jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Corey Oses | Marco Esters | David Hicks | Simon Divilov | Hagen Eckert | Rico Friedrich | Michael J. Mehl | Andriy Smolyanyuk | Xiomara Campilongo | Axel van de Walle | Jan Schroers | A. Gilad Kusne | Ichiro Takeuchi | Eva Zurek | Marco Buongiorno Nardelli | Marco Fornari | Yoav Lederer | Ofir Levy | Cormac Toher | Stefano Curtarolo
Source: arXiv (Cornell University) (preprint, 2022-08-05)
Field: Materials Science — Materials Chemistry

aflow++: a C++ framework for autonomous materials design The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets - facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems Workflow ; Computer science ; Interoperability ; Software ; Consistency (knowledge bases) ; Key (lock) ; Systems engineering ; Software engineering ; Distributed computing ; Database ; Engineering ; Artificial intelligence Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalytic Processes in Materials Science
Authors: 英喜 森 | 雅彦 奥村 | 充洋 板倉
Source: Institutional Repositories DataBase (IRDB) (article, 2022-03-10)
Field: Materials Science — Materials Chemistry

Consideration of Transferability of Molten Iron of Machine Learning Potentials The inter-atomic potential based on artificial neural network (ANN) is very promising tool for atomic modeling. Using high quality training set constructed by First principles calculation based on Density functional theory (DFT), ANN potential would become sophisticated replica of DFT. However, the transferability of ANN potential depends on the DFT training dataset. In this study, we perform molecular dynamics (MD) calculations for molten iron, which is not directly included in the training data. We also examine the results to discuss the applicability of the constructed potential. Transferability ; Artificial neural network ; Set (abstract data type) ; Training (meteorology) ; Training set ; Quality (philosophy) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Physical and Chemical Molecular Interactions
Authors: Finki Dona Marleny | Nurhaeni Nurhaeni | Bayu Nugraha
Source: AIP conference proceedings (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Genetic algorithm optimization for image classification of coconut wood-based on GLCM Coconut wood is widely used as a building material in South Kalimantan. Coconut wood is processed into planks and used as walls for some of the Banjar people's houses. In this case, the quality of coconut wood needs to be considered. Image processing with pixel grouping techniques can be used to get an image of an object represented by class or material features such as coconut wood images. The method proposed in this research is Artificial Neural Networks with genetic algorithm optimization that applies feature extraction of Gray Level Co-Occurrence Matrix (GLCM). The first data used is a transverse digital image of coconut wood pieces which are then grouped based on their features using the GLCM method and classified using the ANN method optimized using a Genetic Algorithm. The accuracy value of using the proposed method is 75%. Genetic algorithm ; Artificial intelligence ; Computer science ; Artificial neural network ; Pixel ; Pattern recognition (psychology) ; Feature extraction ; Image (mathematics) ; Computer vision ; Machine learning Wood and Agarwood Research ; Agricultural and Environmental Management ; Advanced Image and Video Retrieval Techniques
Authors: Tammo van der Heide | Jolla Kullgren | Peter Broqvist | Vladimir Bačić | Thomas Frauenheim | Bálint Aradi
Source: arXiv (Cornell University) (preprint, 2022-02-07)
Field: Materials Science — Materials Chemistry

Fortnet, a software package for training Behler-Parrinello neural networks A new, open source, parallel, stand-alone software package (Fortnet) has been developed, which implements Behler-Parrinello neural networks. It covers the entire workflow from feature generation to the evaluation of generated potentials, coupled with higher-level analysis such as the analytic calculation of atomic forces. The functionality of the software package is demonstrated by driving the training for the fitted correction functions of the density functional tight binding (DFTB) method, which are commonly used to compensate the inaccuracies resulting from the DFTB approximations to the Kohn-Sham Hamiltonian. The usual two-body form of those correction functions limits the transferability of the parameterizations between very different structural environments. The recently introduced DFTB+ANN approach strives to lift these limitations by combining DFTB with a near-sighted artificial neural network (ANN). After investigating various approaches, we have found the combination of DFTB with an ANN acting on-top of some baseline correction functions (delta learning) the most promising one. It allowed to introduce many-body corrections on top of two-body parametrizations, while excellent transferability to chemical environments with deviating energetics could be demonstrated. Workflow ; Computer science ; Artificial neural network ; Transferability ; Software ; Lift (data mining) ; Software package ; Feature (linguistics) ; Artificial intelligence ; Hamiltonian (control theory) ; Deep neural networks ; Machine learning ; Algorithm ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Crystallography and molecular interactions ; Protein Structure and Dynamics
Authors: Blake Duschatko | Jonathan Vandermause | Nicola Molinari | Boris Kozinsky
Source: Research Square (preprint, 2022-11-18)
Field: Materials Science — Materials Chemistry

