Data science and artificial intelligence are bringing about a revolution in our society, and materials science is no exception. In the future, it will be possible to design materials with tailored properties by relying almost completely on existing data. Indeed, combining data mining with cleverly designed machine learning (ML) algorithms can overcome the need to rely on large experimental and/or computational facilities for discovering new materials[i]. One promising field of application for these types of approaches is that of semiconductors. On the one hand, these materials are central to many technological applications. On the other hand, there is a constant need for improvement, for example for replacing toxic or rare elements while fulfilling optoelectronic and stability criteria.

In this contribution, we present our platform for the discovery of new chalcohalides, an emerging class of semiconducting materials which display the well-known excellent optoelectronic properties of metal halide perovskites while also being considerably more stable[ii]. Our approach consists of two steps. First, the platform connects to databases (AFLOW[iii], NOMAD[iv], and Materials Project[v]) to automatically find all the materials with user-defined constituting elements and properties. For chalcohalides, a key property for optical applications is the direct character of the band gap. Then, ML algorithms are trained to predict the stability, and the width and character of the band gap. The ML model is then applied to predict new compounds, and the prediction is then verified by quantum chemical simulations. We present representative examples of the application of our approach. We note that this protocol can be easily extended to the data mining of other semiconductors (an example on perovskites will be presented).

References:

[i] Keith J.A. et al. "Combining machine learning and computational chemistry for predictive insights into chemical systems." Chemical reviews 121.16 (2021): 9816-9872.

[ii] Ghorpade U.V. et al. "Emerging chalcohalide materials for energy applications." Chemical Reviews 123.1 (2022): 327-378.

[iii] Curtarolo S. et al. "AFLOW: An automatic framework for high-throughput materials discovery." Computational Materials Science 58 (2012): 218-226.

[iv] Draxl C. et al. "NOMAD: The FAIR concept for big data-driven materials science." Mrs Bulletin 43.9 (2018): 676-682.

[v] Jain A. et al. "Commentary: The Materials Project: A materials genome approach to accelerating materials innovation." APL materials 1.1 (2013).