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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.
A deep insight into the chemistry and nature of individual chemical bonds is essential for understanding materials. Bonding analysis is expected to provide important features for large-scale data analysis and machine learning of material properties. Such information on chemical bonds can be calculated using the LOBSTER (www.cohp.de) software package, which post-processes data from modern density functional theory computations by projecting plane wave-based wave functions onto a local atomic orbital basis. We have performed bonding analysis on 1520 compounds (insulators and semiconductors) using a fully automated workflow combining the VASP and LOBSTER software packages. We then automatically evaluated the data with LobsterPy (https://github.com/jageo/lobsterpy) and provide results as a database. The projected densities of states and bonding indicators are benchmarked on VASP projections and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine-learning model for phononic properties, which shows an increase in prediction accuracies by 27 % (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
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 will demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry. I will 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.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials.
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. This open-source software package powers the Materials Project.
In this particular contribution, the handling of obital-resolved "ICOHPLIST.lobster" files from Lobster was implemented in the software package (github handle: @JaGeo).
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.
Automation simplifies the use of computational materials science software and makes it accessible to a wide range of users. This enables high-throughput calcula-tionsand makesiteasier for non-specialists to enter computational materials science. However, in-creasing automation also poses threats that should be considered while interacting with automated procedures.
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.
In a laboratory test, transparent high‐density polyethylene (HDPE) jerrycans have been exposed to both UV radiation and 55 wt‐% nitric acid solution at (41 ± 2)°C, for up to 20 days. For comparison, UV radiant exposure (21 days) and nitric acid exposure (up to 6 weeks) were performed separately, at nearly equal temperatures. The damages are compared with FTIR spectroscopy in ATR and HT‐gel permeation chromatography(GPC) on a molecular level and with hydraulic internal pressure testing as a component test. For the used jerrycans, relevant oxidation can only be found after the combined exposure. This is caused by the decomposition of nitric acid into nitrous gases by UV radiation, which is also observed at lower concentrations (28 wt‐%). After 6 days of laboratory exposure, this is rated as critical, which corresponds to about 1/10 year in Central Europe, according to the UV radiant exposure. The gradual increase in oxidative damage shows the reproducibility of the test.
A damaging action of HNO3-55% only occurs in combination with its decomposition into nitrous gases, which can be caused by UV radiation. In a laboratory test, transparent HDPE jerrycans have been exposed to both UV radiation and 55 wt-% nitric acid solution at (41 ± 2)°C, for up to 20 days. For comparison, UV radiant exposure (21 days) and nitric acid exposure (up to 6 weeks) were performed separately, at nearly equal temperatures. The respective damages are compared with FTIR spectroscopy in ATR and HT-gel permeation chromatography (GPC) on a molecular level and with hydraulic internal pressure testing as a component test. For the used jerrycans, relevant oxidation can only be found after the combined exposure. The gradual increase in oxidative damage shows the good reproducibility of the lab exposure. The decomposition of nitric acid into nitrous gases by UV radiation – as well as the jerrycan oxidation – is also observed at lower HNO3 concentration (28 wt- %). Similar results are obtained after outdoor tests. Again, the damage occurs only after combined exposure, in contrast to the exposures to UV only and to HNO3 only, which were conducted in parallel. Outdoor exposures are most readily accepted as they represent possible end-use conditions. However, the reproducibility of these exposures is poor due to the large temporal variations in weather. There are also several safety risks, which is why the number of replicates remains limited. Since the outdoor and lab exposure tests show the same qualitative results, it is appropriate to conduct systematic studies in the laboratory. After 6 days of lab exposure, the oxidation damage is rated as critical, which corresponds to about 1/10 year in Central Europe, according to the UV radiant exposure. It should be noted that this amount can also occur in two sunny weeks.
The aim of this study was to evaluate cytotoxicity (WST-1 assay), LDH release (LDH assay) and genotoxicity (Comet assay) of three engineered TiO2-NPs with different shapes (bipyramids, rods, platelets) in comparison with two commercial TiO2-NPs (P25, food grade). After NPs characterization (SEM/T-SEM and DLS), biological effects of NPs were assessed on BEAS-2B cells in presence/absence of light. The cellular uptake of NPs was analyzed using Raman spectroscopy.
The cytotoxic effects were mostly slight. After light exposure, the largest cytotoxicity (WST-1 assay) was observed for rods; P25, bipyramids and platelets showed a similar effect; no effect was induced by food grade. No LDH release was detected, confirming the low effect on plasma membrane. Food grade and platelets induced direct genotoxicity while P25, food grade and platelets caused oxidative DNA damage. No genotoxic or oxidative damage was induced by bipyramids and rods. Biological effects were overall lower in darkness than after light exposure. Considering that only food grade, P25 and platelets (more agglomerated) were internalized by cells, the uptake resulted correlated with genotoxicity.
In conclusion, cytotoxicity of NPs was low and affected by shape and light exposure, while genotoxicity was influenced by cellular-uptake and aggregation tendency.
The growth kinetics of the adsorbed layer of poly(2-vinylpiridine) on silicon oxide is studied using a leaching technique which is based on the Guiselin brushes approach. The adsorbed layer is grown from a 200 nm thick P2VP film for several annealing time periods at different annealing temperatures. Then the film is solvent-leached, and the height of the remaining adsorbed layer is measured by atomic force microscopy. At the lowest annealing temperature only a linear growth regime is observed, followed by a plateau. Here, the molecular mobility of segments is too low to allow for a logarithmic growth. At higher annealing temperatures, both linear and logarithmic growth regimes are observed, followed by a plateau. At even higher annealing temperatures, the growth kinetics of the adsorbed layer changes.
A linear growth followed by logarithmic growth kinetics is observed for short annealing time periods. For longer annealing time periods, an upturn of the growth kinetics is observed. At the highest annealing temperature, only a logarithmic growth regime is found. The change in the growth kinetics is discussed by an alteration in the structure of the adsorbed layer. Moreover, the interaction between the polymer segments and the substrate becomes weaker due to both enthalpic and entropic effects. Therefore, at high annealing temperatures the polymer segments might more easily desorb from the substrate.
The composition of the surface layer in dependence from the distance of the polymer/air interface in thin films with thicknesses below 100 nm of miscible polymer blends in a spatial region of a few nanometers is not investigated completely. Here, thin films of the blend poly vinyl methyl ether) (PVME)/polystyrene (PS) with a composition of 25/75 wt% are investigated by Energy Resolved X-ray Photoelectron Spectroscopy (ER-XPS) at a synchrotron storage ring using excitation energies lower than 1 keV. By changing the energy of the photons the information depth is varied in the range from ca. 1 nm to 10 nm. Therefore, the PVME concentration could be estimated in dependence from the distance of the polymer/air interface for film thicknesses below 100 nm. Firstly, as expected for increasing information depth the PVME concentration decreases. Secondly, it was found that the PVME concentration at the surface has a complicated dependence on the film thickness. It increases with decreasing film thickness until 30 nm where a maximum is reached. For smaller film thicknesses the PVME concentration decreases. A simplified layer model is used to calculate the effective PVME concentration in the different spatial regions of the surface layer.