A-RAFFLE—The Search for New Materials

Atomic scale structure prediction is a significant area of focus, yielding results such as the Materials Project [3] and tools such as AIRSS [1] and CALPYSO [2]. However, such approaches are focused on the isolated bulk whereas grain boundaries, interfaces and other phenomena dominate device development. Here, we demonstrate using the insertion of Mg and O into the interface between graphite layers, how interface physics can lead to unique material formation. We contrast how this differs from the bulk, and what the resultant new properties are. To do this, we have developed A-RAFFLE, our structure at interfaces prediction tool, built upon the ARTEMIS interface prediction software [4]. In A-RAFFLE, we have developed an effective combination of using multiple atomic potentials (2, 3 and 4 body) and simple machine learning to iteratively improve and develop our predictions of interface structures. Here, we demonstrate the base capability of A-RAFFLE as a structure prediction tool, using the test case of Mg and O into graphite interfaces, highlighting its strengths and limitations compared to other approaches.<br/>References<br/>[1] Chris J. Pickard and R. J. Needs. “High-Pressure Phases of Silane”. In:Phys. Rev. Lett.97 (4 July 2006), p. 045504.<br/>[2] Yanchao Wang et al. “Crystal structure prediction via particle-swarm op-timization”. In:Phys. Rev. B82 (9 Sept. 2010), p. 094116.<br/>[3] Anubhav Jain et al. “The Materials Project: A materials genome approachto accelerating materials innovation”. In:APL Materials1.1 (2013), p. 011002.issn: 2166532X.<br/>[4] Ned Thaddeus Taylor et al. “ARTEMIS: Ab initio restructuring tool en-abling the modelling of interface structures”. In:Computer Physics Com-munications257 (2020), p. 107515.issn: 0010-4655.