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Found 6 papers, 5 papers with code
Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding
Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science.
A Transformer Model for Symbolic Regression towards Scientific Discovery
Once trained, we apply our best model to the SRSD datasets (Symbolic Regression for Scientific Discovery datasets) which yields state-of-the-art results using the normalized tree-based edit distance, at no extra computational cost.
Neural Structure Fields with Application to Crystal Structure Autoencoders
We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks.
SRSD: Rethinking Datasets of Symbolic Regression for Scientific Discovery
Symbolic Regression (SR) is a task of recovering mathematical expressions from given data and has been attracting attention from the research community to discuss its potential for scientific discovery.
Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
For each of the 120 SRSD datasets, we carefully review the properties of the formula and its variables to design reasonably realistic sampling ranges of values so that our new SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method can (re)discover physical laws from such datasets.
The second-order reduced density matrix method and the two-dimensional Hubbard model
In this paper, we establish the utility of the RDM method when employing the $P$, $Q$, $G$, $T1$ and $T2^\prime$ conditions in the two-dimension al Hubbard model case and we conduct a thorough study applying the $4\times 4$ Hubbard model employing a coefficients.
Strongly Correlated Electrons
