Taking the multiplicity inside the loop: active learning for structural and spin multiplicity elucidation of atomic clusters

• DOI: 10.1007/s00214-021-02820-2
• Publication Date: 2021-08-02T09:10:18Z
• Authors: Maicon Pierre Lou...; Lizandra Barrios ...; Jiří Hostaš; Patrizia Calaminici; Andreas M. Köster; Alain Tchagang; Dennis R. Salahub
• Keywords: machine learning; active learning; 0103 physical sciences; clusters; 02 engineering and technology; efficient global optimization; 0210 nano-technology; DFT; 01 natural sciences

Active learning (AL) has been successfully applied in materials science for the global optimization of clusters and defects in materials. Many important chemistry problems require the structural elucidation of molecules as a first step to the mechanistic elucidation of complex heterogeneous catalysis phenomena. Theoretical methods coupled with global optimization algorithms are successfully used for this purpose. However, it is challenging to find the global minimum structure together with the proper electronic spin multiplicity. In this work, we present an AL implementation for global optimization of atomic clusters where the spin multiplicity is considered in the search loop (SM@AL). The method was implemented in the QMLMaterial software, interfaced with the deMon2k program to perform local structure optimizations. In this work, we present applications of SM@AL for the global optimization, in terms of molecular structure and electronic spin of 3Al@Si11, where Si11 is doped by 3 Al, and Mo4C2 with spin multiplicities 2, 4 and 6 and 1, 3, 5, 7, 9 and 11, respectively.