Abstract
We develop a systematic approach to optimize training set sizes for neural networks when fitting ab initio potential energy surfaces. We use the approach to construct several spectroscopic quality potential energy surfaces for [Li(H2)n]+, n=1−9. Ground state properties are computed for all the systems and for selected isotopologues. The nuclear quantum effects, produce relatively small zero point energy fractions of the classical binding energy. Evaporating a hydrogen molecule from the pentamer requires 2280 K of energy when the zero point energy contribution is included, making the aggregate thermodynamically stable below 200 K.
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