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Chemical heuristics are essential to understanding molecules and materials in chemistry. The periodic table, atomic radii, and electronegativities are only a few examples. Initially, they have been developed by a combination of physical insight and a limited amount of data. It is now possible to test these heuristics and generate new ones using automation based on Materials Informatic tools like pymatgen and greater amounts of data from databases such as a Materials Project. In this session, I'll speak about heuristics and design rules based on coordination environments and the concept of chemical bonding. For example, we have tested the Pauling rules which describe the stability of materials based on coordination environments and their connections on 5000 oxides from the Materials Project. In addition, we have created automated processes for analyzing the chemical bonding situation in crystalline materials with Lobster (www.cohp.de) in order to discover new heuristics and design rules.
The stability of materials is a very important property for materials discovery. In recent years, we have explored several approaches for advancing the prediction of the stability of crystals (e.g., classical chemical heuristics and machine-learned interatomic potentials). For example, we have assessed a famous chemical heuristic – the Pauling rules – regarding their usefulness for structure prediction. They are only of limited predictive power, i.e. the rules 2 to 5 only work for 13 % of all tested oxides. Furthermore, we have shown that machine-learned interatomic potentials can be used to predict phonon properties of a range of silicon allotropes accurately and, therefore, they can also be used to assess the dynamic stability of materials. To do so, we have developed new schemes to build reference databases for machine learned interatomic potentials.
High-throughput computations are nowadays an established way to suggest new candidate materials for applications to experimentalists. Due to new packages for automation and access to databases of computed materials properties, these studies became more and more complex over the last years. Besides suggesting new candidate materials for applications, they also offer a way to understanding the materials properties based on chemical bonds. For example, we have recently used orbital-based bonding analysis to understand the results of high-throughput studies for spintronic materials, ferroelectric materials and photovoltaic materials in detail. To do so, we have developed Python tools for high-throughput bonding analysis with the programs VASP and Lobster (see www.cohp.de). They are based on the Python packages pymatgen, atomate, and custodian. This implementation will be discussed within the talk. We also expect that these tools offer possibilities to arrive at new descriptors based on chemical bonds for materials properties.
The talk „Automation in computational materials science“ deals with the current state of automation in the field of computational materials science. It illustrates how automation can, for example, be used to speed up the search for new ferroelectric materials and spintronic materials. Furthermore, it lists current tools for automation and challenges in the field.
Developments in density functional theory (DFT) calculations, their automation and therefore easier access to materials data have enabled ab initio high-throughput searches for new materials for numerous applications. These studies open up exciting opportunities to find new materials in a much faster way than based on experimental work alone. However, performing density functional theory calculations for several thousand materials can still be very time consuming. The use of, for example, faster chemical heuristics and machine-learned interatomic potentials would allow to consider a much larger number of candidate materials. In addition to DFT based high-throughput searches, the seminar will discuss two possible ways to accelerate high-throughput searches.
Using data analysis on the structures and coordination environments of 5000 oxides, we were able to investigate a chemical heuristic – the famous Pauling rules – regarding its usefulness for the fast prediction of stable materials.
We have also investigated how machine-learned interatomic potentials can be used to accelerate the prediction of (dynamically) stable materials. The use of these potentials makes vibrational properties accessible in a much faster way than based on DFT. Our results based on a newly developed potential for silicon allotropes showed excellent agreement with DFT reference data (agreement of the frequencies within 0.1-0.2 THz).
In addition, we have successfully used high-throughput calculations in the search for new candidate materials for spintronic applications and ferroelectrics