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Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying 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. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties. One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, experimentalists have found a new low-temperature (LT) phase transition of canfieldite at 120K. Therefore, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density-functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Lattice parameters were overestimated by 2%, and thermal properties such as the constant-pressure heat capacity Cp are very close to experimental measurements. Our simulations also reveal a possible new phase transition at around 312 K. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with some argyrodites analogues, Ag8TQ6 (T = Si, Ge, Ti and Sn; Q = S, Se), finding a relationship between the anharmonicity and low thermal conductivity.
We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10\,K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory (DFT) in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI's determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1cm−1.
An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations 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.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties. One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, Slade group found a new low-temperature (LT) phase transition of canfieldite at 120K. Therefore, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density-functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Lattice parameters were overestimated by 2%, and thermal properties such as the constant-pressure heat capacity Cp are very close to experimental measurements. Our simulations also reveal a possible new phase transition at around 312 K. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with some argyrodites analogues, Ag8TS6 (T = Si, Ge, Ti and Sn), finding a relationship between the anharmonicity and low thermal conductivity.
Understanding and Machine Learning of Materials Properties with Quantum-Chemical Bonding Analysis
(2023)
Bonds and local atomic environments are crucial descriptors for material properties. They have been used to create design rules for materials and are used as features in machine learning of material properties. This talk will show how our recently developed tools, that automatically perform quantum chemical bond analysis and enable the study of chemical bonds and local atomic environments, accelerate and improve the development of such heuristics and machine-learned models for materials properties.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties.One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, a new low-temperature (LT) phase transition of canfieldite at 120K has been found. Here, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Thermal properties such as the constant-pressure heat capacity (Cp) and thermal conductivity are very close to experimental measurements. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with argyrodites analogues, Ag8XS6 (X = Sn, Si, Ge), to arrive at an improved
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.
Continuing progress in the field of X-ray scattering methods empowers scientists with new possibilities to capture the most important piece of information about the structure of the sample - its 3D electron density.
Although the first methods appeared almost a century ago, recovering the density structure of a sample is still very problematic. Most avail-able imaging techniques transform a 3D electron density of a realspace structure into the 2D Fourier Transform of the intensity of scattered waves in the reciprocal space. This process causes a loss of information.
Firstly, instead of a 3D sample, a 2D image is created, and secondly, the phase information of the scattered waves is lost. The latter is known as the ”phase problem” and poses a serious obstacle on a way to recover a 3D electron density. In this work, we draw attention to the problem of forward and inverse Small Angle X-Ray Scattering. In the first, forward, part, we rethink the existing pipelines to computationally simulate such scattering experiments. Although there are efficient implementations of fast Fourier transformation, they often have some drawbacks. For instance, to calculate a 3D fast Fourier transform it is required to place its density in the RAM. For high-resolution structures of size > 1024 3 , this becomes very problematic, as the whole density structure requires more than 16 GB of memory.
CUDA solution allows for a very fast and parallelizable implementation of high-resolution data on hundreds of last-generation machines.
Such computations are very pricy and inaccessible for most scientists.
To bypass this limitation, we propose a solution for a split-up 3D fast Fourier transform, which is implemented as a sequence of 2D and 1D operations. We compare our implementation on the simulated 3D shapes and show the result of a proof-of-concept on 4096 3 Metallorganic framework density structure. In the second, inverse problem, we train an invertible neural network, that given scattering data can predict the shape and its parameters. The architecture is built such, that the inverse problem is learned together with the forward process - the Fourier Transformation. We achieved very good results with this architecture, nonetheless, further testing is required, as the current training set only encompasses three simple shapes: sphere, hard sphere and cylinder.
All code to reproduce and analyze the results is available at https: //github.com/sofyalaski/SAXS-simulations.
Mechanochemical reactions are driven by the direct absorption of mechanical energy by a solid (often crystalline) material. Understanding how this energy is absorbed and ultimately causes a chemical transformation is essential for understanding the elementary stages of mechanochemical transformations. Using as a model system the energetic material LiN3 we here consider how vibrational energy flows through the crystal structure. By considering the compression response of the crystalline material we identify the partitioning of energy into an initial vibrational excitation. Subsequent energy flow is based on concepts of phonon–phonon scattering, which we calculate within a quasi-equilibrium model facilitated by phonon scattering data obtained from Density Functional Theory (DFT). Using this model we demonstrate how the moments (picoseconds) immediately following mechanical impact lead to significant thermal excitation of crystalline LiN3, sufficient to drive marked changes in its electronic structure and hence chemical reactivity. This work paves the way towards an ab initio approach to studying elementary processes in mechanochemical reactions involving crystalline solids.