Understanding the structure of amorphous carbon and silicon with machine learning atomistic modeling

When we think about semiconductors the first ones to come to mind are silicon, germanium and the III-Vs (GaAs, InP, AlN, GaN, etc.), in their crystalline forms. In fact, the degree of crystallinity in these materials often dictates the quality of the devices that can be made with them. As an example, dislocation densities as low as 1000/cm2 can negatively affect the performance of GaAs LEDs. For reference, within a perpendicular section of (111)-oriented bulk GaAs, that’s a bit less than one in a trillion (1012) “bad” primitive unit cells. Typical overall defect densities (e.g., including point defects) are much higher, but still the number of “good” atoms is thousands of times larger than the number of “bad” atoms. With this in mind, one might be boggled to find out amorphous semiconductors can have useful properties of their own, despite being the equivalent of a continuous network of crystallographic defects.

On the way to understand the properties of an amorphous material, our first pit stop is understanding its atomic-scale structure. For this purpose, computational atomistic modeling tools are particularly useful, since many of the techniques commonly used to study the atomistic structure of crystals are not applicable to amorphous materials, precisely because they rely on the periodicity of the crystal lattice. Unfortunately, one of the most used computational approaches for materials modeling, density functional theory (DFT), is computationally too expensive to study the sheer structural complexity in real amorphous materials, in particular long-range structure for which many thousands or even millions of atoms need to be considered.

The introduction and popularization in recent years of machine learning (ML) based atomistic modeling and, in particular, ML potentials (MLPs), has enabled for the first time realistic studies of amorphous semiconductors with accuracy close to that of DFT. As two of the most important such materials, amorphous silicon (a-Si) and amorphous carbon (a-C) have been the target of much of these early efforts.

In a recent Topical Review in Semiconductor Science and Technology (provided Open Access via this link), I have tried to summarize our attempts to understand the structure of a-C and a-Si and highlight how MLPs allow us to peek at the structure of these materials and draw the connection between this structure and the emerging properties. The discussion is accompanied by a general description of atomistic modeling of a-C and a-Si and a brief introduction to MLPs, and it could be interesting to the material scientist curious about the modeling of amorphous materials or the DFT practicioner curious about what MLPs can do that DFT can’t.

At this stage, the field is still evolving (fast!) and I expect(/hope) this review will become obsolete soon, as more accurate and CPU-efficient atomistic ML techniques become commonplace. Especially, I expect the description of a-Si and a-C to rapidly evolve from the study of pure samples to the more realistic (and chemically complex) materials, containing unintentional defects as well as chemical functionalization. All in all, I am excited to witness in which direction the field will steer in the next 5-10 years. I promise to do my part and wait at least that much before writing the next review on the topic!

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