Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurations. Many approaches to the construction of such fingerprints, also called structural descriptors, have been proposed. In this work, we compare the performance of fingerprints based on the overlap matrix, the smooth overlap of atomic positions, Behler–Parrinello atom-centered symmetry functions, modified Behler–Parrinello symmetry functions used in the ANI-1ccx potential and the Faber–Christensen–Huang–Lilienfeld fingerprint under various aspects. We study their ability to resolve differences in local environments and in particular examine whether there are certain atomic movements that leave the fingerprints exactly or nearly invariant. For this purpose, we introduce a sensitivity matrix whose eigenvalues quantify the effect of atomic displacement modes on the fingerprint. Further, we check whether these displacements correlate with the variation of localized physical quantities such as forces. Finally, we extend our examination to the correlation between molecular fingerprints obtained from the atomic fingerprints and global quantities of entire molecules.

A good hydrogen storage material should adsorb hydrogen in high concentrations and with optimal binding energies. Exohedrally metal decorated carbon fullerene structures were proposed as a promising material in this context. We present a fully ab-initio, unbiased structure search of the configurational space of decorated C 60 fullerenes and find that many of the hitherto postulated ground state structures are not ground states. We determine the energetically lowest configurations for decorations with a varying number of decorating atoms (2 n 32) for alkali metals, alkaline-earth metals as well as some other important elements and find that the dense uniform distribution of the decorating atoms over the surface of the C 60 , desired for hydrogen storage, can be obtained only for a few elements. An understanding of the behavior of the decorating atoms can be obtained by analyzing their bonding characteristics via the electron localization function.

The decahedral cage is the theoretically established ground state of the hydrogen saturated Si 20 H 20 fullerene. However it has never been observed experimentally. Based on an extensive exploration of the potential energy surface and by constructing theoretical reaction pathways from possible initial structures to the ground state of Si 20 H 20 , we show that there is no driving force towards the global minimum. There exists a huge number of intermediate structures that consist mainly of collapsed cages. Visiting all these intermediate states to find the ground state is not possible on experimentally relevant time scales. In this way the ground state becomes kinetically inaccessible. We contrast the features of the potential energy landscape of Si 20 H 20 with that of C 60 which spontaneously forms by condensation.

Finding complex reaction and transformation pathways, involving many intermediate states, is in general not possible on the DFT level with existing simulation methods due to the very large number of required energy and force evaluations. This is due to a large extent to the fact that for complex reactions, it is not possible to determine which atom in the educt is mapped onto which atom in the product. Trying out all possible atomic index mappings is not feasible because of the factorial increase in the number of possible mappings. By using a penalty function that is invariant under index permutations, we can bias the potential energy surface in such a way that it obtains the characteristics of a structure seeker whose global minimum is the product. By performing a Minima Hopping based global optimization on this biased potential energy surface we can rapidly find intermediate states that lead into the global minimum. Based on this information we can then extract the full reaction pathway. We first demonstrate for a benchmark system, namely LJ38 that our method allows to preferentially find intermediate states that are relevant for the lowest energy reaction pathway and that we therefore need a much smaller number of intermediate states than previous methods to find the lowest energy reaction pathway. Finally we apply the method to two real systems, C60 and C20H20 and show that the found reaction pathway contains valuable information on how the system can be synthesized.

Using fingerprints used mainly in machine learning schemes of the potential energy surface, we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The fact that these long range effects exist for many configurations implies that atomistic simulation methods, such as force fields or modern machine learning schemes, that are based on locality assumptions, are limited in accuracy. We show that the basic driving mechanism for the long range effects is charge transfer. If the charge transfer is known, locality can be recovered for certain quantities such as the band structure energy.