Project Overview

[Dingo]

 
Project Summary

QuChemPedIA : Quantum Chemistry encycloPed and Intelligence Artificielle.

An Invitation code is required to attach and at the time of posting it was: 3VwMu3-eTCg32

"Abstract: The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML molecular predictions have been recently published with an accuracy on par with Density Functional Theory calculations. Such ML models need to be tested and generalized on real data. PC9, a new QM9 equivalent dataset (only H, C, N, O and F and up to 9 "heavy" atoms) of the PubChemQC project is presented in thisarticle. A statistical study of bonding distances and chemical functions shows that this new dataset encompasses more chemical diversity. Kernel Ridge Regression, Elastic Net and the Neural Network model provided by SchNet have been used on both datasets. The overall accuracy in energy prediction is higher for the QM9 subset. However, a model trained on PC9 shows a stronger ability to predict energies of the other dataset."

The QM9 dataset has around 130k small molecules, when our PC9 has 119k (but was extracted from another type of calculations). The problem is that the full results of the QM9 are not openly available. They have extracted some results of the costly quantum mechanics calculations and trashed the log. We are not satisfied by PC9 that was a simple demonstration that more diversity is needed.

For the moment the boinc project is aiming at recalculating the interesting molecules of QM9 and PC9 with the same level of calculation this time. All the results will be available at the quchempedia document base https://quchempedia.univ-angers.fr when this platform will be a little bit more robust (beginning 2020) in par with our quality control tool as written by my colleague.
We are not fully happy with NWChem yet. With the same boinc project damotbe and myself, are using Gaussian (proprietary) which is much efficient. But Nwchem is open source...
We have calculated roughly 130 k over 200 k thanks to your help!
For December we hope to propose to the community to calculate new molecules that maybe don't even exist and are not stable in order to help machine learning tool to generalize better. Those new molecules will be generated by a machine learning procedure. Too long to explain here right now.

Applications

The following applications are supported:

Please see their Applications page as they change with new applications HERE  They are a combination of CPU and VirtualBox applications.

Connecting to the Project

The projects Home Page is at: https://quchempedia.univ-angers.fr/athome/

Don't forget to join BOINC@Australia at home Team following your registration.

Statistics

View our Team Members List and their current score HERE
View detailed BOINCStats for our Team HERE