Electronic transport descriptors for the rapid screening of thermoelectric materials

June 2021: Materials Horizons

https://pubs.rsc.org/en/content/articlelanding/2021/mh/d1mh00751c

Combining data-driven screening and high-throughput calculations has emerged as a potential method for accelerating the discovery of novel materials for thermoelectric energy conversion. One way to increase the efficacy of successfully choosing a candidate material during the screening phase is through its evaluation using transport descriptors. These allow the rapid assessment of the potential of a material by utilizing the appropriate combination of one or more fundamental parameters. In this paper, we deploy a combination of data-driven screening from the Materials Project database and selected 12 potential candidates in the trigonal ABX2 chalcogenide family, followed by charge transport property simulations from first principles. The results suggest that carrier scattering processes in these materials are dominated by ionised impurities and polar optical phonons, contrary to the oft-assumed acoustic-phonon-dominated scattering. Using these data, we further derive ground-state transport descriptors for the carrier mobility and the thermoelectric powerfactor. We find that in addition to low carrier mass, high dielectric constant was found to be an important factor towards high carrier mobility. A quadratic correlation between dielectric constant and transport performance was established and further validated with literature. Looking ahead, dielectric constant can potentially be exploited as an independent criterion towards improved thermoelectric performance.

Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing

August 2018 - Joule : Cell Press

https://www.sciencedirect.com/science/article/pii/S2542435118302289

In collaboration with Prof. Tonio Buonassisi (MIT), we lay out the pathway for increasing the speed of discovery.

Successful materials innovations can transform society. However, materials research often involves long timelines and low success probabilities, dissuading investors who have expectations of shorter times from bench to business. A combination of emergent technologies could accelerate the pace of novel materials development by ten times or more, aligning the timelines of stakeholders (investors and researchers), markets, and the environment, while increasing return on investment. First, tool automation enables rapid experimental testing of candidate materials. Second, high-performance computing concentrates experimental bandwidth on promising compounds by predicting and inferring bulk, interface, and defect-related properties. Third, machine learning connects the former two, where experimental outputs automatically refine theory and help define next experiments. We describe state-of-the-art attempts to realize this vision and identify resource gaps. We posit that over the coming decade, this combination of tools will transformthe way we perform materials research, with considerable first-mover advantages at stake.

Inverse design of inorganic crystals using generalized invertible crystallographic representation

Deep learning has fostered many novel applications in materials informatics. However, the inverse design of inorganic crystals, i.e. generating new crystal structure with targeted properties, remains a grand challenge. An important ingredient for such generative models is an invertible representation that accesses the full periodic table. This is challenging due to limited data availability and the complexity of 3D periodic crystal structures. In this paper, we present a generalized invertible representation that encodes the crystallographic information into the descriptors in both real space and reciprocal space. Combining with a generative variational autoencoder (VAE), a wide range of crystallographic structures and chemistries with desired properties can be inversedesigned. We show that our VAE model predicts novel crystal structures that do not exist in the training and test database (Materials Project) with targeted formation energies and band gaps. We validate those predicted crystals by first-principles calculations. Finally, to design solids with practical applications, we address the sparse label problem by building a semi-supervised VAE and demonstrate its successful prediction of unique