[Dataset request] Pb_Te_2023

[jvita]

Name

Josh Vita

Email

vita1@llnl.gov

Dataset name

Pb_Te_2023

Authors

Zhanpeng Gong, Jefferson Zhe Liu, Xiangdong Ding, Jun Sun, Junkai Deng

Links

• Paper: https://journals.aps.org/prb/pdf/10.1103/PhysRevB.108.134112
• Data: unknown; contact authors?

Dataset description

[[AI-generated description]]

The dataset used to train the model in this pdf consists of:

• AIMD trajectories: The authors performed ab initio molecular dynamics simulations of 2D PbTe with different strain and temperature conditions using DFT calculations. They extracted the atomic configurations, energies, and forces from the trajectories as the training data.
• DFT calculations: The authors also performed DFT calculations to obtain the static properties, energy landscapes, phonon dispersion, and polarization of 2D PbTe. These results were used to validate the accuracy of the model and to investigate the phase transition mechanisms.
• Size and diversity: The dataset contains about 40,000 frames of AIMD trajectories with various strain and temperature conditions, covering a wide phase space of 2D PbTe. The dataset is large and diverse enough to train a deep learning model with high accuracy and generalization.

Source: Conversation with Bing, 10/26/2023

File details

No response

Method

DFT

Method (other)

No response

Software

VASP

Software (other)

No response

Software version(s)

No response

Additional details

No response

Property types

Atomic forces, Potential energy

Other/additional property

No response

Property details

No response

Elements

No response

Number of Configurations

No response

Naming convention

No response

Configuration sets

No response

Configuration labels

No response

Distribution license

No response

Permissions

• [ ] I confirm that I have the necessary permissions to submit this dataset

[gpwolfe]

contacted corresponding author

[gpwolfe]

Second contact attempt