ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules

Understand

One of the grand challenges in modern theoretical chemistry is designing and implementing approximations that expedite ab initio methods without loss of accuracy.

• Machine learning (ML), in particular neural networks, are emerging as a powerful approach to constructing various forms of transferable atomistic potentials.
• They have been successfully applied in a variety of applications in chemistry, biology, catalysis, and solid-state physics.
• However, these models are heavily dependent on the quality and quantity of data used in their fitting.