A High-Throughput Database Of Phonons: Automation, Infrastructure, Machine Learning and Data-Driven Ferroelectric Materials Discovery

Databases of computed materials properties are revolutionizing the way materials science is done. A large body of materials properties such as formation energies, band gap, or elastic constants are nowadays available in major computational databases such as the Materials Project, AFLOW or OQMD.<br/>Here, we report on a large database of highly converged phonons obtained using density functional perturbation theory (DFPT). DFPT offers the advantage of not requiring supercells and providing direct access to derived quantities such as electron-phonon coupling matrices. We first discuss how the parameters (k-point, q-point densities, …) have been selected and the high-throughput infrastructure developed to automatically run these computations. We then present the data of more than 2,000 phonons and how it is available on the Materials Project (http://www.materialproject.org). A use case will be presented as well where new ferroelectric materials are detected using this phonon database. This approach identifies A4X2O anti-Ruddlesen-Popper materials as new ferroelectric and even magnetoelectric multiferroic materials. We will also report on our current work in automation of electron-phonon computations and the development of a database of transport properties including full electron-phonon interactions.<br/>The cost of high-quality phonons computations makes challenging to cover all the structures present in the Materials Project database. Machine learning is an obvious path to bypass full phonon computations and we will also highlight our recent work on predicting vibrational entropy using neural networks.