TY  - JOUR
AU  - Di Napoli, Edoardo
AU  - Wu, Xinzhe
AU  - Bornhake, Thomas
AU  - Kowalski, Piotr M.
TI  - Computing formation enthalpies through an explainable machine learning method: the case of lanthanide orthophosphates solid solutions
JO  - Frontiers in applied mathematics and statistics
VL  - 10
SN  - 2297-4687
CY  - Lausanne
PB  - Frontiers Media
M1  - FZJ-2024-02198
SP  - 1355726
PY  - 2024
AB  - In the last decade, the use of AI in Condensed Matter physics has seen a steep increase in the number of problems tackled and methods employed. A number of distinct Machine Learning approaches have been employed in many different topics; from prediction of material 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 into the mechanisms governing a physical phenomena. In this study, 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 lanthanide orthophosphates.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001202191300001
DO  - DOI:10.3389/fams.2024.1355726
UR  - https://juser.fz-juelich.de/record/1024477
ER  -