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DTSTAMP:20210916T132448Z
LOCATION:Michel Mayor
DTSTART;TZID=Europe/Stockholm:20210705T163000
DTEND;TZID=Europe/Stockholm:20210705T170000
UID:submissions.pasc-conference.org_PASC21_sess119_msa278@linklings.com
SUMMARY:High Resolution Fingerprints to Detect Nonlocal Effects
DESCRIPTION:Minisymposium\n\nHigh Resolution Fingerprints to Detect Nonloc
 al Effects\n\nGoedecker\n\nFingerprints are a widely used tool to quantify
  the similarity/dissimilarity of atomic environments and they are used as 
 input into various machine learning schemes. I will first compare the reso
 lution power of some widely used fingerprints. Such a comparison already s
 hows that some standard fingerprints can not resolve important structural 
 differences. In addition the fingerprint distances are frequently not in a
 greement with chemical intuition about what important structural differenc
 es are. I introduce the sensitivity matrix and show that the analysis of t
 he eigenvalues and eigenvectors of this matrix helps to assess the perform
 ance of fingerprints. In particular, following eigenvectors belonging to t
 iny eigenvalues reveals that there are manifolds in configuration space on
  which many fingerprints are constant within a very small threshold. As a 
 consequence any machine learning scheme that <br /> is fed with such a fin
 gerprint will predict virtually identical physical properties even though 
 the environments is different and will therefore in general have different
  physical properties. Finally I used the Overlap Matrix (OM) fingerprint, 
 which does not suffer from the above mentioned problems, to detect charge 
 transfer in C60 clusters. The result shows that even in such a simple cova
 lent system there is a significant long range  charge transfer and th
 at it is there not possible to obtain the exact total energy of such a sys
 tem as the sum of local atomic energies.\n\nDomain: Chemistry and Material
 s
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