Wide-ranging predictions of new stable compounds powered by recommendation engines.

The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed.

Accelerating the prediction of stable materials with machine learning.

Despite the rise in computing power, the large space of possible combinations of elements and crystal structure types makes large-scale high-throughput surveys of stable materials prohibitively expensive, especially for complex materials and materials subject to environmental conditions such as finite temperature. When physics-based computational methods and labor-intensive experiments are not feasible, machine learning (ML) methods can be a rapid and powerful alternative. Owing to a wealth of experimental and first-principles data as well as improved ML frameworks designed for materials modeling, ML is shown to be effective in predicting stability parameters and accelerating the discovery of new stable materials.

The role of ligands in the mechanical properties of Langmuir nanoparticle films.

Langmuir monolayers of ligand-capped inorganic nanoparticles exhibit rich morphologies under lateral compression such as wrinkling, folding, and multilayer nucleation. We demonstrate that the ligands play a crucial role in the mechanical properties of nanoparticle films by probing the morphology and anisotropic stress response during lateral compression of films with systematically varied ligand concentrations. Increasing the ligand concentration of the films past a threshold value inhibits monolayer wrinkling and folding in favor of multilayer formation, and sharply reduces the compressive and shear moduli.