Simulation Study of Electron Beam Induced Surface Plasmon Excitation at Nanoparticles

Phenomenon of localized surface plasmon excitation at nanostructured materials has attracted much attention in recent decades for their wide applications in single molecule detection,surface-enhanced Raman spectroscopy and nano-plasmonics.In addition to the excitation by external light field,an electron beam can also induce the local surface plasmon excitation.Nowadays,electron energy loss spectroscopy(EELS)technique has been increasingly employed in experiment to investigate the surface excitation characteristics of metallic nanoparticles.However,a present theoretical analysis tool for electromagnetic analysis based on the discrete dipole approximation(DDA)method can only treat the case of excitation by light field.In this work we extend the DDA method for the calculation of EELS spectrum for arbitary nanostructured materials.We have simulated EELS spectra for different incident locations of an electron beam on a single silver nanoparticle,the simulated results agree with an experimental measurement very well.The present method then provides a computation tool for study of the local surface plasmon excitation of metallic nanoparticles induced by an electron beam.

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade.One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments,which has given rise to the need for accelerated and accurate automated estimation of the properties of materials.In this regard,spectroscopic data are widely used for materials discovery because these data include essential information about materials.An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure,or kernel function.The required measure should be robust in terms of peak shifting,peak broadening,and noise.However,the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved.We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra.The similarity measures show good correspondence with the materials parameter,that is,the crystal-field parameter,in all measures.The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening.We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra.The regression model enabled the materials parameter,that is,10 Dq,to be automatically estimated from the spectra.With regard to research progress in similarity measures,this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data.

Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.

Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling

Spectroscopy is a widely used experimental technique,and enhancing its efficiency can have a strong impact on materials research.We propose an adaptive design for spectroscopy experiments that uses a machine learning technique to improve efficiency.We examined X-ray magnetic circular dichroism(XMCD)spectroscopy for the applicability of a machine learning technique to spectroscopy.An XMCD spectrum was predicted by Gaussian process modelling with learning of an experimental spectrum using a limited number of observed data points.Adaptive sampling of data points with maximum variance of the predicted spectrum successfully reduced the total data points for the evaluation of magnetic moments while providing the required accuracy.The present method reduces the time and cost for XMCD spectroscopy and has potential applicability to various spectroscopies.