Automating selective area electron diffraction phase identification using machine learning

Selective area electron diffraction(SAED)patterns can provide valuable insight into the structure of a material.However,the manual identification of collected patterns can be a significant bottleneck in the overall phase classification workflow.In this work,we utilize the recent advances in computer vision and machine learning(ML)to automate the indexing of SAED patterns.The performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium alloys.The most successful approach trained a neural network(NN)to make a classification of the phase and zone axis,and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase identification.The results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization.

Phase Identification of Low-voltage Distribution Network Based on Stepwise Regression Method

Accurate information for consumer phase connectivity in a low-voltage distribution network(LVDN)is critical for the management of line losses and the quality of customer service.The wide application of smart meters provides the data basis for the phase identification of LVDN.However,the measurement errors,poor communication,and data distortion have significant impacts on the accuracy of phase identification.In order to solve this problem,this paper proposes a phase identification method of LVDN based on stepwise regression(SR)method.First,a multiple linear regression model based on the principle of energy conservation is established for phase identification of LVDN.Second,the SR algorithm is used to identify the consumer phase connectivity.Third,by defining a significance correction factor,the results from the SR algorithm are updated to improve the accuracy of phase identification.Finally,an LVDN test system with 63 consumers is constructed based on the real load.The simulation results prove that the identification accuracy achieved by the proposed method is higher than other phase identification methods under the influence of various errors.

Identification method of seismic phase in three-component seismograms on the basis of wavelet transform

This paper puts forward wavelet transform method to identify P and S phases in three component seismograms using polarization information contained in the wavelet transform coefficients of signal. The P and S wave locator functions are constructed by using eigenvalue analysis method to wavelet transform coefficient across several scales. Locator functions formed by wavelet transform have stated noise resistance capability, and is proved to be very effective in identifying the P and S arrivals of the test data and actual earthquake data.

Identification of Crystalline Phases in Toothpaste byPowder Diffractometry

The abrasive crystalline phases in Colgate Regular, Aquafresh , and Macleans tcothpaste were identifiedby X-ray powder diffractometry to be calcium ortho-phaphate hydrate (CaHPO_4 . 2H_2O),calcite (CaCO_3) ,and aragonite and calcite (beth CaCO_3), respectively.

Automated Identification of Ordered Phases for Simulation Studies of Block Copolymers

In unit cell simulations,identification of ordered phases in block copolymers(BCPs)is a tedious and time-consuming task,impeding the advancement of more streamlined and potentially automated research workflows.In this study,we propose a scattering-based automated identification strategy(SAIS)for characterization and identification of ordered phases of BCPs based on their computed scattering patterns.Our approach leverages the scattering theory of perfect crystals to efficiently compute the scattering patterns of periodic morphologies in a unit cell.In the first stage of the SAIS,phases are identified by comparing reflection conditions at a sequence of Miller indices.To confirm or refine the identification results of the first stage,the second stage of the SAIS introduces a tailored residual between the test phase and each of the known candidate phases.Furthermore,our strategy incorporates a variance-like criterion to distinguish background species,enabling its extension to multi-species BCP systems.It has been demonstrated that our strategy achieves exceptional accuracy and robustness while requiring minimal computational resources.Additionally,the approach allows for real-time expansion and improvement to the candidate phase library,facilitating the development of automated research workflows for designing specific ordered structures and discovering new ordered phases in BCPs.

Phase composition, transition and structure stability of functionally graded cemented carbide with dual phase structure

The phase composition, phase transition and phase structure transformation of the wire-cut section of functionally graded WC-Co cemented carbide with dual phase structure were investigated by XRD phase analysis. It is shown that the composition of η phase in the core zone is Co_3W_3C (M_6 C type). The structure of cobalt based solid solution binder phase is fcc type. At the cooling stage of the sintering process, the phase transition of η phase, i.e. M_6C→M_12C and the martensitic phase transition of the cobalt based solid solution binder phase, i.e. fcc→hcp are suppressed, which facilitates the strengthening of the alloy. Because the instantaneous temperature of the discharge channel is as high as 10 000 ℃ during the wire cutting process, the processed surface is oxidized. Nevertheless, the oxide layer thickness is in micro grade. In the oxide film, η phase is decomposed into W_2C and CoO, and cobalt based solid solution binder is selectively oxidized, while WC remains stable due to the existence of carbon containing liquid organic cutting medium.

DiTing:A large-scale Chinese seismic benchmark dataset for artificial intelligence in seismology

In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.

Atomic-scale investigation of precipitate phases in QE22 Mg alloy

Precipitation-hardenable commercial Mg alloy QE22(Mg-2.5Ag-2.ONd-0.7Zr,wt.%)has excellent mechan-ical properties,but precipitates in this alloy have not been well understood.In this work,precipitate phasesγ",γ,andδformed during the isothermal ageing process at 150,200,250,and 300℃have been characterized using atomic-resolution high-angle annular dark-field scanning transmission electron mi-croscopy and atomic-scale energy-dispersive X-ray spectroscopy.The morphology,crystal structure,and orientation relationship of these precipitate phases have been determined.Domain boundaries usually exist in a singleγparticle,which can be characterized by a separation vector of[1(1)01]_(α).Theδphase forms in situ from its precursorγphase,consequently leading to the formation of three different variants within a single 8 particle.The nucleation of theδphase is strongly related to the domain boundaries of the y phase.The formation of theγphase may be promoted by its precursorγ"phase.The similarities in atomic structures of theγ",γ,andδphases are described and discussed,indicating that transfor-mations between these precipitate phases can be accomplished through the diffusion of added alloying elements.

Investigation of the isothermal section of the Ce-Co-Al ternary system at 573 K

The isothermal section of the Ce-Co-Al ternary system at 573 K was investigated by X-ray powder diffraction （XRD）, scanning electron microscopy （SEM） and energy dispersive X-ray spectroscopy （EDS） techniques. It consisted of 19 single-phase regions, 46 two-phase regions and 25 three-phase regions. Four ternary compounds, namely CeCoAl, Ce2Co15Al2, CeCoAl4, CeCo2Al8, were confirmed in this system. At 573 K, the maximum solid solubilities of Co in CeAl2 and Al in CeCo2 were about 10.4 at.% and 10.0 at.%, respectively. The homogeneity range of CoAl phase extended from about 46.0 to 56.0 at.% Al.