jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fleur Legrain | Ambroise van Roekeghem | Stefano Curtarolo | Jesús Carrete | Georg K. H. Madsen | Natalio Mingo
Source: Journal of Chemical Information and Modeling (preprint, 2018-10-10)
Field: Materials Science — Materials Chemistry

Vibrational Properties of Metastable Polymorph Structures by Machine Learning Despite vibrational properties being critical for the ab initio prediction of finite-temperature stability as well as thermal conductivity and other transport properties of solids, their inclusion in ab initio materials repositories has been hindered by expensive computational requirements. Here we tackle the challenge, by showing that a good estimation of force constants and vibrational properties can be quickly achieved from the knowledge of atomic equilibrium positions using machine learning. A random-forest algorithm trained on 121 different mechanically stable structures of KZnF<sub>3</sub> reaches a mean absolute error of 0.17 eV/Å<sup>2</sup> for the interatomic force constants, and it is less expensive than training the complete force field for such compounds. The predicted force constants are then used to estimate phonon spectral features, heat capacities, vibrational entropies, and vibrational free energies, which compare well with the ab initio ones. The approach can be used for the rapid estimation of stability at finite temperatures. Metastability ; Ab initio ; Force field (fiction) ; Stability (learning theory) ; Force constant ; Phonon ; Ab initio quantum chemistry methods ; Materials science ; Computational chemistry ; Statistical physics ; Thermodynamics ; Molecular physics ; Molecule ; Chemistry ; Physics ; Computer science ; Machine learning ; Condensed matter physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Inorganic Fluorides and Related Compounds ; Inorganic Chemistry and Materials
Authors: Yusei Miura | Tetsuya Sakurai | Claus Aranha | Toshiya Senda | Ryuichi Kato | Yusuke Yamada
Source: arXiv (Cornell University) (preprint, 2018-12-25)
Field: Materials Science — Materials Chemistry

Classification of X-Ray Protein Crystallization Using Deep Convolutional Neural Networks with a Finder Module Recently, deep convolutional neural networks have shown good results for image recognition. In this paper, we use convolutional neural networks with a finder module, which discovers the important region for recognition and extracts that region. We propose applying our method to the recognition of protein crystals for X-ray structural analysis. In this analysis, it is necessary to recognize states of protein crystallization from a large number of images. There are several methods that realize protein crystallization recognition by using convolutional neural networks. In each method, large-scale data sets are required to recognize with high accuracy. In our data set, the number of images is not good enough for training CNN. The amount of data for CNN is a serious issue in various fields. Our method realizes high accuracy recognition with few images by discovering the region where the crystallization drop exists. We compared our crystallization image recognition method with a high precision method using Inception-V3. We demonstrate that our method is effective for crystallization images using several experiments. Our method gained the AUC value that is about 5% higher than the compared method. Convolutional neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Crystallization ; Image (mathematics) ; Data set ; Deep learning ; Artificial neural network ; Set (abstract data type) ; Protein crystallization ; Engineering Enzyme Structure and Function ; Cell Image Analysis Techniques ; Machine Learning in Materials Science
Authors: Yi Zhang | Andrej Mesaroš | K. Fujita | Stephen D. Edkins | Mohammad Hamidian | Kelvin Ch’ng | H. Eisaki | S. Uchida | J. C. Davis | Ehsan Khatami | Eun-Ah Kim
Source: arXiv (Cornell University) (preprint, 2018-08-01)
Field: Materials Science — Materials Chemistry

Using Machine Learning for Scientific Discovery in Electronic Quantum Matter Visualization Experiments Essentials of the scientific discovery process have remained largely unchanged for millennia: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established scientific theory. Today, however, we face major scientific challenges because automated scientific instrumentation and large-scale data acquisition are generating data sets of such volume and complexity as to defy human analysis. To achieve scientific discovery using these enormous empirical data sets will require new approaches. A good example is modern visualization studies of electronic quantum matter (EQM) which yield dense arrays of electronic structure images that are often astonishingly complex at the atomic-scale. Given the recent advances in machine learning (ML) studies of synthetic EQM data, the outstanding challenge now is to engage ML with real EQM data. Here we report development and training of an array of artificial neural networks (ANN) designed to recognize different types of hypothesized order hidden in EQM image-arrays. The ANNs are used to analyze a large, experimentally-derived EQM image archive, spanning a wide range of electron densities and energies, in carrier-doped cuprate Mott insulators. Remarkably, this ML system repeatedly and reliably discovers, throughout all these noisy and complex data, the features of a very specific ordered state of EQM. It discerns a lattice-commensurate, precisely four-unit-cell periodic, unidirectional, translational-symmetry-breaking state, consistent with those long predicted within strong-coupling theories of electronic liquid crystals. This marks a breakthrough in the capability of ML to achieve physics discovery and suggests broad new avenues for the scientific method. Visualization ; Artificial intelligence ; Computer science ; Mott insulator ; Artificial neural network ; Data science ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Electronic and Structural Properties of Oxides
Authors: Xiaoniu Li | Jingying Zhao | Hai Guo
Source: International Journal of Materials and Product Technology (article, 2018-11-26)
Field: Materials Science — Materials Chemistry

