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001024477 1001_ $$0P:(DE-Juel1)144723$$aDi Napoli, Edoardo$$b0$$eCorresponding author$$ufzj
001024477 245__ $$aComputing formation enthalpies through an explainable machine learning method: the case of lanthanide orthophosphates solid solutions
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001024477 520__ $$aIn the last decade, the use of AI in Condensed Matter physics has seen a steep increase in the number of problems tackled and methods employed. A number of distinct Machine Learning approaches have been employed in many different topics; from prediction of material properties to computation of Density Functional Theory potentials and inter-atomic force fields. In many cases, the result is a surrogate model which returns promising predictions but is opaque on the inner mechanisms of its success. On the other hand, the typical practitioner looks for answers that are explainable and provide a clear insight into the mechanisms governing a physical phenomena. In this study, we describe a proposal to use a sophisticated combination of traditional Machine Learning methods to obtain an explainable model that outputs an explicit functional formulation for the material property of interest. We demonstrate the effectiveness of our methodology in deriving a new highly accurate expression for the enthalpy of formation of solid solutions of lanthanide orthophosphates.
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001024477 536__ $$0G:(DE-Juel1)SDLQM$$aSimulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM)$$cSDLQM$$fSimulation and Data Laboratory Quantum Materials (SDLQM)$$x1
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001024477 7001_ $$0P:(DE-Juel1)178969$$aWu, Xinzhe$$b1$$ufzj
001024477 7001_ $$0P:(DE-Juel1)188288$$aBornhake, Thomas$$b2
001024477 7001_ $$0P:(DE-Juel1)137024$$aKowalski, Piotr M.$$b3$$ufzj
001024477 773__ $$0PERI:(DE-600)2823454-6$$a10.3389/fams.2024.1355726$$gVol. 10, p. 1355726$$p1355726$$tFrontiers in applied mathematics and statistics$$v10$$x2297-4687$$y2024
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