QuChemPedIA@home/en
| QuChemPedIA is short for Quantum Chemistry Collaborative EncyclopedIA.
Molecular chemistry is lagging behind in term of open science. Although modelization by quantum mechanics applied to chemistry has become almost mandatory in any major publication, computational raw data is most of the time kept in the labs or destroyed. Furthermore, the software used in this area tend to lack effective quality control and computational details are usually incomplete in the articles and the information may not be reused or reproduced. The first objective of this project is to constitute a large collaborative open platform that will solve and store quantum molecular chemistry results. Original output files will be available to be reused to tackle new chemical studies for different applications. Machine learning and more generally artificial intelligence applied to chemistry data promises to revolutionize this area in the near future, but these methods require a lot of data that this project will be able to provide. Today, it is impossible for a human to take into account the results, even limited to the most important data, for millions of known molecules. The second objective of this project is to radically change the approach developing artificial intelligence and optimization methods in order to explore efficiently the highly combinatorial molecular space. Generative models aim to provide an artificial assistant, which on the one hand has learned to predict the characteristics of a molecule and estimate its cost of synthesis, and on the other hand is able to browse effectively the molecular space. Generative models would open many perspectives by greatly facilitating the screening of new molecules with many potential applications (energy, medicine, materials, etc.). The bottleneck for our AIs is the computing power needed to verify the properties of the generated molecules. By supporting this project, you will help chemical researchers around the world by building a unique collection of results. You will also help our AIs to propose much more new targets for the different applications we are addressing than we could do on our own. On Windows systems, an installed VirtualBox-version ist necessary to run the project. On Linux, the app runs native. Badges:
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