A predictive modelling of nanocomposite coating microhardness based on extremely randomised trees Nanocomposite coating is a coating made of particles whose sizes are of nanoscale. The microhardness of the coating is an importance parameter. Currently, experimental method is mainly adopted in the coating's microhardness and performance research, with high research cost and long time period. In this paper, the content of the nano-particles in the plating liquid, current density, duty ratio, addition of additives and ultrasonic power are set as inputs; the micro hardness of the nanocomposite coating is set as output. Extremely randomised trees (ERT) are used to establish a strong prediction model. The prediction performance is the ERT model is superior to that of the single models such as linear regression, back-propagation neural network and radial basis function neural network, etc. and other ensemble learning methods. ERT model can be used for predicting the microhardness of nanocomposite coating, providing an efficient and highly reliable method for new material performance prediction. Indentation hardness ; Coating ; Materials science ; Nanocomposite ; Composite material ; Artificial neural network ; Microstructure ; Computer science ; Machine learning Corrosion Behavior and Inhibition
Authors: M. Manzoor Hussain | V. Pitchi Raju | J. Kandasamy | D. Govardhan
Source: IOP Conference Series Materials Science and Engineering (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Prediction Of Tensile And Shear Strength Of Friction Surfaced Tool Steel Deposit By Using Artificial Neural Networks Friction surface treatment is well-established solid technology and is used for deposition, abrasion and corrosion protection coatings on rigid materials. This novel process has wide range of industrial applications, particularly in the field of reclamation and repair of damaged and worn engineering components. In this paper, we present the prediction of tensile and shear strength of friction surface treated tool steel using ANN for simulated results of friction surface treatment. This experiment was carried out to obtain tool steel coatings of low carbon steel parts by changing contribution process parameters essentially friction pressure, rotational speed and welding speed. The simulation is performed by a 33-factor design that takes into account the maximum and least limits of the experimental work performed with the 23-factor design. Neural network structures, such as the Feed Forward Neural Network (FFNN), were used to predict tensile and shear strength of tool steel sediments caused by friction. Materials science ; Ultimate tensile strength ; Abrasion (mechanical) ; Artificial neural network ; Shear (geology) ; Welding ; Corrosion ; Rotational speed ; Metallurgy ; Structural engineering ; Composite material ; Mechanical engineering ; Engineering ; Computer science Metallurgy and Material Science ; Metal Alloys Wear and Properties ; Aluminum Alloy Microstructure Properties
Authors: Jian Yu | Jun Li | Yinglong Jiang | Junhao Chu
Source:  (article, 2018-05-01)
Field: Materials Science — Materials Chemistry

Data-Mining Driven Design for Novel Perovskite-type Piezoceramics Materials Genome Initiative is envisioning the discovery, development, manufacturing and deployment of advanced materials twice as fast and at a fraction of cost. High throughput computation and experimentation will generate big data, which underscores the emergence of the fourth paradigm data science. In contrast to machine-learning needing big-data, data-mining assisted by domain knowledge and expertise works well with a limited number of data. In this presentation, data-mining based on material genome approach were performed in field of perovskite-type oxides. New ferroelectric ceramics based on BiFeO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> for high temperature piezoelectric applications are realized with piezoresponse of 1.5 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">~</sub> 4.0 times the present commercial non-perovskite counterpart. Our essay demonstrates data-mining driven searching sure able to reduce time-to-insight and human effort on synthesization, accelerating new materials discovery and deployment. Software deployment ; Big data ; Computer science ; Domain (mathematical analysis) ; Perovskite (structure) ; Computation ; Field (mathematics) ; Data-driven ; Artificial intelligence ; Data mining ; Engineering ; Algorithm ; Operating system Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies
Authors: Javad Sayyad Amin | Sohrab Zendehboudi | Esmat Mohamadi
Source: Environmental Progress & Sustainable Energy (article, 2018-03-02)
Field: Chemistry — Analytical Chemistry

