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researcher

Joe G Greener

3 papers hereh-index 3101 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • middle author1
  • last author1

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • q-bio.BM2
  • physics.chem-ph1

identity via Semantic Scholar / OpenAlex

collaborators

3 papers

q-bio.BM2026

Training a force field for proteins and small molecules from scratch

Alexandre Blanco-González, Thea K Schulze, Evianne Rovers +1

Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a…

q-bio.BM2024

Reversible molecular simulation for training classical and machine learning force fields

Joe G Greener

The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, es…

physics.chem-ph2024

On the design space between molecular mechanics and machine learning force fields

Yuanqing Wang, Kenichiro Takaba, Michael S. Chen +14

A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics (MM), with which one can simulate a biomolecular system efficiently enough and meaningfully e…

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