Understanding the Trend in Core-Shell Preferences for BimetallicNanoclusters: A Machine Learning Approach

Finding out the driving factors in core-shellpreference of nanoscale binary metal alloys is important due totheir ubiquitous presence in applications ranging from catalysis tobiomedical. We consider binary-alloyed metallic nanoparticlesencompassing a vast range of alkali, alkaline, basic, 3d, 4d, and 5dtransition metals, and p-block metals and determine the core-shellpreference by calculating the segregation energies of single-atomalloy clusters by density functional theory. Application of machinelearning to this large database, built on features characterizing theconstituents, leads to the identification of four key factors: (i)cohesive energy difference, (ii) atomic radius difference, (iii)coordination number difference, and (iv) magnetism, providing thecore-to-shell preference of a given constituent. Interestingly, therelative importance of one key feature over another is found to be decided by the metal type. Our analysis also predicts that, for verysmall and very large differences of cohesive energy of the constituents, instead of core-shell structure, mixed and Janus structures arestabilized, respectively. Our exhaustive study will be useful in designing bimetallic nanoalloys with specific chemical ordering of theconstituent species.