Evolution of tar ball aggregates in Caspian Sea: Implications of connectionist tools linked with image analysis Intensive petroleum activities in the Caspian Sea lead to a considerable oil pollution in the various parts of this region. Tar balls stranded on the south coast of the Caspian Sea evidently cause (and accelerate) this serious environmental threat. In this study, a poloxamine copolymeric surfactant is used to agglomerate tar balls in a system containing the solvent, antisolvent, and Caspian Sea tar balls. This research work is conducted to determine the cumulative size distribution of tar balls in the presence of a poloxamine copolymer. The evolution of tar ball aggregates is experimentally and theoretically investigated and the steady‐state size distribution of the tar balls is estimated by Back Propagation Artificial Neural Network (ANN‐BP) and a new evolutionary algorithm, called Imperialist Competitive Algorithm (ICA). The average size of tar ball aggregates increases with time and reaches a maximum value. It then declines and eventually approaches a steady‐state condition so that an almost constant size for the particles is attained. The statistical analysis shows a good agreement between the predicted values (obtained by the neural network combined with ICA, known as ANN‐ICA) and the real data for the steady‐state size distribution of the aggregates. This research study can considerably help to further understand evolution of tar balls (that hit the beaches) and consequently to find effective and economical ways for tar balls removal from the beaches. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1901–1907, 2018 Agglomerate ; tar (computing) ; Ball (mathematics) ; Artificial neural network ; Environmental science ; Biological system ; Computer science ; Materials science ; Pulp and paper industry ; Mathematics ; Artificial intelligence ; Engineering ; Composite material ; Biology ; Geometry Petroleum Processing and Analysis ; Global Energy and Sustainability Research ; Enhanced Oil Recovery Techniques
Authors: Hirotaka Akita | Kosuke Nakago | Tomoki Komatsu | Yohei Sugawara | Shin‐ichi Maeda | Yukino Baba | Hisashi Kashima
Source: arXiv (Cornell University) (preprint, 2018-07-04)
Field: Materials Science — Materials Chemistry

BayesGrad: Explaining Predictions of Graph Convolutional Networks Recent advances in graph convolutional networks have significantly improved the performance of chemical predictions, raising a new research question: "how do we explain the predictions of graph convolutional networks?" A possible approach to answer this question is to visualize evidence substructures responsible for the predictions. For chemical property prediction tasks, the sample size of the training data is often small and/or a label imbalance problem occurs, where a few samples belong to a single class and the majority of samples belong to the other classes. This can lead to uncertainty related to the learned parameters of the machine learning model. To address this uncertainty, we propose BayesGrad, utilizing the Bayesian predictive distribution, to define the importance of each node in an input graph, which is computed efficiently using the dropout technique. We demonstrate that BayesGrad successfully visualizes the substructures responsible for the label prediction in the artificial experiment, even when the sample size is small. Furthermore, we use a real dataset to evaluate the effectiveness of the visualization. The basic idea of BayesGrad is not limited to graph-structured data and can be applied to other data types. Computer science ; Graph ; Machine learning ; Artificial intelligence ; Dropout (neural networks) ; Property (philosophy) ; Bayesian probability ; Bayesian network ; Visualization ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Shanlin Rao | Gianni Klesse | Phillip J. Stansfeld | Stephen J. Tucker | Mark S.P. Sansom
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-12-16)
Field: Chemistry — Spectroscopy

