Machine learning×Cryo-electron microscopy

The Frontier Brief for this collision is still being written. The evidence below already reflects the current model run.

Pre-company space. No named players have staked this collision yet — an emerging pairing where the field, not a firm, is the story so far.

20 researchers publish on both sides of this collision without the fields themselves having met. Every name below is counted from papers in the corpus — not inferred.

• Jianlin ChengUniversity of Missouri Health System
  3/2
• Frank DiMaioUniversity of Pennsylvania
  1/4
• Ye DuanUniversity of Information Technology and Communications
  2/1
• Yang ZhangZhejiang University of Technology
  1/2
• Nabin GiriUniversity of Missouri
  1/1
• Highsmith MaxUniversity of Missouri
  1/1
• Adil Al-AzzawiUniversity of Missouri
  1/1
• John J. TannerUniversity of Missouri
  1/1
• Fa ZhangShandong University
  1/1
• Anes OuadouUniversity of Missouri
  1/1

• Harvard UniversityUS
  72/87
• Stanford UniversityUS
  131/42
• University of CambridgeGB
  65/62
• University of California, BerkeleyUS
  132/30
• University of OxfordGB
  66/50
• Tsinghua UniversityCN
  73/34

• Vitrification after multiple rounds of sample application and blotting improves particle density on cryo-electron microscopy grids2017 · 87 citations · DOI ↗
  3
• PAL 2.0: a physics-driven bayesian optimization framework for material discovery2023 · 21 citations · DOI ↗
  3
• Machine learning for molecular and materials science2018 · 4,750 citations · DOI ↗
  2
• Cryo-electron microscopy structures of the SARS-CoV spike glycoprotein reveal a prerequisite conformational state for receptor binding2016 · 687 citations · DOI ↗
  2

Counted from the corpus. Institution counts use best-effort affiliation (every author on a paper is paired with every institution on it), so read them as presence, not headcount.