Machine learning potential for interacting dislocations in the presence of free surfaces.

Computing the total energy of a system of N interacting dislocations in the presence of arbitrary free surfaces is a difficult task, requiring Finite Element (FE) numerical calculations. Worst, high accuracy requires very fine meshes in the proximity of each dislocation core. Here we show that FE calculations can be conveniently replaced by a Machine Learning (ML) approach. After formulating the elastic problem in terms of one and two-body terms only, we use Sobolev training to obtain consistent information on both energy and forces, fitted using a feed-forward neural network (NN) architecture. As an example, we apply the proposed methodology to corrugated, heteroepitaxial semiconductor films, searching for the minimum-energy dislocation distributions by using Monte Carlo. Importantly, the presence of an interaction cutoff allows for the application of the method to systems of different sizes without the need to repeat training. Millions of energy evaluations are performed, a task which would have been impossible by brute-force FE calculations. Finally, we show how forces can be exploited in running 2D ML-based dislocation dynamics simulations.

Stress-Induced Acceleration and Ordering in Solid-State Dewetting.

We report on the influence of elastic strain on solid-state dewetting. Using continuum modeling, we first study the consequences of elastic stress on the pinching of the film away from the triple line during dewetting. We find that elastic stress in the solid film decreases both the time and the distance at which the film pinches in such a way that the dewetting front is accelerated. In addition, the spatial organization of islands emerging from the dewetting process is affected by strain. As an example, we demonstrate that ordered arrays of quantum dots can be achieved from solid-state dewetting of a square island in the presence of elastic stress.

Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges.

In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials' discovery or develop new understanding of materials' behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.