Dr. Qichao Hu

universe.

You can compute, for example, 10 to the 8th, 10 to the 9th, pretty quickly.

Wet data, you're talking about 10 to the 4th, 10 to the 5th, so significantly less than the dry data, but that's enough to calibrate the dry data.

Okay.

Okay, so the molecule database consists of just molecule structures.

And the structures are in 3D, but then you can represent the 3D in what's called small strings.

For example, water is H2O, and then you just write O. So you can represent a 3D structure with a string of letters, C, H, O, those letters.

And then what we compute and what we measure are these properties.

The properties are just in these numbers.

For example, melting point, boiling point, energy levels, viscosity, just numbers.

So at the end, you end up with an Excel table of 10 to the 9th, 10 to the 11th, eventually 10 to the 60th smiles streams, and then all the numbers, all the properties.

That's awesome.

Yeah, yeah.

Not enough.

Not enough.

So the basic dry data we're computing is using a technique called density function theory.

That one, if we use machine learning accelerated density function theory, we do about 9 million molecules a day, just the single molecules level.

And then once you get to the cocktail level, so that's where you mix three or five different molecules together,

Right now we can do about 2,000 a day, but we need to do way more.

Yeah, exactly.