% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{DiNapoli:1019412,
      author       = {Di Napoli, Edoardo and Wu, Xinzhe and Bornhake, Thomas and
                      Kowalski, Piotr M.},
      title        = {{C}omputing formation enthalpies through an explainable
                      machine learning method: the case of {L}anthanide
                      {O}rthophosphates solid solutions},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-05370},
      year         = {2023},
      abstract     = {In the last decade, the use of Machine and Deep Learning
                      (MDL) methods in Condensed Matter physics has seen a steep
                      increase in the number of problems tackled and methods
                      employed. A number of distinct MDL approaches have been
                      employed in many different topics; from prediction of
                      materials properties to computation of Density Functional
                      Theory potentials and inter-atomic force fields. In many
                      cases the result is a surrogate model which returns
                      promising predictions but is opaque on the inner mechanisms
                      of its success. On the other hand, the typical practitioner
                      looks for answers that are explainable and provide a clear
                      insight on the mechanisms governing a physical phenomena. In
                      this work, we describe a proposal to use a sophisticated
                      combination of traditional Machine Learning methods to
                      obtain an explainable model that outputs an explicit
                      functional formulation for the material property of
                      interest. We demonstrate the effectiveness of our
                      methodology in deriving a new highly accurate expression for
                      the enthalpy of formation of solid solutions of lanthanides
                      orthophosphates.},
      keywords     = {Computational Engineering, Finance, and Science (cs.CE)
                      (Other) / Materials Science (cond-mat.mtrl-sci) (Other) /
                      Machine Learning (cs.LG) (Other) / FOS: Computer and
                      information sciences (Other) / FOS: Physical sciences
                      (Other) / 68T05, 62J07 (Other)},
      cin          = {JSC / IEK-13},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IEK-13-20190226},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 1221 - Fundamentals
                      and Materials (POF4-122) / 1212 - Materials and Interfaces
                      (POF4-121) / Simulation and Data Laboratory Quantum
                      Materials (SDLQM) (SDLQM)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-1221 /
                      G:(DE-HGF)POF4-1212 / G:(DE-Juel1)SDLQM},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2303.03748},
      url          = {https://juser.fz-juelich.de/record/1019412},
}