jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chao‐Ching Chiang | T. -H. Lee
Source: Scientific Reports (article, 2019-09-02)
Field: Materials Science — Materials Chemistry

Annihilating Pores in the Desired Layer of a Porous Silicon Bilayer with Different Porosities for Layer Transfer A silicon layer that is tens of micrometers thick on a handle substrate is desired for applications involving power devices, microelectromechanical systems (MEMS), highly efficient silicon solar cells (<50 µm), etc. In general, if the initial silicon layer obtained from the layer transfer process using the etch-stop or ion-cut techniques, which may provide very accurate thickness control, is too thin, then additional epitaxial growth is required to increase the thickness of the silicon layer. However, epitaxial growth under strict predeposition conditions is a time-consuming and expensive process. On the other hand, producing porous silicon via anodization in a hydrofluoric acid solution offers an efficient way to control the dimensions of the generated pores directly on the nano- or macroscale via the current density. When sintering the porous layer via high-temperature argon annealing, the porosity of the porous layer determines whether this porous layer can serve as a device layer or a separation layer. In addition, it is clearly easier to create a transferred layer ten of micrometers thick via anodization than by ion implantation and/or epitaxial deposition. Materials science ; Layer (electronics) ; Silicon ; Wafer ; Porous silicon ; Epitaxy ; Optoelectronics ; Bilayer ; Substrate (aquarium) ; Anodizing ; Transfer printing ; Hydrofluoric acid ; Nanotechnology ; Composite material ; Chemistry ; Metallurgy Silicon Nanostructures and Photoluminescence ; Nanowire Synthesis and Applications ; Semiconductor materials and devices
Authors: Gongrui Guo | Ping Zhu | Martin R. Fuchs | Wuxian Shi | Babak Andi | Yuan Gao | Wayne A. Hendrickson | Seán McSweeney | Qun Liu
Source: IUCrJ (article, 2019-05-03)
Field: Materials Science — Materials Chemistry

Synchrotron microcrystal native-SAD phasing at a low energy <i>De novo</i> structural evaluation of native biomolecules from single-wavelength anomalous diffraction (SAD) is a challenge because of the weakness of the anomalous scattering. The anomalous scattering from relevant native elements - primarily sulfur in proteins and phospho-rus in nucleic acids - increases as the X-ray energy decreases toward their <i>K</i>-edge transitions. Thus, measurements at a lowered X-ray energy are promising for making native SAD routine and robust. For microcrystals with sizes less than 10 µm, native-SAD phasing at synchrotron microdiffraction beamlines is even more challenging because of difficulties in sample manipulation, diffraction data collection and data analysis. Native-SAD analysis from microcrystals by using X-ray free-electron lasers has been demonstrated but has required use of thousands of thousands of microcrystals to achieve the necessary accuracy. Here it is shown that by exploitation of anomalous microdiffraction signals obtained at 5 keV, by the use of polyimide wellmounts, and by an iterative crystal and frame-rejection method, microcrystal native-SAD phasing is possible from as few as about 1 200 crystals. Our results show the utility of low-energy native-SAD phasing with microcrystals at synchrotron microdiffraction beamlines. Synchrotron ; Computer science ; Low energy ; Crystallography ; Chemistry ; Physics ; Materials science ; World Wide Web ; Atomic physics ; Optics Enzyme Structure and Function ; Biochemical and Molecular Research ; Porphyrin Metabolism and Disorders
Authors: Marcin M. Wysokiński
Source: Scientific Reports (article, 2019-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mechanism for transitions between ferromagnetic and antiferromagnetic orders in d-electron metallic magnets We propose mechanism for pressure-induced transitions between ferromagnetic and antiferromagnetic phases that relies on a competition between characteristic energy scales ubiquitous among d-electron metallic magnetic compounds. Principles behind the mechanism are demonstrated on the example of the minimal two-orbital p-d lattice model. We suggest that LaCrGe<sub>3</sub>, where pressure-induced ferromagnetic-to-antiferromagnetic phase transition has been recently observed, is a promising candidate to realize discussed mechanism. Antiferromagnetism ; Ferromagnetism ; Mechanism (biology) ; Magnet ; Lattice (music) ; Phase transition Iron-based superconductors research ; Rare-earth and actinide compounds ; Magnetic and transport properties of perovskites and related materials
Authors: Adarsh Dave | Jared Mitchell | Kirthevasan Kandasamy | Sven Burke | Biswajit Paria | Barnabás Póczos | Jay Whitacre | Venkatasubramanian Viswanathan
Source: ChemRxiv (preprint, 2019-11-20)
Field: Materials Science — Materials Chemistry

Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine-Learning Innovations in batteries take years to formulate, requiring extensive experimentation during the design and optimization phases. We approach the design of a battery electrolyte as a black-box optimization problem. We report here the discovery of a novel battery electrolyte by a robotic electrolyte experiment guided by machine-learning software. Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries, we utilize Dragonfly - a Bayesian machine-learning software package - to search mixtures of commonly used lithium and sodium salts for super-concentrated aqueous electrolytes with wide electrochemical stability windows. Dragonfly autonomously managed the robotic test-stand, recommending electrolyte designs to test and receiving experimental feed- back in real time. Within 40 hours of continuous experimentation, Dragonfly discovered a novel, high-performing aqueous sodium electrolyte that a human-guided design process may have missed. This result demonstrates the possibility of integrating robotics with machine-learning to rapidly and autonomously discover novel battery materials. Electrolyte ; Battery (electricity) ; Computer science ; Robotics ; Artificial intelligence ; Software ; Robot ; Chemistry ; Operating system Machine Learning in Materials Science ; Machine Learning and Algorithms ; Fuel Cells and Related Materials
Authors: Xin Chen | Xingyu Gao | Ya‐Fan Zhao | De-Ye Lin | Weidong Chu | Haifeng Song
Source: Computer Physics Communications (article, 2019-12-05)
Field: Materials Science — Materials Chemistry

TensorAlloy: An automatic atomistic neural network program for alloys Artificial neural network ; Python (programming language) ; Computer science ; Computation ; Virial theorem ; Atom (system on chip) ; Artificial intelligence ; Virial coefficient ; Statistical physics ; Computational science ; Machine learning ; Algorithm ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Zijiang Yang | Reda Al-Bahrani | Andrew Reid | Stefanos Papanikolaou | Surya R. Kalidindi | Wei‐keng Liao | Alok Choudhary | Ankit Agrawal
Source:  (article, 2019-07-01)
Field: Materials Science — Materials Chemistry

Deep learning based domain knowledge integration for small datasets: Illustrative applications in materials informatics Deep learning has shown its superiority to traditional machine learning methods in various fields, and in general, its success depends on the availability of large amounts of reliable data. However, in some scientific fields such as materials science, such big data is often expensive or even impossible to collect. Thus given relatively small datasets, most of data-driven methods are based on traditional machine learning methods, and it is challenging to apply deep learning for many tasks in these fields. In order to take the advantage of deep learning even for small datasets, a domain knowledge integration approach is proposed in this work. The efficacy of the proposed approach is tested on two materials science datasets with different types of inputs and outputs, for which domain knowledge-aware convolutional neural networks (CNNs) are developed and evaluated against traditional machine learning methods and standard CNN-based approaches. Experiment results demonstrate that integrating domain knowledge into deep learning can not only improve the model's performance for small datasets, but also make the prediction results more explainable based on domain knowledge. Computer science ; Deep learning ; Artificial intelligence ; Machine learning ; Convolutional neural network ; Domain knowledge ; Domain (mathematical analysis) ; Big data ; Field (mathematics) ; Informatics ; Data science ; Data mining Machine Learning in Materials Science ; Neural Networks and Applications ; Topic Modeling
Authors: Ryo Nagai | Ryosuke Akashi | Osamu Sugino
Source: arXiv (Cornell University) (preprint, 2019-03-01)
Field: Materials Science — Materials Chemistry

