Utilizing Wyckoff Sites to Construct Machine-Learning-Driven Interatomic Potentials for Crystalline Materials: A Case Study on α-Alumina

Abstract

We present a methodology leveraging machine learning models to generate interatomic potentials for crystalline materials. This approach is rooted in the material's crystallography in question. Specifically, we tap into the occupied Wyckoff sites, extracting the defining features that encapsulate the atomic local order in the material. Our choice for the target variable is the formation energy per atom, derived from the total energy of the structure's representative cell. Our machine learning model's architecture depends on the occupied Wyckoff sites. The diversity of these occupied sites conditions the layering scheme within the model. Atoms occupying a particular Wyckoff site were modeled with the architecture and learning parameters linked to the respective layer. To illustrate our method, we developed an interatomic potential for atomic interactions in α-alumina. For training purposes, we generated the samples through quantum mechanical computations. The evaluation of the learned interatomic potential involved conducting molecular dynamics calculations on a 2 × 2 × 2 supercell, yielding formation energies per atom deviating by less than 1.0 meV from the quantum mechanics results. The methodology described here paves the way for further innovations, potentially ushering in the creation of interatomic potentials that can be utilized for more than one material. Moreover, this approach provides valuable insights into the feasibility of substituting atoms within a compound by focusing exclusively on the specific Wyckoff sites that they occupy.

Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2

“…In JPC C , the special issue papers describe ML and other data science techniques utilized in the scope of nanoparticles and nanostructures; surface and interface processes; electron, ion, and thermal transport; optic, electronic, and optoelectronic materials; and catalysts and catalysis; as well as energy conversion and storage materials and processes. As in the Parts A and B, a number of articles directly address either the development of new methods or the use of ML methods in new ways. − Several contributions relate to methods in the use or development of so-called machine learning potentials (MLP) or machine learning interaction potentials (MLIP), including a Perspective on improving these models authored by Maxson et al, as well as other contributions. − Interfaces and related phenomena are addressed in several articles. − Nanomaterials and their properties are also prominently featured. − Another large category in JPC C includes studies that address the calculation of properties of materials for wide-ranging applications. − …”