Uncertainty Driven Active Learning of Coarse Grained Free Energy Models Computer science ; Energy (signal processing) ; Econometrics ; Economics ; Statistics ; Mathematics Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Machine Learning and Algorithms
Authors: Guoqiang Wang | Yingtao Tao | Heng Sun
Source: Highlights in Science Engineering and Technology (article, 2022-12-07)
Field: Materials Science — Polymers and Plastics

Research on performance control of intercalated meltblown nonwovens based on neural network and grey relational analysis In this paper, aiming at the performance of intercalated meltblown nonwoven materials, the relationship between its process parameters, structural variables and product performance is explored, and the product performance can be optimized by changing the process parameters through regression, planning and other methods. The results of grey correlation analysis showed that the intercalation rate had a significant effect on both structural variables and product properties. According to the results of the multivariate nonlinear regression model, the ideal maximum cleaning efficiency can reach 99.7%. Finally, in order to explore when the influencing factors such as acceptance distance, hot air speed, thickness, and compression rebound rate are limited, to achieve the goal of achieving the highest filtering efficiency and the smallest filtering resistance as possible, the machine learning method is used, and random forest is selected as the regression model, build samples and make predictions, and finally get the optimal value of the acceptance distance of 21 cm and the hot air speed of 1580 r/min. Artificial neural network ; Grey relational analysis ; Product (mathematics) ; Multivariate statistics ; Process (computing) ; Compression (physics) ; Computer science ; Soft sensor ; Materials science ; Biological system ; Artificial intelligence ; Process engineering ; Mathematics ; Statistics ; Composite material ; Engineering Textile materials and evaluations ; Grey System Theory Applications
Authors: Blake Duschatko | Jonathan Vandermause | Nicola Molinari | Boris Kozinsky
Source: arXiv (Cornell University) (preprint, 2022-10-28)
Field: Materials Science — Materials Chemistry

Uncertainty Driven Active Learning of Coarse Grained Free Energy Models Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning approaches hold great promise to fitting complex many-body data. However, training models may require collection of large amounts of expensive data. Moreover, quantifying trained model accuracy is challenging, especially in cases of non-trivial free energy configurations, where training data may be sparse. We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces. Specifically, we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and open the possibility of adaptive transfer of models across different chemical systems. Uncertainties also characterize models' accuracy of free energy predictions, even when training is performed only on forces. This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models. Granularity ; Computer science ; Consistency (knowledge bases) ; Artificial intelligence ; Machine learning ; Energy (signal processing) ; Bayesian probability ; Bayesian inference ; Data collection ; Work (physics) ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Block Copolymer Self-Assembly
Authors: J.R. James | Nathan Pruyne | Tiberiu Stan | Marcus Schwarting | Jiwon Yeom | Seungbum Hong | Peter W. Voorhees | Ben Blaiszik | Ian Foster
Source: arXiv (Cornell University) (preprint, 2022-05-02)
Field: Materials Science — Materials Chemistry

3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as x-ray computed tomography (XCT) have provided new insights into dendritic phase transformation phenomena. However, manual identification of dendritic morphologies in microscopy data can be both labor intensive and potentially ambiguous. The analysis of 3D datasets is particularly challenging due to their large sizes (terabytes) and the presence of artifacts scattered within the imaged volumes. In this study, we trained 3D convolutional neural networks (CNNs) to segment 3D datasets. Three CNN architectures were investigated, including a new 3D version of FCDense. We show that using hyperparameter optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures can be trained to outperform the previous state of the art. The 3D U-Net architecture trained in this study produced the best segmentations according to quantitative metrics (pixel-wise accuracy of 99.84% and a boundary displacement error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and best segmentations according to visual inspection. The trained 3D CNNs are able to segment entire 852 x 852 x 250 voxel 3D volumes in only ~60 seconds, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification. Convolutional neural network ; Hyperparameter ; Computer science ; Artificial intelligence ; Segmentation ; Voxel ; Pattern recognition (psychology) ; Deep learning ; Speedup ; Transformation (genetics) ; Dendrite (mathematics) ; Pixel ; Computer vision ; Parallel computing ; Mathematics ; Chemistry Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Machine Learning in Materials Science
Authors: Sayam Singla | Sajid Mannan | Mohd Zaki | N. M. Anoop Krishnan
Source: arXiv (Cornell University) (preprint, 2022-11-01)
Field: Materials Science — Materials Chemistry

