A Quantum-Chemical Bonding Database for Solid-State Materials
arXiv:2304.02726 · doi:10.1038/s41597-023-02477-5
Abstract
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 a local, 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 database structure of the bonding analysis database, which allows easy data retrieval, is also explained. 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.
arXiv comment20 pages, 6 main figures
References in corpus (12)
- A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
- Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
- AFLOW: An automatic framework for high-throughput materials discovery
- The Open Catalyst 2020 (OC20) Dataset and Community Challenges
- Atomistic Line Graph Neural Network for Improved Materials Property Predictions
- AiiDA: Automated Interactive Infrastructure and Database for Computational Science
- Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm
- High-Throughput Computational Screening of thermal conductivity, Debye temperature and Grüneisen parameter using a quasi-harmonic Debye Model
- MODNet -- accurate and interpretable property predictions for limited materials datasets by feature selection and joint-learning
- Anharmonicity in Thermal Insulators: An Analysis from First Principles
- A Quantum-Chemical Bonding Database for Solid-State Materials
- Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet