Data driven computational design of stable oxygen evolution catalysts by DFT and machine learning:Promising electrocatalysts

The revolutionary development of machine learning(ML),data science,and analytics,coupled with its application in material science,stands as a significant milestone of the scientific community over the last decade.Investigating active,stable,and cost-efficient catalysts is crucial for oxygen evolution reaction owing to the significance in a range of electrochemical energy co nversion processes.In this work,we have demonstrated an efficient approach of high-throughput screening to find stable transition metal oxides under acid condition for high-performance oxygen evolution reaction(OER)catalysts through density functional theory(DFT)calculation and a machine learning algorithm.A methodology utilizing both the Materials Project database and DFT calculations was introduced to assess the acid stability under specific reaction conditions.Building upon this,OER catalytic activity of acid-stable materials was examined,highlighting potential OER catalysts that meet the required properties.We identified IrO_(2),Fe(SbO_(3))_(2),Co(SbO_(3))_(2),Ni(SbO_(3))_(2),FeSbO_(4),Fe(SbO_(3))4,MoWO_(6),TiSnO_(4),CoSbO_(4),and Ti(WO_(4))_(2)as promising catalysts,several of which have already been experimentally discovered for their robust OER performance,while others are novel for experimental exploration,thereby broadening the chemical scope for efficient OER electrocatalysts.Descriptors of the bond length of TM-O and the first ionization energy were used to unveil the OER activity origin.From the calculated results,guidance has been derived to effectively execute advanced high-throughput screenings for the discovery of catalysts with favorable properties.Furthermore,the intrinsic correlation between catalytic performance and various atomic and structural factors was elucidated using the ML algorithm.Through these approaches,we not only streamline the choice of the promising electrocatalysts but also offer insights for the design of varied catalyst models and the discovery of superior catalysts.

Local probing of the non-uniform distribution of ferrielectric and antiferroelectric phases

Piezoresponse force microscopy(PFM)is an indispensable tool in the investigation of local electromechanical responses and polarization switching.The acquired data provide spatial information on the local disparity of polarization switching and electromechanical responses,making this technique advantageous over macroscopic approaches.Despite its widespread application in ferroelectrics,it has rarely been used to investigate the ferrielectric(FiE)behaviors in antiferroelectric(AFE)materials.Herein,the PFM was utilized to study the local electromechanical behavior and distribution of FiE,and the AFE phases of PbZrO_(3)thin-film were studied,where only the FiE behavior is observable using a macroscopic approach.The FiE region resembles a ferroelectric material at low voltages but exhibits a unique on-field amplitude response at high voltages.In contrast,the AFE region only yields an observable response at high voltages.Phase-field simulations reveal the coexistence of AFE and FiE states as well as the phase-transition processes that underpin our experimental observations.Our work illustrates the usefulness of PFM as an analytical tool to characterize AFE/FiE materials and their phase-coexistence behavior,thereby providing insights to guide property modification and potential applications.

Deep learning for exploring ultra-thin ferroelectrics with highly improved sensitivity of piezoresponse force microscopy

Hafnium oxide-based ferroelectrics have been extensively studied because of their existing ferroelectricity,even in ultra-thin film form.However,studying the weak response from ultra-thin film requires improved measurement sensitivity.In general,resonance-enhanced piezoresponse force microscopy(PFM)has been used to characterize ferroelectricity by fitting a simple harmonic oscillation model with the resonance spectrum.However,an iterative approach,such as traditional least squares(LS)fitting,is sensitive to noise and can result in the misunderstanding of weak responses.In this study,we developed the deep neural network(DNN)hybrid with deep denoising autoencoder(DDA)and principal component analysis(PCA)to extract resonance information.The DDA/PCA-DNN improves the PFM sensitivity down to 0.3 pm,allowing measurement of weak piezoresponse with low excitation voltage in 10-nm-thick Hf_(0.5)Zr_(0.5)O_(2) thin films.Our hybrid approaches could provide more chances to explore the low piezoresponse of the ultra-thin ferroelectrics and could be applied to other microscopic techniques.

Super-resolution visible photoactivated atomic force microscopy

Imaging the intrinsic optical absorption properties of nanomaterials with optical microscopy(OM)is hindered by the optical diffraction limit and intrinsically poor sensitivity.Thus,expensive and destructive electron microscopy(EM)has been commonly used to examine the morphologies of nanostructures.Further,while nanoscale fluorescence OM has become crucial for investigating the morphologies and functions of intracellular specimens,this modality is not suitable for imaging optical absorption and requires the use of possibly undesirable exogenous fluorescent molecules for biological samples.Here we demonstrate super-resolution visible photoactivated atomic force microscopy(pAFM),which can sense intrinsic optical absorption with~8 nm resolution.Thus,the resolution can be improved down to~8 nm.This system can detect not only the first harmonic response,but also the higher harmonic response using the nonlinear effect.The thermoelastic effects induced by pulsed laser irradiation allow us to obtain visible pAFM images of single gold nanospheres,various nanowires,and biological cells,all with nanoscale resolution.Unlike expensive EM,the visible pAFM system can be simply implemented by adding an optical excitation sub-system to a commercial atomic force microscope.