Equivariant representations for molecular Hamiltonians


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Nigam, Jigyasa</dc:creator>
  <dc:creator>Willatt, Michael J.</dc:creator>
  <dc:creator>Ceriotti, Michele</dc:creator>
  <dc:date>2021-12-09</dc:date>
  <dc:description>The application of machine learning to the modeling of materials and molecules has proven to be extremely successful in accelerating the understanding, design, and characterization of materials. A major factor in this success has been the development of representations of atomic structures that reflect physics-based symmetries of the underlying interactions. Most of the descriptions of atomic properties or even global observables rely on decompositions into atomic contributions that are subsequently learnt in an atom-centered framework. However, many quantities associated with quantum mechanical calculations, such as the single-particle Hamiltonian matrices written in an atomic-orbital basis, are associated with multiple atom-centers. 
Following the introduction of equivariant N-center structural descriptors, in the reference below, that generalize the very successful atom-centered density correlation features to the problem of learning properties indexed by N atoms, we present benchmarks showing how the construction can be applied to efficiently learn the matrix elements of the (effective) single-particle Hamiltonian in an atom-centered orbital basis. 
In this record, we include the dataset comprising the Fock and overlap matrices in the def2-SVP of 1000 distorted water molecules, up to 4500 ethanol structures, and a subset of QM7-CHNO molecules. We also provide scripts to generate the two-center representations and fit linear and sparse kernel models for the Hamiltonians.</dc:description>
  <dc:identifier>https://archive.materialscloud.org/record/2021.217</dc:identifier>
  <dc:identifier>doi:10.24435/materialscloud:2d-3e</dc:identifier>
  <dc:identifier>mcid:2021.217</dc:identifier>
  <dc:identifier>oai:materialscloud.org:1156</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Materials Cloud</dc:publisher>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>ERC</dc:subject>
  <dc:subject>MARVEL</dc:subject>
  <dc:subject>EPFL</dc:subject>
  <dc:subject>H2020</dc:subject>
  <dc:subject>N-center-representations</dc:subject>
  <dc:subject>machine learning hamiltonians</dc:subject>
  <dc:subject>equivariant representations</dc:subject>
  <dc:subject>hamiltonian learning</dc:subject>
  <dc:title>Equivariant representations for molecular Hamiltonians</dc:title>
  <dc:type>Dataset</dc:type>
</oai_dc:dc>