Data-driven discovery of dynamics from time-resolved coherent scattering  

作　　者：Nina Andrejevic Tao Zhou Qingteng Zhang Suresh Narayanan Mathew J.Cherukara Maria K.Y.Chan 

机构地区：[1]Center for Nanoscale Materials,Argonne National Laboratory,Lemont,IL,60439,USA [2]Advanced Photon Source,Argonne National Laboratory,Lemont,IL,60439,USA

出　　处：《npj Computational Materials》2024年第1期856-869,共14页计算材料学(英文)

基　　金：supported by Laboratory Directed Research and Development(LDRD)funding from Argonne National Laboratory,provided by the Director,Office of Science,of the U.S.Department of Energy under Contract No.DE-AC02-06CH11357;M.K.Y.C.acknowledges the support from the BES SUFD Early Career award;Work performed at the Center for Nanoscale Materials,aU.S.Department of Energy Office of Science User Facility,was supported by the U.S.DOE,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357;This research used resources of the Advanced Photon Source,a U.S.Department of Energy(DOE)Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No.DE-AC02-06CH11357.

摘　　要：Coherent X-ray scattering(CXS)techniques are capable of interrogating dynamics of nano-to mesoscale materials systems at time scales spanning several orders of magnitude.However,obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space,computational cost of high-fidelity simulations,and effectiveness of approximate or phenomenologicalmodels.In this work,we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns.Our approach uses neural differential equations to parameterize unknown realspace dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations.This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise.Moreover,the trained model enables estimation of long-term dynamics well beyond the maximum observation time,which can be used to inform and refine experimental parameters in practice.Finally,we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan.Our proposed framework bridges the wide existing gap between approximate models and complex data.

关 键 词：DYNAMICS RESOLVED APPROXIMATE 

分 类 号：O17[理学—数学]