Historical milestone · 2017
ANI-1 neural potential
Reviewed through September 18, 2026
2017 · Historical milestone
ANI-1 neural potential
- Era
- 2010s
- Theme
- AI for science
- Evidence form
- Computational chemistry experiment
- School / paradigm
- Scientific machine learning
- Institution / context
- University of Florida; University of North Carolina
- Researchers
- Justin Smith; Olexandr Isayev; Adrian Roitberg
Understand
Plain-language record, transferred from the reviewed source module.
Theory or experimental setup. Trained a neural-network potential on density-functional calculations for organic molecular conformations.
Result / historical claim. Approximated quantum-chemical potential energies efficiently and transferred to larger molecules in tested cases.
Apply
Professional implication, only where the reviewed record states one.
The checked-in record does not state a separate professional application for this entry. The topic page places it in the wider research lineage: AI for science.
Verify
Evidence status, stated limitations, and the external sources this record actually carries.
Evidence form. Computational chemistry experiment
Limitation / debate. Accuracy depended on element coverage, conformation coverage, and expensive reference calculations.
Source status. The source link below is the verified link our reviewed topic research already carries for this milestone.
Reproduce
A reproduction tutorial is linked only when one exists for this exact record.
A reproduction tutorial is not yet available for this entry. The closest reviewed material is AI for science.
Cite or share
APA-like: This historical record carries a year only, and no author or publisher of record in the checked-in data. An APA reference would have to invent that metadata.
BibTeX: BibTeX requires an author and publication venue. Historical lineage entries store a narrative record and its source link, not structured authorship, so the field would be fabricated.