Completing density functional theory by machine-learning hidden messages from molecules Kohn-Sham density functional theory is the base of modern computational approaches to electronic structures. Their accuracy vitally relies on the exchange-correlation energy functional, which encapsulates electron-electron interaction beyond the classical one. The functional provides a way to obtain the density and energy without solving the many-body equation and can, in principle, be determined to reproduce the exact ones universally. However, the past approaches are dependent on the theoretical development, which limits the possibility of the functional to human's intuition. Here, we demonstrate a systematic way to machine-learn a functional from a database, without complicated assumptions. The density and energy are related with a flexible feed-forward neural network, which is trained to reproduce accurate dataset, and the KS-DFT is solved by taking the functional derivatives with the back-propagation technique. Surprisingly, a trial functional, trained for just a few molecules, has been shown to be applicable to hundreds of molecular systems with comparable accuracy to the standard functionals. Also, by adding the nodes connected to the hidden layers, a non-local term is straightforwardly included to improve accuracy, which has been hitherto impractically difficult. Utilizing the strategy of rapidly advancing machine learning techniques, this novel approach is expected to enrich the DFT framework by constructing a functional just from a database for materials conventionally difficult to calculate accurately. Density functional theory ; Computer science ; Intuition ; Energy functional ; Time-dependent density functional theory ; Artificial intelligence ; Hybrid functional ; Artificial neural network ; Machine learning ; Theoretical computer science ; Physics ; Quantum mechanics ; Cognitive science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Ebrahim Balouji | Thomas Hammarström | Tomas McKelvey
Source:  (article, 2019-11-01)
Field: Materials Science — Materials Chemistry

Partial Discharge Classification in Power Electronics Applications using Machine Learning A study of machine learning (ML) methods for classification of data from partial discharges (PDs) is described. A novel set of features are suggested and tested using an extensive set of machine learning based algorithms. The aim is to classify PDs occurring within insulation systems and power electronics devices (PED). Due to the increased use of pulse width modulation waveform (PWM) in PEDs, an increased insulation degradation has been observed due to a more intense PD exposure. This study aims to develop suitable tools to detect types of defects to facilitate diagnostics as well as to improve isolation system design. To evaluate the performance of ML based classification, several algorithms have been developed to detect and classify PDs from different kind of material defects with the aim to address the reason behind the appearance of partial discharges. Experiments with different PD source locations and volume of the defect and voltage rise time were investigated on an artificial cavity test object. Relevant signal features found important are for example the maximum magnitude, duration, the distance from polarity shift, the time distance between PDs and the absolute value of the area of the detected PD waveform. It has been observed that forming such PD features based on their time occurrence results in an accurate and generalized solution. With these features the best results were achieved with the deep learning LSTM architecture reaching a test accuracy of 98.3%. For industry applications, feature engineering is useful to reduce amount of data necessary to be analyzed by the neural network or ML algorithm. Partial discharge ; Waveform ; Computer science ; Artificial intelligence ; Electronics ; Artificial neural network ; Pulse-width modulation ; Feature extraction ; SIGNAL (programming language) ; Power (physics) ; Pattern recognition (psychology) ; Feature (linguistics) ; Volume (thermodynamics) ; Backpropagation ; Set (abstract data type) ; Fault (geology) ; Voltage ; Machine learning ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Julian Lißner | Felix Fritzen
Source: Mathematical and Computational Applications (article, 2019-05-31)
Field: Materials Science — Materials Chemistry

Data-Driven Microstructure Property Relations An image based prediction of the effective heat conductivity for highly heterogeneous microstructured materials is presented. The synthetic materials under consideration show different inclusion morphology, orientation, volume fraction and topology. The prediction of the effective property is made exclusively based on image data with the main emphasis being put on the 2-point spatial correlation function. This task is implemented using both unsupervised and supervised machine learning methods. First, a snapshot proper orthogonal decomposition (POD) is used to analyze big sets of random microstructures and, thereafter, to compress significant characteristics of the microstructure into a low-dimensional feature vector. In order to manage the related amount of data and computations, three different incremental snapshot POD methods are proposed. In the second step, the obtained feature vector is used to predict the effective material property by using feed forward neural networks. Numerical examples regarding the incremental basis identification and the prediction accuracy of the approach are presented. A Python code illustrating the application of the surrogate is freely available. Snapshot (computer storage) ; Computer science ; Python (programming language) ; Computation ; Algorithm ; Proper orthogonal decomposition ; Feature vector ; Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Data mining ; Point of delivery Machine Learning in Materials Science ; Composite Material Mechanics ; Topology Optimization in Engineering
Authors: Pengfei Chen | Weiwen Liu | Chang‐Yu Hsieh | Guangyong Chen | Shengyu Zhang
Source: arXiv (Cornell University) (preprint, 2019-06-13)
Field: Materials Science — Materials Chemistry

Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization Graph Neural Networks (GNNs) achieve an impressive performance on structured graphs by recursively updating the representation vector of each node based on its neighbors, during which parameterized transformation matrices should be learned for the node feature updating. However, existing propagation schemes are far from being optimal since they do not fully utilize the relational information between nodes. We propose the information maximizing graph neural networks (IGNN), which maximizes the mutual information between edge states and transform parameters. We reformulate the mutual information as a differentiable objective via a variational approach. We compare our model against several recent variants of GNNs and show that our model achieves the state-of-the-art performance on multiple tasks including quantum chemistry regression on QM9 dataset, generalization capability from QM9 to larger molecular graphs, and prediction of molecular bioactivities relevant for drug discovery. The IGNN model is based on an elegant and fundamental idea in information theory as explained in the main text, and it could be easily generalized beyond the contexts of molecular graphs considered in this work. To encourage more future work in this area, all datasets and codes used in this paper will be released for public access. Computer science ; Parameterized complexity ; Theoretical computer science ; Maximization ; Mutual information ; Differentiable function ; Artificial neural network ; Graph ; Enhanced Data Rates for GSM Evolution ; Generalization ; Artificial intelligence ; Algorithm ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Tarak K Patra | Troy D. Loeffler | Henry Chan | Mathew J. Cherukara | Badri Narayanan | Subramanian K. R. S. Sankaranarayanan
Source: Applied Physics Letters (article, 2019-11-04)
Field: Materials Science — Materials Chemistry

A coarse-grained deep neural network model for liquid water We introduce a coarse-grained deep neural network (CG-DNN) model for liquid water that utilizes 50 rotational and translational invariant coordinates and is trained exclusively against energies of ∼30 000 bulk water configurations. Our CG-DNN potential accurately predicts both the energies and the molecular forces of water, within 0.9 meV/molecule and 54 meV/Å of a reference (coarse-grained bond-order potential) model. The CG-DNN water model also provides good prediction of several structural, thermodynamic, and temperature dependent properties of liquid water, with values close to those obtained from the reference model. More importantly, CG-DNN captures the well-known density anomaly of liquid water observed in experiments. Our work lays the groundwork for a scheme where existing empirical water models can be utilized to develop a fully flexible neural network framework that can subsequently be trained against sparse data from high-fidelity albeit expensive beyond-DFT calculations. Liquid water ; Artificial neural network ; Work (physics) ; Water model ; Invariant (physics) ; Experimental data ; Empirical modelling Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Thomas A. Holme
Source: Journal of Chemical Education (article, 2019-04-09)
Field: Chemistry — Physical and Theoretical Chemistry

Can Today’s Chemistry Curriculum Actually Produce Tomorrow’s Adaptable Chemist? Chemistry as a profession has seemed to avoid the kind of large-scale disruption that has visited other professions in the 21st century. While the probability that jobs for chemists will be filled by robots or machine-learning computers may be modest, the arrival of these tools will undoubtedly affect the workplace that students who graduate from chemistry programs encounter when they leave school. To what extent does the current curriculum help these students leverage the opportunities presented by these emerging technological trends, and to what extent will their education leave them wanting? The time for some critical self-analysis of our curricular biases and choices is perhaps overdue. Chemist ; Curriculum ; Leverage (statistics) ; Engineering ethics ; Chemistry education ; Chemistry ; Engineering ; Computer science ; Psychology ; Pedagogy ; Artificial intelligence Various Chemistry Research Topics ; Biomedical and Engineering Education ; Health and Medical Research Impacts
Authors: Meng Lei | Zhongyu Rao | Ming Li | Xinhui Yu | Liang Zou
Source: Applied Sciences (article, 2019-03-15)
Field: Chemistry — Analytical Chemistry

Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems. Coal ; Near-infrared spectroscopy ; Particle swarm optimization ; Computer science ; Identification (biology) ; Pattern recognition (psychology) ; Data mining ; Artificial intelligence ; Biological system ; Process engineering ; Machine learning ; Engineering ; Physics ; Ecology ; Biology ; Waste management Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Kai Guo | Markus J. Buehler
Source: Matter (article, 2019-08-01)
Field: Materials Science — Biomaterials

Nature’s Way: Hierarchical Strengthening through Weakness Nanotechnology ; Brittleness ; Materials science ; Nanocomposite ; Toughening ; Polymer science ; Toughness ; Computer science ; Composite material Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Graphene and Nanomaterials Applications