CateCom: A Practical Data-Centric Approach to Categorization of Computational Models.

The advent of data-driven science in the 21st century brought about the need for well-organized structured data and associated infrastructure able to facilitate the applications of artificial intelligence and machine learning. We present an effort aimed at organizing the diverse landscape of physics-based and data-driven computational models in order to facilitate the storage of associated information as structured data. We apply object-oriented design concepts and outline the foundations of an open-source collaborative framework that is (1) capable of uniquely describing the approaches in structured data, (2) flexible enough to cover the majority of widely used models, and (3) utilizes collective intelligence through community contributions.

Computing RPA Adsorption Enthalpies by Machine Learning Thermodynamic Perturbation Theory.

Correlated quantum-chemical methods for condensed matter systems, such as the random phase approximation (RPA), hold the promise of reaching a level of accuracy much higher than that of conventional density functional theory approaches. However, the high computational cost of such methods hinders their broad applicability, in particular for finite-temperature molecular dynamics simulations. We propose a method that couples machine learning techniques with thermodynamic perturbation theory to estimate finite-temperature properties using correlated approximations.

Effects of charge doping and constrained magnetization on the electronic structure of an FeSe monolayer.

The electronic structural properties in the presence of constrained magnetization and a charged background are studied for a monolayer of FeSe in non-magnetic, checkerboard- and striped-antiferromagnetic (AFM) spin configurations. First-principles techniques based on the pseudopotential density functional approach and the local spin density approximation are utilized. Our findings show that the experimentally observed shape of the Fermi surface is best described by the checkerboard AFM spin pattern.