[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128618-en":3,"doc-seo-128618-105":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},128618,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Accelerating Multiscale Electronic Stopping Power Predictions with Timedependent Density Functional Theory and Machine Learning","Stopping power quantifies how particle radiation releases energy in a material and is central to nuclear reactor design, medical treatments, and emerging semiconductor and quantum technologies. While nuclear stopping is well characterized, electronic stopping data has been costly and relied on restrictive assumptions such as isotropy. A combined workflow is presented that uses timedependent density functional theory and machine learning to compute electronic stopping from first principles in multiple directions, then interpolate efficiently to new directions at dramatically reduced core-hour cost. The method is demonstrated on proton irradiation in aluminum and predicts Bragg Peak depth dependence on incident angle.","npj | computational materials Article  \nPublished in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences  \n[https://doi.org/10.1038/s41524-024-01374-8](https://doi.org/10.1038/s41524-024-01374-8)  \nAccelerating multiscale electronic stopping power predictions with timedependent density functional theory and machine learning  \n Check for updates  \nLogan Ward 1 , Ben Blaiszik 1,2, Cheng-Wei Lee 3, Troy Martin2, Ian Foster1,2 & André Schleife 3   \nKnowing the rate at which particle radiation releases energy in a material, the “stopping power,” is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functionaltheory(TDDFT) and machine learning to reduce the time to assess new materials to hours on a supercomputer and provide valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping fromfirst principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach ina study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the “Bragg Peak,” varies depending on the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical simulations. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science.  \nParticle radiation plays critical roles in modern society, including fabricating semiconductor electronics, characterizing materials and devices, cancer therapy, damage in nuclear reactors, and many others. Advancing the application of particle radiation in these fields by maximizing and focusing on their benefit and minimizing their detrimental impact requires precise control of microscopic length scales. Achieving such control relies critically on a detailed fundamental understanding ofhow energetic particles interact with target materials. Such understanding has been built for more than one hundred years1,2 through thorough experiments and sophisticated theoretical or computational models. However, radiation experiments have high costs and low throughput, which ultimately can be attributed to high safety margins for experiments involving ionizing radiation. These factors have motivated the development of intricate models that support experiments  \nand eventually allow for predictions ofthe radiation–matter interaction and its consequences.  \nThe high kinetic energies of ion beams and the energy-dependent response of the target material render radiation damage and the stopping power of the material, a friction-like, velocity-dependent force acting on radiation particles, inherently multi-scale problems. Typically, one separation of scales is achieved by distinguishing the early stages of the process, where the projectile ion predominantly interacts with the electronic system of the target, from nuclear stopping, which happens only after the projectile slows down significantly. To deal with the large length and timescale aspect of radiation damage, the predominant mode for predictions relies on models around the binary collision approximation, parameterized by electronic and nuclear-stopping data collected, e.g., in the vener","cbCaichsK1HZlsjN","https://ap.wps.com/l/cbCaichsK1HZlsjN","pdf",2101958,3,1,10,"English","en",105,"# Overview and Motivation\n## Stopping Power in Radiation-Matter Interactions\n## Challenges in Electronic Stopping Data\n# Proposed Multiscale Method\n## TDDFT for Direction-Resolved Electronic Stopping\n## Machine Learning Surrogate for Efficient Interpolation\n# Demonstration and Applications\n## Proton Irradiation in Aluminum\n## Predicting Bragg Peak vs Incident Angle\n## Implications for Quantum-to-Continuum Radiation Damage Modeling","[{\"question\":\"Why is electronic stopping power difficult to obtain compared with nuclear stopping?\",\"answer\":\"Electronic stopping data has historically been expensive to gather and often depends on simplifying assumptions such as isotropy, whereas nuclear stopping is already well established in the literature.\"},{\"question\":\"How does the proposed method combine timedependent density functional theory and machine learning?\",\"answer\":\"It uses timedependent density functional theory to compute electronic stopping from first principles for several directions, then trains a machine-learning model to interpolate stopping behavior to other directions with far fewer core-hours.\"},{\"question\":\"What key result is demonstrated for proton irradiation in aluminum?\",\"answer\":\"The approach predicts how the Bragg Peak depth of maximum energy deposition varies with the incident angle, a quantity otherwise difficult to access through modeling at comparable quantum simulation scales.\"}]","Accelerating Multiscale Electronic Stopping Power Predictions with Timedependent Density Functional Theory and Machine Learning | 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is electronic stopping power difficult to obtain compared with nuclear stopping?","Question",{"text":76,"@type":77},"Electronic stopping data has historically been expensive to gather and often depends on simplifying assumptions such as isotropy, whereas nuclear stopping is already well established in the literature.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method combine timedependent density functional theory and machine learning?",{"text":81,"@type":77},"It uses timedependent density functional theory to compute electronic stopping from first principles for several directions, then trains a machine-learning model to interpolate stopping behavior to other directions with far fewer core-hours.",{"name":83,"@type":74,"acceptedAnswer":84},"What key result is demonstrated for proton irradiation in aluminum?",{"text":85,"@type":77},"The approach predicts how the Bragg Peak depth of maximum energy deposition varies with the incident angle, a quantity 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