[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126579-en":3,"doc-seo-126579-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126579,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics","Advanced experimental measurements are essential for condensed matter and material physics, but limited facility resources and growing experimental complexity constrain progress. A methodology is presented that integrates machine learning with Bayesian optimal experimental design, demonstrated through x-ray photon fluctuation spectroscopy measurements of spin fluctuations. A neural network model enables scalable spin dynamics simulations to compute distribution and utility quantities needed by BOED, while automatic differentiation improves parameter estimation. Numerical benchmarks show superior guidance for XPFS experiments and more informative results under limited experimental time, and the approach generalizes beyond XPFS.","arXiv :2306 .02015v1 [ cond-mat .mtrl-sci ] 3 Jun 2023  \nMachine learning enabled experimental design and parameter estimation for ultrafast spin dynamics  \nZhantao Chen 1,2 , Cheng Peng 1 , Alexander N. Petsch 1,2 , Sathya R. Chitturi 1,3 , Alana Okullo4 , Sugata Chowdhury4 , Chun Hong Yoon2 , and Joshua J. Turner 1,2,*  \n1 Stanford Institute for Materials and Energy Sciences, Stanford University, Stanford, CA, USA.  \n2 Linac Coherent Light Source, SLAC National Accelerator Laboratory, Menlo Park, CA, USA.  \n3 Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.  \n4 Department of Physics and Astronomy, Howard University, Washington DC, USA.  \n*  \nCorresponding author: [joshuat@slac.stanford.edu](joshuat@slac.stanford.edu)  \nJune 6, 2023  \nAbstract  \nAdvanced experimental measurements are crucial for driving theoretical developments and unveiling novel phenomena in condensed matter and material physics, which often suffer from the scarcity of facility resources and increasing complexities. To address the limitations, we introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED), exemplified with x-ray photon fluctuation spectroscopy (XPFS) measurements for spin fluctuations. Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. The capability of automatic differentiation from the neural network model is further leveraged for more robust and accurate parameter estimation. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time. Although focusing on XPFSand spin fluctuations, our method can be adapted to other experiments, facilitating more efficient data collection and accelerating scientific discoveries.  \n1 Introduction  \nEver since the discovery of x-rays, considerable breakthroughs have been made using them as a probe of matter, from testing models of the atom to solving the structure of deoxyribonucleic acid (DNA) . Over the last few decades with the proliferation of synchrotron x-ray sources around the world, the application to many scientific fields has progressed tremendously and allowed studies of complicated structures and phenomena like protein dynamics and crystallography [1, 2], electronic structures of strongly correlated materials [3, 4], and a wide variety of elementary excitations [5, 6] . With the the development of the next generation of light sources, especially the x-ray free electron lasers (X-FEL) [7, 8], not only have discoveries accelerated, but completely novel techniques have been developed and new fields of science have emerged, such as laboratory astrophysics [9, 10 , 11 , 12] and single particle diffractive imaging [13, 14 , 15] .  \nAmong these emerging techniques brought by X-FELs, the development of x-ray photon fluctuation spectroscopy (XPFS) holds particular relevance for condensed matter and material physics [16] . XPFS is a unique and powerful approach that opens up numerous opportunities to probe ultrafast dynamics of timescales corresponding to the µeV to meV-energy level. As the high-level coherence of the x-ray beam encodes subtle changes in the system at these timescales, XPFS is capable of investigating fluctuations of elementary excitations, such as that of the spin [17] . The fluctuation spectra collected using this method can be directly related back to correlation functions derived from Hamiltonians [18, 19], yielding invaluable experimental insights for theoretical developments and deeper understandings of the underlying physics.  \nDespite the breakthroughs, the critical dependence of XPFS on the rare experimental resource of X-FEL beamtime has prevented widespread adoption of such advanced XPFS measurements and hindered further  \nscientific explorations. The targ","cbCaisQ1KwN5GEpM","https://ap.wps.com/l/cbCaisQ1KwN5GEpM","pdf",1892016,3,1,18,"English","en",105,"# Introduction\n## X-ray probes and emerging ultrafast techniques\n## XPFS for ultrafast spin dynamics\n## Bayesian optimal experimental design challenges\n## ML surrogates for efficient BOED","[{\"question\":\"What problem does the paper address in ultrafast spin dynamics experiments?\",\"answer\":\"It addresses the difficulty of performing theory-informed, data-driven experimental design when experimental beamtime is scarce and forward simulations for BOED are computationally expensive and complex.\"},{\"question\":\"How does the proposed method combine machine learning with Bayesian optimal experimental design?\",\"answer\":\"It uses a neural network model to perform large-scale spin dynamics simulations, enabling efficient computation of distribution and utility terms required for BOED, and it leverages automatic differentiation for improved parameter estimation.\"},{\"question\":\"What evidence demonstrates the effectiveness of the method for XPFS measurements?\",\"answer\":\"Numerical benchmarks indicate improved performance in guiding XPFS experiments, predicting model parameters, and producing more informative measurements within limited experimental time.\"}]","Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics | 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problem does the paper address in ultrafast spin dynamics experiments?","Question",{"text":76,"@type":77},"It addresses the difficulty of performing theory-informed, data-driven experimental design when experimental beamtime is scarce and forward simulations for BOED are computationally expensive and complex.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method combine machine learning with Bayesian optimal experimental design?",{"text":81,"@type":77},"It uses a neural network model to perform large-scale spin dynamics simulations, enabling efficient computation of distribution and utility terms required for BOED, and it leverages automatic differentiation for improved parameter estimation.",{"name":83,"@type":74,"acceptedAnswer":84},"What evidence demonstrates the effectiveness of the method for XPFS measurements?",{"text":85,"@type":77},"Numerical benchmarks indicate improved performance in guiding XPFS experiments, predicting model parameters, and 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