A descriptor for molecular environments in molecular crystals

Descriptors are the basic input for supervised machine learning schemes. Several such descriptors have been proposed and compared to characterize atomic environments [1]. To describe van der Waals and other long range interactions [2] adapted fingerprints are required that contain information about a much larger environment and describe the relative orientation and distances of possibily large molecules in a molecular crystal. We fill this gap and present a descriptor, that when used as an input for a high dimensional neural network [4], can accurately describe hydrogen bonding and van der Waals interactions between  molecules in molecular crystals. It is based on an atomic fingerprint [3] that is used for a very large region containing several molecules and compressed with the simplex method [5].

 

[1] B. Parsaeifard et al., Machine Learning: Science and Technology, 2021 (2)

[2] B. Parsaeifard et al., Condensed Matter, 2021 (6)

[3] L. Zhu et al., J. Chem. Phys., 2016 (144)

[4] J. Behler, Int. J. Quantum Chem., 2015 (115)

[5] B. Parsaeifard et al. J. Chem. Phys., 2020 (153)