A Heuristic Derived from Analysis of the Ion Channel Structural Proteome Permits the Rapid Identification of Hydrophobic Gates Abstract Ion channel proteins control ionic flux across biological membranes through conformational changes in their transmembrane pores. An exponentially increasing number of channel structures captured in different conformational states are now being determined. However, these newly-resolved structures are commonly classified as either open or closed based solely on the physical dimensions of their pore and it is now known that more accurate annotation of their conductive state requires an additional assessment of the effect of pore hydrophobicity. A narrow hydrophobic gate region may disfavour liquid-phase water, leading to local de-wetting which will form an energetic barrier to water and ion permeation without steric occlusion of the pore. Here we quantify the combined influence of radius and hydrophobicity on pore de-wetting by applying molecular dynamics simulations and machine learning to nearly 200 ion channel structures. This allows us to propose a simple simulation-free heuristic model that rapidly and accurately predicts the presence of hydrophobic gates. This not only enables the functional annotation of new channel structures as soon as they are determined, but may also facilitate the design of novel nanopores controlled by hydrophobic gates. Significance statement Ion channels are nanoscale protein pores in cell membranes. An exponentially increasing number of structures for channels means that computational methods for predicting their functional state are needed. Hydrophobic gates in ion channels result in local de-wetting of pores which functionally closes them to water and ion permeation. We use simulations of water behaviour within nearly 200 different ion channel structures to explore how the radius and hydrophobicity of pores determine their hydration vs. de-wetting behaviour. Machine learning-assisted analysis of these simulations enables us to propose a simple model for this relationship. This allows us to present an easy method for the rapid prediction of the functional state of new channel structures as they emerge. Wetting ; Ion channel ; Chemistry ; Membrane ; Ion ; Nanopore ; Chemical physics ; Nanotechnology ; Materials science ; Biological system ; Organic chemistry Mass Spectrometry Techniques and Applications ; Nanopore and Nanochannel Transport Studies ; Lipid Membrane Structure and Behavior
Authors: Andrea Pedrini | Marco Piastra | Epifanio G. Virga
Source: Soft Matter (article, 2018-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Non-monotonic, lily-like twist distribution in toroidal nematics Toroidal nematics are droplets of nematic liquid crystals in the form of a circular torus. When the nematic director is subject to planar degenerate boundary conditions, the bend-only director field with vector lines along the parallels of all nested torodial shells is an equilibrium solution for all values of the elastic constants. Local stability analyses have shown that an instability is expected to occur for sufficiently small values of the twist elastic constant. It is natural to wonder whether in this regime the global equilibrium would be characterized by a monotonic twist, or not. In the former case, the twist distribution over the torus' circular cross-section would be represented pictorially by a fennel-like surface emanating from the centre. We prove that instead the stable twist distribution is represented by a lily-like surface. Thus, generically the twist distribution is not monotonic and its maximum may fall well within the torus, far away from the boundary. To cope with the peculiar complexity of the elastic free-energy functional in the fully non-linear setting, we developed an ad hoc deep-learning optimization method, which here is also further validated and documented for it promises to be applicable to other similar problems, equally intractable analytically. Toroid ; Twist ; Liquid crystal ; Monotonic function ; Distribution (mathematics) ; Cross section (physics) ; Physics ; Materials science ; Condensed matter physics ; Mechanics ; Geometry ; Mathematics ; Nuclear physics ; Mathematical analysis ; Plasma Liquid Crystal Research Advancements ; Orbital Angular Momentum in Optics ; Photonic Crystals and Applications
Authors: Satoshi Shimokawa | Hirotaka Oshima | Koichi Shimizu | Y. Uehara | Jun Fujisaki | Atsushi Furuya | Hajime Igarashi
Source: 2018 IEEE International Magnetics Conference (INTERMAG) (article, 2018-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Fast Three-dimensional Optimization of Magnetic Cores for Loss and Volume Reduction Power electronic devices have been widely applied such as in consumer devices and electrical vehicles, and improvement of their efficiency is strongly required. One of the main factors of energy loss in the devices is the magnetic core loss in inductors and transformers. Therefore, simulation technology that realizes accurate energy loss estimation based on magnetic characteristics of the materials has been highly demanded. For this reason, we have been developing large-scale magnetic field simulation technology by combining micromagnetic models and the finite element method (FEM) [1]. One the other hand, structural design optimization for devices with complicated shapes requires huge man-hours, and it is difficult to judge whether the designed shape is sufficiently optimized. Genetic algorithm (GA) is widely used in various optimal design problems for its excellent global search capabilities. However, GA generally requires large numbers of simulations, and it takes a long time to search the optimal solution. To solve this problem, neural networks (NN) are applied as surrogate models instead of physics models to reduce computing time in some previous work [2]. In this work, we perform shape parameter optimization of an EI-shaped inductor using GA and NN surrogate models designed from magnetic simulation results. Magnetic loss and volume of the cores are chosen to be objective functions. Few previous works have been reported on optimization studies with magnetic core loss as an objective function [3]. Here we solve multi-objective optimization problems, and Pareto optimal fronts for various inductance values are obtained. We also show that experiment that employs one of the optimal design parameters presents good agreement within 10% with the simulation results. Inductor ; Inductance ; Computer science ; Finite element method ; Multi-objective optimization ; Genetic algorithm ; Electromagnetic coil ; Magnetic field ; Mathematical optimization ; Electronic engineering ; Electrical engineering ; Mathematics ; Engineering ; Physics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Induction Heating and Inverter Technology