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.

Predicting interfacial thermal resistance by machine learning

Various factors affect the interfacial thermal resistance(ITR)between two materials,making ITR prediction a high-dimensional mathematical problem.Machine learning is a cost-effective method to address this.Here,we report ITR predictive models based on experimental data.The physical,chemical,and material properties of ITR are categorized into three sets of descriptors,and three algorithms are used for the models.Those descriptors assist the models in reducing the mismatch between predicted and experimental values and reaching high predictive performance of 96%.Over 80,000 material systems composed of 293 materials were inputs for predictions.Among the top-100 high-ITR predictions by the three different algorithms,25 material systems are repeatedly predicted by at least two algorithms.One of the 25 material systems,Bi/Si achieved the ultra-low thermal conductivity in our previous work.We believe that the predicted high-ITR material systems are potential candidates for thermoelectric applications.This study proposed a strategy for material exploration for thermal management by means of machine learning.

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.

Empirical interatomic potentials optimized for phonon properties

Molecular dynamics simulations have been extensively used to study phonons and gain insight,but direct comparisons to experimental data are often difficult,due to a lack of accurate empirical interatomic potentials for different systems.As a result,this issue has become a major barrier to realizing the promise associated with advanced atomistic-level modeling techniques.Here,we present a general method for specifically optimizing empirical interatomic potentials from ab initio inputs for the study of phonon transport properties,thereby resulting in phonon optimized potentials.The method uses a genetic algorithm to directly fit the empirical parameters of the potential to the key properties that determine whether or not the atomic level dynamics and most notably the phonon transport are described properly.

First-principles screening of structural properties of intermetallic compounds on martensitic transformation

Martensitic transformation with good structural compatibility between parent and martensitic phases are required for shape memory alloys(SMAs)in terms of functional stability.In this study,first-principles-based materials screening is systematically performed to investigate the intermetallic compounds with the martensitic phases by focusing on energetic and dynamical stabilities as well as structural compatibility with the parent phase.The B2,D0_(3),and L2_(1) crystal structures are considered as the parent phases,and the 2H and 6M structures are considered as the martensitic phases.In total,3384 binary and 3243 ternary alloys with stoichiometric composition ratios are investigated.It is found that 187 alloys survive after the screening.Some of the surviving alloys are constituted by the chemical elements already widely used in SMAs,but other various metallic elements are also found in the surviving alloys.The energetic stability of the surviving alloys is further analyzed by comparison with the data in Materials Project Database(MPD)to examine the alloys whose martensitic structures may cause further phase separation or transition to the other structures.

Fine-grained optimization method for crystal structure prediction

Crystal structure prediction based on first-principles calculations is often achieved by applying relaxation to randomly generated initial structures.Relaxing a structure requires multiple optimization steps.It is time consuming to fully relax all the initial structures,but it is difficult to figure out which initial structure leads to the optimal solution in advance.In this paper,we propose a optimization method for crystal structure prediction,called Look Ahead based on Quadratic Approximation,that optimally assigns optimization steps to each candidate structure.It allows us to identify the most stable structure with a minimum number of total local optimization steps.Our simulations using known systems Si,NaCl,Y_(2)Co_(17),Al_(2)O_(3),and GaAs showed that the computational cost can be reduced significantly compared to random search.This method can be applied for controlling all kinds of local optimizations based on first-principles calculations to obtain best results under restricted computational resources.

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.

Deep-learning-based quality filtering of mechanically exfoliated 2D crystals

Two-dimensional(2D)crystals are attracting growing interest in various research fields such as engineering,physics,chemistry,pharmacy,and biology owing to their low dimensionality and dramatic change of properties compared to the bulk counter parts.Among the various techniques used to manufacture 2D crystals,mechanical exfoliation has been essential to practical applications and fundamental research.However,mechanically exfoliated crystals on substrates contain relatively thick flakes that must be found and removed manually,limiting high-throughput manufacturing of atomic 2D crystals and van der Waals heterostructures.Here,we present a deep-learning-based method to segment and identify the thickness of atomic layer flakes from optical microscopy images.Through carefully designing a neural network based on U-Net,we found that our neural network based on Unet trained only with the data based on realistically small number of images successfully distinguish monolayer and bilayer MoS2 and graphene with a success rate of 70–80%,which is a practical value in the first screening process for choosing monolayer and bilayer flakes of all flakes on substrates without human eye.The remarkable results highlight the possibility that a large fraction of manual laboratory work can be replaced by AI-based systems,boosting productivity.

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.

Temperature-dependent phonon spectra of magnetic random solid solutions

A first-principles-based computational tool for simulating phonons of magnetic random solid solutions including thermal magnetic fluctuations is developed.The method takes fluctuations of force constants due to magnetic excitations as well as due to chemical disorder into account.The developed approach correctly predicts the experimentally observed unusual phonon hardening of a transverse acoustic mode in Fe–Pd an Fe–Pt Invar alloys with increasing temperature.This peculiar behavior,which cannot be explained within a conventional harmonic picture,turns out to be a consequence of thermal magnetic fluctuations.The proposed methodology can be straightforwardly applied to a wide range of materials to reveal new insights into physical behaviors and to design materials through computation,which were not accessible so far.