Drawing on a database of quantum chemical results for over 7000 molecules, their program could give the atomisation energies of unfamiliar molecules to within 1% - and in a billionth of the time required for a full approximation. Von Lilienfeld's team trained the algorithm on a subset of molecules in the database, comparing their matrices to find 'distances' between molecules - a measure of the difference between the molecules in terms of their matrices. In the case of finding an unknown molecule's atomisation energy, the distances between the unknown molecule and all the known molecules gave weights for how much each known atomisation energy could contribute to an estimate for the unknown molecule. The researchers found that with a landscape of more than 5000 molecules, the error for predicting atomisation energies of new molecules drops below 10kcal/mol, approaching the 5kcal/mol accuracy of hybrid DFT.
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