**Physics Dept
- MtSE Joint Seminar**

**December 12, Thursday
(*SPECIAL DAY*)**

**Machine Learning with
Schrödinger Equation**

**Dr. Guangqi
Li**

Dept. of Chemistry, Columbia Univ.

(Materials Physics, Host: Ken
Ahn)

***TALK*: Tiernan 409, 11:30am - 12:30pm **

**(* SPECIAL
TIME/ROOM and NO TEA TIME *)**

***LUNCH*: Tiernan 406, 12:30pm - 3pm**

Machine learning (ML) is a method of data
analysis, with the powerful ability in predicting. It had become overwhelming,
in atomistic simulation and electronic property predictions. Recently, ML had
been utilized in quantum system, Density Function Theory (DFT), molecular
dynamics, and even in predicting the reaction performance for catalysis, and
the protein-ligand interaction. So far, two large databases had been set up.
One includes 134 kilo molecules with their quantum chemistry structures and
properties. Another includes 20 million calculated off-equilibrium
conformations for 57462 small organic molecules. These databases were obtained
from DFT. Due to the self-interaction term (electron interacts with itself in
mathematical equation), DFT is well-known for its error. The hypothesis of
Localized Orbital Correction (via adding extra operators to remove the error)
had been proposed to the numerical as atomic energies, ionization potential,
and the 3d electron in transition metals. Combining this hypothesis, the new
obtained databases will have the high accuracy when compared to the experiment
or the benchmark quantum chemical calculations.