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Generating Near-Optimized Molecular Geometries Across Reactions using Neural Networks & Back Propagation

The rate-limiting step of computational chemistry is computer capital. That is, the amount of research a lab can do is proportional to their computational throughput. Computational chemistry is generally used in materials research, where the properties of a new molecule are not known and not well studied. One important calculation in this scenario is in optimizing the geometry to an energy minima for a novel molecular structure. This process takes an initial geometry estimate and iteratively moves around all of the atoms to achieve a minima. Materials research often involves studying many similar molecules or reactions. To cut down on computational costs for materials research calculations, this paper proposes an implementation of nonlinear models to generate initial geometry estimates that require fewer calculations to achieve global energy minima. Specifically, this paper aims to justify efforts put into training a neural network to predict the resulting geometries for the products of an indicated reaction given the reactants. To do this, a feed-forward neural network is given initial bond length and angle measurements and is trained, using back propagation, to give the product’s measurements. The resulting molecules are then re-constructed from this data. This paper uses a quinone and quinol set of geometries optimized with Gaussian 09 and the B3LYP/6-311+G(d,p) model chemistry. For the molecules in this set, a neural network with two hidden layers is shown to learn to accurately predict geometries very close to the set of true optimized geometries. A comparison is made to an industry standard lowest energy conformer calculator, ChemAxon’s lowestEnergyConformer plugin. Experiments show that the network can produce molecules with closer geometries and lower total energies than the tool with under 80 data points, and this is expected to decrease calculation times.