Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism
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| Format: | Preprint |
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2024
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| _version_ | 1866913526267772928 |
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| author | Focassio, Bruno Domina, Michelangelo Patil, Urvesh Fazzio, Adalberto Sanvito, Stefano |
| author_facet | Focassio, Bruno Domina, Michelangelo Patil, Urvesh Fazzio, Adalberto Sanvito, Stefano |
| contents | Machine-learning models can be trained to predict the converged electron charge density of a density functional theory (DFT) calculation. In general, the value of the density at a given point in space is invariant under global translations and rotations having that point as a centre. Hence, one can construct locally invariant machine-learning density predictors. However, the widely used projector augmented wave (PAW) implementation of DFT requires the evaluation of the one-center augmentation contributions, that are not rotationally invariant. Building on our recently proposed Jacobi-Legendre charge-density scheme, we construct a covariant Jacobi-Legendre model capable of predicting the local occupancies needed to compose the augmentation charge density. Our formalism is then applied to the prediction of the energy barrier for the 1H-to-1T phase transition of two-dimensional MoS$_2$. With extremely modest training, the model is capable of performing a non-self-consistent nudged elastic band calculation at virtually the same accuracy as a fully DFT-converged one, thus saving thousands of self-consistent DFT steps. Furthermore, at variance with machine-learning force fields, the charge density is here available for any nudged elastic band image, so that we can trace the evolution of the electronic structure across the phase transition. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_08876 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism Focassio, Bruno Domina, Michelangelo Patil, Urvesh Fazzio, Adalberto Sanvito, Stefano Materials Science Strongly Correlated Electrons Machine-learning models can be trained to predict the converged electron charge density of a density functional theory (DFT) calculation. In general, the value of the density at a given point in space is invariant under global translations and rotations having that point as a centre. Hence, one can construct locally invariant machine-learning density predictors. However, the widely used projector augmented wave (PAW) implementation of DFT requires the evaluation of the one-center augmentation contributions, that are not rotationally invariant. Building on our recently proposed Jacobi-Legendre charge-density scheme, we construct a covariant Jacobi-Legendre model capable of predicting the local occupancies needed to compose the augmentation charge density. Our formalism is then applied to the prediction of the energy barrier for the 1H-to-1T phase transition of two-dimensional MoS$_2$. With extremely modest training, the model is capable of performing a non-self-consistent nudged elastic band calculation at virtually the same accuracy as a fully DFT-converged one, thus saving thousands of self-consistent DFT steps. Furthermore, at variance with machine-learning force fields, the charge density is here available for any nudged elastic band image, so that we can trace the evolution of the electronic structure across the phase transition. |
| title | Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism |
| topic | Materials Science Strongly Correlated Electrons |
| url | https://arxiv.org/abs/2408.08876 |