Accelerated Design of Chalcogenide Glasses through Interpretable Machine Learning for Composition Property Relationships Chalcogenide glasses possess several outstanding properties that enable several ground breaking applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite the ubiquitous usage of these glasses, the composition property relationships in these materials remain poorly understood. Here, we use a large experimental dataset comprising approx 24000 glass compositions made of 51 distinct elements from the periodic table to develop machine learning models for predicting 12 properties, namely, annealing point, bulk modulus, density, Vickers hardness, Littleton point, Youngs modulus, shear modulus, softening point, thermal expansion coefficient, glass transition temperature, liquidus temperature, and refractive index. These models, by far, are the largest for chalcogenide glasses. Further, we use SHAP, a game theory based algorithm, to interpret the output of machine learning algorithms by analyzing the contributions of each element towards the models prediction of a property. This provides a powerful tool for experimentalists to interpret the models prediction and hence design new glass compositions with targeted properties. Finally, using the models, we develop several glass selection charts that can potentially aid in the rational design of novel chalcogenide glasses for various applications. Chalcogenide ; Thermal expansion ; Chalcogenide glass ; Refractive index ; Materials science ; Shear modulus ; Softening point ; Glass transition ; Computer science ; Mineralogy ; Composite material ; Polymer ; Geology ; Optoelectronics Phase-change materials and chalcogenides ; Consumer Perception and Purchasing Behavior
Authors: Richard Ganser | Simon Bongarz | Alexander von Mach | Luis Azevedo Antunes | Alfred Kersch
Source: arXiv (Cornell University) (preprint, 2022-06-01)
Field: Materials Science — Materials Chemistry

Piezo- and pyroelectricity in Zirconia: a study with machine learned force fields The discovery of very large piezo- and pyroelectric effects in ZrO2 and HfO2-based thin films opens up new opportunities to develop silicon-compatible sensor and actor devices. The effects are amplified close to the polar-orthorhombic to tetragonal phase transition temperature. Molecular dynamics is the preferred technique to simulate such effects, though its application has to solve the dilemma between sufficient accuracy and sufficient efficiency of the interatomic force field. Here we present a deep neural network-based interatomic force field of ZrO2 learned from ab initio data using a systematic learning procedure in the Deep Potential framework. The model potential is verified to predict a variety of structural and dynamic properties with an accuracy comparable to density functional theory calculations. Then the Deep Potential model is used to reproduce the different thermal expansion and piezo and pyroelectric phenomena in ZrO2 with molecular dynamics calculations. At low temperature simulating the direct effect we find negative values for the piezo-and pyroelectric coefficients matching the ab initio calculations. Approaching the phase transition temperature these values remain negative and become large. Simulating the field induced effect above the phase transition temperature we find positive, giant piezo-electric coefficients matching the observations. The model is able to explain the large values and the sign of the experimental observations in relation to the polar-orthorhombic to tetragonal phase transition. The model furthermore explains the recently observed giant dielectric constant in a similiar system. Pyroelectricity ; Phase transition ; Orthorhombic crystal system ; Ab initio ; Tetragonal crystal system ; Materials science ; Condensed matter physics ; Force field (fiction) ; Dielectric ; Phase (matter) ; Physics ; Optoelectronics ; Optics ; Ferroelectricity ; Quantum mechanics Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Semiconductor materials and devices
Authors: Lei Ye | Lei Tong | Yali Bi | Yilun Wang | Xinyu Huang | Zhuiri Peng | Zheng Li | Langlang Xu | Runfeng Lin | Sihan Chen | WenHao Shi | Ming Tang | Hui Yu | Xinliang Zhang | Ping Wang | Xiang Shui Miao
Source: Research Square (preprint, 2022-04-26)
Field: Materials Science — Materials Chemistry

