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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.15303 |
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| _version_ | 1866912770865233920 |
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| author | Patyukova, Elena Yin, Junwen Basak, Susmita Sanchez, Samuel Pinilla Elena, Alin Teobaldi, Gilberto |
| author_facet | Patyukova, Elena Yin, Junwen Basak, Susmita Sanchez, Samuel Pinilla Elena, Alin Teobaldi, Gilberto |
| contents | Performing density functional theory (DFT) calculations requires a careful choice of computational parameters to ensure convergence and obtain meaningful results. This represents a particularly important problem for high-throughput and agentic workflows, where due to computational cost, any additional convergence studies are preferably to be avoided. So, there is a need for tools and models which are able to predict DFT parameters from basic input information, such as a structure. In this work, we develop a machine learning approach to predict the appropriate k-point sampling in DFT calculations and generate the input files for Quantum Espresso self-consistent field calculations. To achieve this, we first generated a training dataset comprising over 20,000 materials, each with an energy convergence threshold of 1 meV/atom. Several ML models were evaluated for their ability to predict k-points distance, and uncertainty estimation was incorporated to guarantee that, for at least 85-95% of compounds, the predicted k-distance lies within the convergence region. The best-performing models are made publicly available through an open-access web application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15303 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic generation of input files with optimised k-point meshes for Quantum Espresso self-consistent field single point total energy calculations Patyukova, Elena Yin, Junwen Basak, Susmita Sanchez, Samuel Pinilla Elena, Alin Teobaldi, Gilberto Materials Science Performing density functional theory (DFT) calculations requires a careful choice of computational parameters to ensure convergence and obtain meaningful results. This represents a particularly important problem for high-throughput and agentic workflows, where due to computational cost, any additional convergence studies are preferably to be avoided. So, there is a need for tools and models which are able to predict DFT parameters from basic input information, such as a structure. In this work, we develop a machine learning approach to predict the appropriate k-point sampling in DFT calculations and generate the input files for Quantum Espresso self-consistent field calculations. To achieve this, we first generated a training dataset comprising over 20,000 materials, each with an energy convergence threshold of 1 meV/atom. Several ML models were evaluated for their ability to predict k-points distance, and uncertainty estimation was incorporated to guarantee that, for at least 85-95% of compounds, the predicted k-distance lies within the convergence region. The best-performing models are made publicly available through an open-access web application. |
| title | Automatic generation of input files with optimised k-point meshes for Quantum Espresso self-consistent field single point total energy calculations |
| topic | Materials Science |
| url | https://arxiv.org/abs/2512.15303 |