combined with data-from-learning to ensure a nearly uniform final data distribution over a wide range of <i>T</i> <sub>C</sub> (10-1,000 K). Global and local analysis of the features confirms the models' interpretability. We also demonstrate that the <i>T</i> <sub>C</sub> can be accurately estimated by a purely first-principles-based approach, free from any empirical corrections. Heisenberg model ; Monte Carlo method ; Curie temperature ; Anisotropy ; Statistical physics ; Hamiltonian (control theory) ; Isotropy ; Interpretability ; Magnetism ; Condensed matter physics ; Ferromagnetism ; Physics ; Artificial intelligence ; Mathematics ; Computer science ; Statistics ; Quantum mechanics ; Mathematical optimization Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Condensed Matter Physics
Authors: Ali Shabani | Gholamreza Nabiyouni | Davood Ghanbari
Source: Journal of Materials Science Materials in Electronics (article, 2022-03-18)
Field: Chemistry — Organic Chemistry

Preparation and photocatalytic study of CoFe2O4/TiO2/Au nanocomposites and their applications in organic pollutant degradation and modeling by an artificial neural network (ANN) Methyl orange ; Photocatalysis ; Materials science ; Nanocomposite ; X-ray photoelectron spectroscopy ; Chemical engineering ; Nanoparticle ; Environmental pollution ; Surface plasmon resonance ; Fourier transform infrared spectroscopy ; Nanotechnology ; Catalysis ; Organic chemistry ; Chemistry Nanomaterials for catalytic reactions ; Gold and Silver Nanoparticles Synthesis and Applications ; Copper-based nanomaterials and applications
Authors: Cefe López
Source: arXiv (Cornell University) (preprint, 2022-09-23)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Advanced Materials Artificial intelligence is gaining strength and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems and processes can be devised and optimized thanks to machine learning techniques and such progress can be turned into in-novative computing platforms. Future materials scientists will profit from understanding how machine learning can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by compu-tation to account for the origins, procedures and applications of artificial intelligence. Machine learning and its methods are reviewed to provide basic knowledge on its implementation and its potential. The materials and systems used to implement artificial intelligence with electric charges are finding serious competition from other information carrying and processing agents. The impact these techniques are having on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materi-als and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom as exemplified by the power to discover unheard of mate-rials or physical laws buried in data. Computer science ; Artificial intelligence ; Applications of artificial intelligence ; Data science Machine Learning in Materials Science
Authors: Yuan Chiang | Ting-Wai Chiu | Shu‐Wei Chang
Source: Frontiers in Materials (article, 2022-01-12)
Field: Materials Science — Materials Chemistry

ImageMech: From Image to Particle Spring Network for Mechanical Characterization The emerging demand for advanced structural and biological materials calls for novel modeling tools that can rapidly yield high-fidelity estimation on materials properties in design cycles. Lattice spring model , a coarse-grained particle spring network, has gained attention in recent years for predicting the mechanical properties and giving insights into the fracture mechanism with high reproducibility and generalizability. However, to simulate the materials in sufficient detail for guaranteed numerical stability and convergence, most of the time a large number of particles are needed, greatly diminishing the potential for high-throughput computation and therewith data generation for machine learning frameworks. Here, we implement CuLSM, a GPU-accelerated compute unified device architecture C++ code realizing parallelism over the spring list instead of the commonly used spatial decomposition, which requires intermittent updates on the particle neighbor list. Along with the image-to-particle conversion tool Img2Particle, our toolkit offers a fast and flexible platform to characterize the elastic and fracture behaviors of materials, expediting the design process between additive manufacturing and computer-aided design. With the growing demand for new lightweight, adaptable, and multi-functional materials and structures, such tailored and optimized modeling platform has profound impacts, enabling faster exploration in design spaces, better quality control for 3D printing by digital twin techniques, and larger data generation pipelines for image-based generative machine learning models. Spring (device) ; Particle (ecology) ; Characterization (materials science) ; Materials science ; Nanotechnology ; Composite material ; Geology ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Calcium Carbonate Crystallization and Inhibition
Authors: Eugenio Ferrario | Riccardo Miggiano | Menico Rizzi | Davide M. Ferraris
Source: Computational and Structural Biotechnology Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