Ultrafast synergistic excitation for in-situ computing Abstract Nonlinear optical phenomena (NLOPs) in two-dimensional (2D) materials can be envisioned for neuromorphic functions at the device and related system level. But it has been attended rarely that transition among multi-energy states as one origin of NLOPs directly used for neuromorphic functions, which is assisted to understand nature of device-level nonlinear optical neuromorphic performance. Here we introduced a pump-probe-control technology to reveal multi-energy-state transition in multilayer molybdenum disulfide, enhancing nonlinear signals by transitions from two-photon absorption to synergistic excited states absorption and enabling an in-situ computing concept within an array of pure 2D flakes. Optical weighted average calculation and artificial neural network were realized without the fabrication of complex extrinsic structures, while preserving the femtosecond speed and femto-Joule power consumption, revealing the feasibility of pump-probe-control technology for nonlinear neuromorphic functions. Ultrashort pulse ; In situ ; Excitation ; Chemistry ; Computer science ; Physics ; Optics ; Organic chemistry ; Laser 2D Materials and Applications ; Perovskite Materials and Applications ; Neural Networks and Reservoir Computing
Authors: Tran Doan Huan | Vu Ngoc Tuoc
Source: arXiv (Cornell University) (preprint, 2022-11-07)
Field: Materials Science — Materials Chemistry

Machine-learning approach for discovery of conventional superconductors First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future discoveries if their reliability can be improved. The main challenge of current ML approaches, typically aiming at predicting the critical temperature $T_{\rm c}$ of a solid from its chemical composition and target pressure, is that the correlations to be learned are deeply hidden, indirect, and uncertain. In this work, we showed that predicting superconductivity at any pressure from the atomic structure is sustainable and reliable. For a demonstration, we curated a diverse dataset of 584 atomic structures for which $λ$ and $ω_{\log}$, two parameters of the electron-phonon interactions, were computed. We then trained some ML models to predict $λ$ and $ω_{\log}$, from which $T_{\rm c}$ can be computed in a post-processing manner. The models were validated and used to identify two possible superconductors whose $T_{\rm c}\simeq 10-15$K at zero pressure. Interestingly, these materials have been synthesized and studied in some other contexts. In summary, the proposed ML approach enables a pathway to directly transfer what can be learned from the high-pressure atomic-level details that correlate with high-$T_{\rm c}$ superconductivity to zero pressure. Going forward, this strategy will be improved to better contribute to the discoveries of new superconductors. Superconductivity ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Condensed matter physics Machine Learning in Materials Science
Authors: Márcio S. Gomes-Filho | A. Martínez Torres | Alexandre Reily Rocha | Luana S. Pedroza
Source: arXiv (Cornell University) (preprint, 2022-09-08)
Field: Materials Science — Materials Chemistry

Size and Quality of Quantum Mechanical Data Sets for Training Neural Network Force Fields for Liquid Water Molecular dynamics simulations have been used in different scientific fields to investigate a broad range of physical systems. However, the accuracy of calculation is based on the model considered to describe the atomic interactions. In particular, ab initio molecular dynamics (AIMD) has the accuracy of density functional theory (DFT), and thus is limited to small systems and relatively short simulation time. In this scenario, Neural Network Force Fields (NNFF) have an important role, since it provides a way to circumvent these caveats. In this work we investigate NNFF designed at the level of DFT to describe liquid water, focusing on the size and quality of the training data-set considered. We show that structural properties are less dependent on the size of the training data-set compared to dynamical ones (such as the diffusion coefficient), and a good sampling (selecting data reference for training process) can lead to a small sample with good precision. Artificial neural network ; Computer science ; Molecular dynamics ; Quality (philosophy) ; Work (physics) ; Set (abstract data type) ; Force field (fiction) ; Experimental data ; Sampling (signal processing) ; Process (computing) ; Quantum ; Data set ; Training (meteorology) ; Field (mathematics) ; Sample (material) ; Sample size determination ; Diffusion ; Statistical physics ; Artificial intelligence ; Physics ; Mathematics ; Computational chemistry ; Chemistry ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies