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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
