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Record W3023775684 · doi:10.2172/1616251

Basic Energy Sciences Roundtable: Opportunities for Basic Research at the Frontiers of XFEL Ultrafast Science

2017· report· en· W3023775684 on OpenAlexaff
Tony F. Heinz, Oleg Shpyrko, Dimitri Basov, N. Berrah, P. H. Bucksbaum, Tom Devereaux, David Fritz, Kelly J. Gaffney, Oliver Geßner, Venkat Gopalan, Md. Zahid Hasan, Alessandra Lanzara, Todd J. Martı́nez, Andy Millis, Shaul Mukamel, Margaret M. Murnane, Keith A. Nelson, Rohit P. Prasankumar, David A. Reis, Ken Schäfer, Greg Scholes, Zhi‐Xun Shen, Albert Stolow, Haidan Wen, Martin Wolf, Di Xiao, Linda Young, Bruce C. Garrett, Linda Horton, Helen M. Kerch, Jeff Krause, Tom Settersten, Lane Wilson, Katie Runkles, Terry L. Anderson, Glennda Chui, Eiko Rutherford

Bibliographic record

Venuenot available
Typereport
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUltrashort pulseCharacterization (materials science)NanotechnologyFemtosecondScale (ratio)PhysicsEnergy (signal processing)Engineering physicsData scienceComputer scienceLaserMaterials scienceOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Advances in science and technology over the past century have been driven by an improved understanding of matter on ultrashort length scales, reaching down to atomic dimensions. In contrast, methods aimed at understanding dynamics on the ultrafast time scales of atomic motion are comparatively new. Ultrafast characterization has already yielded crucial insights not attainable from slower measurements. The interplay between atomic-scale structure and the associated ultrafast dynamics governs the macroscopic functionality observed in matter. Understanding and controlling materials and chemical processes at these length and time scales are key to discovery and innovation to advance energy and related national priorities. The recent availability of x-ray free-electron lasers (XFELs) provides a probe that simultaneously reaches the required resolution in both space and time. X-ray wavelengths extend down to the atomic scale, while x-ray pulse durations now lie in the femtosecond (10-15 seconds) range. This capability allows the evolution of materials and chemical processes to be followed on their natural time and length scales, providing fundamental scientific understanding of the complexity of the world around us. Because of the transformative potential of new ultrafast x-ray characterization tools provided by XFELs, it is imperative to lay out a roadmap for the exciting scientific opportunities that can be explored using these research tools. To identify the highest priority research opportunities, the U.S. Department of Energy Office of Basic Energy Sciences (BES) convened a roundtable of experts in chemistry, materials physics, and ultrafast and x-ray science. This group of experimentalists and theorists met on October 25–26, 2017 to explore research opportunities that will leverage current and imminent ultrafast XFEL capabilities and advance the broader BES science mission. This report summarizes major new scientific frontiers that can be addressed by emerging XFEL capabilities. The conclusions of the roundtable discussion are summarized in the following three Priority Research Opportunities (PROs).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.011
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0050.003
Scholarly communication0.0120.009
Open science0.0040.011
Research integrity0.0200.016
Insufficient payload (model declined to judge)0.0580.023

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.161
GPT teacher head0.457
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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