Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure

Pressure-induced Transformations of Dense Carbonyl Sulfide to Singly Bonded Amorphous Metallic Solid

The application of pressure, internal or external, transforms molecular solids into non-molecular extended network solids with diverse crystal structures and electronic properties. These transformations can be understood in terms of pressure-induced electron delocalization; however, the governing mechanisms are complex because of strong lattice strains, phase metastability and path dependent phase behaviors. Here, we present the pressure-induced transformations of linear OCS (R3m, Phase I) to bent OCS (Cm, Phase II) at 9 GPa; an amorphous, one-dimensional (1D) polymer at 20 GPa (Phase III); and an extended 3D network above ~35 GPa (Phase IV) that metallizes at ~105 GPa. These results underscore the significance of long-range dipole interactions in dense OCS, leading to an extended molecular alloy that can be considered a chemical intermediate of its two end members, CO(2) and CS(2)

Electron affinity of liquid water

The electron affinity of liquid water is a fundamental property which has not yet been accurately measured. Here, the authors predict this property by coupling path-integral molecular dynamics with ab initio potentials and electronic structure calculations, revisiting several estimates used in the literature