Ingenieurwissenschaften und zugeordnete Tätigkeiten
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Organisationseinheit der BAM
We created a workflow that fully automates bonding analysis using Crystal Orbital Hamilton Populations, which are bond-weighted densities of states. This enables understanding of crystalline material properties based on chemical bonding information. To facilitate data analysis and machine-learning research, our tools include automatic plots, automated text output, and output in machine-readable format.
Chemical bonding and coordination environments are crucial descriptors of material properties. They have previously been applied to creating chemical design guidelines and chemical heuristics. They are currently being used as features in machine learning more and more frequently. I will discuss implementations and algorithms (ChemEnv and LobsterEnv) for identifying these coordination environments based on geometrical characteristics and chemical bond quantum chemical analysis. I'll demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry. I'll also show how these tools can be used to create new design guidelines and a new understanding of chemistry. To use quantum-chemical bonding analysis on a large-scale and for machine-learning approaches, fully automatic workflows and analysis tools have been developed. After presenting the capabilities of these tools, I will also point out how these developments relate to the general trend towards automation in the field of density functional based materials science.
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.
In the pursuit of discovering materials with desirable properties, extending the available material libraries is crucial. High-throughput simulations have become an integral part in designing new materials in the past decades. However, there is no straightforward way of distinguishing synthesizable materials from all the proposed candidates. This project focuses on employing AI-driven methods to estimate synthesizability of materials.
Up to now, material scientists and engineers have relied on domain knowledge as well as empirical heuristics to guess the stability and synthesizability of molecules and crystals. The famous Pauling rules of crystal stability are an example of such heuristics. However, after the accelerating material discovery in all the years since Pauling, these rules now fail to account for the stability of most known crystals. A new predictive set of heuristics for crystal stability/synthesizability is unlikely to be uncovered by human perception, given the magnitude and dimensionality of crystallographic data. Hence, a data-driven approach should be proposed to find a predictive model or set of heuristics which differentiate synthesizable crystal structures from the rest. The main challenge of this research problem is the lack of a negative set for classification. Here, there are two classes of data: the positive class which contains synthesizable materials and the negative class which contains materials which are not synthesizable. While the data from the positive class is simply the data of crystals which have been experimentally synthesized, we do not have access to data points which are certainly unsynthesizable. Strictly speaking, if an attempt of synthesizing a crystal fails, it does not necessarily follow that the crystal is not synthesizable. Also, there is no database available which contains the intended crystal structures of unsuccessful synthesis attempts.
This project proposes a semi-supervised learning scheme to predict crystal synthesizability. The ML model is trained on experimental and theoretical crystal data. The initial featurization focuses on local environments which is inspired by the Pauling Rules. The experimental data points are downloaded through the Pymatgen API from the Materials Project database which contains relaxed structures recorded in Inorganic Crystal Structure Database – ICSD. The theoretical data is queried from select databases accessible through the Optimade project’s API.
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.
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
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.
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.