30 research outputs found
Harnessing quantum chemical bonding analysis descriptors for material property predictions
No full textExamining the bonding between their constituent atoms in crystalline materials has played a vital role in understanding material properties.[1–4] For instance, low thermal conductivity in materials is typically attributed to its anharmonicity, which has been reported to arise from strong antibonding interactions and local environment distortions.[5–7] The bonds in the material are often quantified in terms of bond strength and can be extracted from crystalline materials using density-based[8], energy-based[9], and orbital-based[10] methods. LOBSTER[11] is a program that relies on an orbital-based method to extract such bonding information by projecting the plane wave-based wave functions of modern density functional theory computations (DFT) onto a local atomic orbital basis. Since our goal was to use bonding analysis descriptors for material property predictions, we needed to first systematically generate large quantities of bonding analysis data. To streamline this process, we have developed a user-friendly workflow[12], which is now also part of the atomate2[13] package that can generate bonding information data extracted using the LOBSTER program for crystalline materials. This workflow requires only the structure as input from the user. Employing this workflow, we have generated for ~13000 crystalline compounds such bonding analysis data. To create new descriptors from these data, we use our package LobsterPy.[14] The curated descriptors span different types, including statistical representations of bonding characteristics for traditional ML algorithms (e.g., random forests), textual descriptions for large language models (LLMs), and structure graphs for graph neural networks (GNNs). These descriptors are then tested by employing them in several state-of-the-art ML algorithms and architectures to predict the mechanical, vibrational, and thermal properties of crystalline materials. Through this work, we are not only able to demonstrate how one can enhance the model’s predictive accuracy[15] by incorporating quantum chemical bonding-based descriptors alongside typical composition and structure-based descriptors but it also aids in uncovering relationships between bonding and materials properties on a larger scale, which was not possible before
A Quantum-Chemical Bonding Database for Solid-State Materials
No full textA 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
Linking quantum chemical bonding analysis descriptors to material property predictions
No full textExamining the bonding between their constituent atoms in crystalline materials has played a vital role in understanding material properties. For instance, low thermal conductivity in materials is typically attributed to its anharmonicity, which has been reported to arise from strong antibonding interactions and local environment distortions. Employing an automated for bonding analysis that we developed, we have generated for ~13000 crystalline compounds such bonding analysis data. To create new descriptors from these data automatically, we extended our package LobsterPy. The curated descriptors span different types, including statistical representations of bonding characteristics for traditional ML algorithms (e.g., random forests), textual descriptions for large language models (LLMs), and structure graphs for graph neural networks (GNNs). These descriptors are then tested by employing them in several state-of-the-art ML algorithms and architectures to predict the mechanical, vibrational, and thermal properties of crystalline materials. Through this work, we are not only able to demonstrate how one can enhance the model’s predictive accuracy by incorporating quantum chemical bonding-based descriptors alongside typical composition and structure-based descriptors, but it also aids in uncovering relationships between bonding and materials properties on a larger scale, which was not possible before
Quantum-Chemical Bonding Database (Unprocessed data : Part 1)
No full textThis data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials."
Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID)
Refer to README.md file instructions to reproduce the data
Quantum-Chemical Bonding Database (Unprocessed data : Part 6)
No full textThis data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials."
Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID
A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 2)
No full textThis database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary
A Quantum-Chemical Bonding Database for Solid-State Materials
No full textUnderstanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from
bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information
Quantum-Chemical Bonding Database (Unprocessed data : Part 4)
No full textThis data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials."
Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID
A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 1)
No full textThis database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary
Quantum-Chemical Bonding Database (Unprocessed data : Part 8)
No full textThis data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials."
Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID
