Accelerating materials property predictions using machine learning

Abstract

The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.

Figure 1. The machine (or statistical) learning methodology.

First, material motifs within a class are reduced to numerical fingerprint vectors. Next, a suitable measure of chemical (dis)similarity, or chemical distance, is used within a learning scheme—in this case, kernel ridge regression—to map the distances to properties.

Figure 2. Learning performance of chemo-structural fingerprint vectors.

Parity plots comparing property values computed using DFT against predictions made using learning algorithms trained using chemo-structural fingerprint vectors. Pearson's correlation index is indicated in each of the panels to quantify the agreement between the two schemes.

Figure 3. High throughput predictions and correlations from machine learning.

(a) The upper triangle presents a schematic of the atomistic model composed of repeat units with 8 building blocks. Populating each of he 8 blocks with one of the seven units leads to 29,365 systems. The matrix in the lower triangle depicts the Pearson's correlation index for each pair of the eight properties of the 8-block systems predicted using machine learning. (b) Panels p1 to p6 show the correlations between the band gap and six properties. The panel labels are also appropriately indexed in (a). The circle in panel p6 indicates systems with a simultaneously large dielectric constant and band gap.

Figure 4. Learning performance of electron charge density-based fingerprint vectors.

Parity plots comparing property values computed using DFT against predictions made using learning algorithms trained using electron density-based fingerprint vectors. The Fourier coefficients of the planar-averaged Kohn-Sham charge density are used to construct the fingerprint vector. Pearson's correlation index is indicated in each of the panels to quantify the agreement between the two schemes.