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Riccardo Conte — most-cited papers & profile · AI for Science
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Riccardo Conte
3
papers ·
11
citations
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Most-cited papers
Tell machine learning potentials what they are needed for: Simulation-oriented training exemplified for glycine
2024 · 11 citations
$Δ$-Machine Learning for Potential Energy Surfaces: A PIP approach to bring a DFT-based PES to CCSD(T) Level of Theory
2020
Breaking the Coupled Cluster Barrier for Machine Learned Potentials of Large Molecules: The Case of 15-atom Acetylacetone
2021
Top co-authors
Apurba Nandi
· 3
Chen Qu
· 3
Joel M. Bowman
· 2
Paul Houston
· 2
and Joel M. Bowman
· 1
Fuchun Ge
· 1
Paul L. Houston
· 1
Pavlo O. Dral
· 1
Peikun Zheng
· 1
Ran Wang
· 1
Topics
Chemistry
Math & Equations
Physics ML
Materials