The integration of AlphaFold-predicted and crystal structures of human trans-3-hydroxy-l-proline dehydratase reveals a regulatory catalytic mechanism Computational methods for protein structure prediction have made significant strides forward, as evidenced by the last development of the neural network AlphaFold, which outperformed the CASP14 competitors by consistently predicting the structure of target proteins. Here we show an integrated structural investigation that combines the AlphaFold and crystal structures of human <i>trans</i>-3-Hydroxy-l-proline dehydratase, an enzyme involved in hydroxyproline catabolism and whose structure had never been reported before, identifying a structural element, absent in the AlphaFold model but present in the crystal structure, that was subsequently proved to be functionally relevant. Although the AlphaFold model lacked information on protein oligomerization, the native dimer was reconstructed using template-based and <i>ab initio</i> computational approaches. Moreover, molecular phasing of the diffraction data using the AlphaFold model resulted in dimer reconstruction and straightforward structure solution. Our work adds to the integration of AlphaFold with experimental structural and functional data for protein analysis, crystallographic phasing and structure solution. Dimer ; Crystal structure ; Phaser ; Protein structure ; Structural genomics ; Chemistry ; Dehydratase ; Ab initio ; Trimer ; Crystallography ; Computational biology ; Biology ; Enzyme ; Biochemistry ; Physics Enzyme Structure and Function ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: Amel Ali Alhussan | M.S. Gaafar | Mafawez Alharbi | S.Y. Marzouk | Sayer Obaid Alharbi | Hussain ElRashidy | Mai S. Mabrouk | Hussah Nasser AlEisa | Nagwan Abdel Samee
Source: Electronics (article, 2022-03-26)
Field: Materials Science — Ceramics and Composites

Prediction of the Judd–Ofelt Parameters of Dy3+-Doped Lead Borosilicate Using Artificial Neural Network Developments in the field of glass research necessitate the mimicking of the optical properties of glass materials before melting the raw materials, as they are very expensive nowadays. An artificial neural network (ANN) was utilized during this work to train and predict the Judd–Ofelt parameters of various glasses, such as Ω2, Ω4 and Ω6, and the radiative lifetimes of many different types of rare-earth-doped glasses. The optimized ANN architecture for forecasting the Judd–Ofelt parameters were found to be very near to the experimentally measured parameters. Then, the conferred ANN model was employed to predict the Judd–Ofelt parameters of some newly prepared borosilicate glasses. Therein, a new glass system of 0.25 PbO–0.2 SiO2–(0.55 − x) B2O3–x Dy2O3, was prepared in order to employ the melt-quenching technique. The parameter results of the Judd–Ofelt theory, as well as the Ω2, Ω4 and Ω6 and radiative lifetimes showed that the supplementation of Dy2O3 switched the BO4 units to BO3 units with oxygens that were non-bridging atoms, thus weakening the glass frameworks. Therefore, it is very important to use an ANN to predict the Judd–Ofelt parameters of several rare-earth-doped glasses as luminescent materials. Borosilicate glass ; Doping ; Materials science ; Luminescence ; Radiative transfer ; Optical glass ; Mineralogy ; Analytical Chemistry (journal) ; Optics ; Composite material ; Optoelectronics ; Physics ; Chemistry Glass properties and applications ; Luminescence Properties of Advanced Materials ; Phase-change materials and chalcogenides
Authors: Kai Ye | Zhenyu Wang | Pengyuan Chen | Yangheran Piao | Kuan Zhang | Shu Wang | Xiaoming Jiang | Xiaohui Cui
Source: Scientific Reports (article, 2022-06-21)
Field: Chemistry — Analytical Chemistry

A novel GAN-based regression model for predicting frying oil deterioration Frying is a common food processing method because fried food is popular with consumers for its attractive colour and crisp taste. What's concerning is that the complex physical and chemical reactions occurring during deep frying are harmful to the well-being of people. For this reason, researchers proposed various detecting methods to assess frying oil deterioration. Some studies design sensor probe, others utilize spectroscopic related methods. However, these methods all need the participating of professionals and expensive instruments. Some of the methods can only function on a fixed temperature. To fix the defects of the above models, in this study, we make use of recent advances in machine learning, specifically generative adversarial networks (GAN). We propose a GAN-based regression model to predict frying oil deterioration. First, we conduct deep frying experiments and record the values of indexes we choose under different temperature and frying time. After collecting the data, we build a GAN-based regression model and train it on the dataset. Finally, we test our model on the test set and analyze the experimental results. Our results suggest that the proposed model can predict frying oil deterioration without experiments. Our model can be applied to other regression problems in various research areas, including price forecasting, trend analysis and so on. Deep frying ; Computer science ; Regression ; Regression analysis ; Artificial intelligence ; Machine learning ; Regression function ; Deep learning ; Statistics ; Mathematics ; Food science Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies