Add mlearn_Mo_test readme file

README.md

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+---
+configs:
+- config_name: default
+  data_files: "main/*.parquet"
+license: bsd-3-clause
+tags:
+- molecular dynamics
+- mlip
+- interatomic potential
+pretty_name: mlearn Mo test
+---
+### Cite this dataset  
+Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., and Ong, S. P. _mlearn Mo test_. ColabFit, 2023. https://doi.org/10.60732/3db3283a
+  ### View on the ColabFit Exchange  
+https://materials.colabfit.org/id/DS_l0b6iq3no012_0  
+# Dataset  Name  
+mlearn Mo test  
+### Description  
+A comprehensive DFT data set was generated for six elements - Li, Mo, Ni, Cu, Si, and Ge. These elements were chosen to span a variety of chemistries (main group metal, transition metal, and semiconductor), crystal structures (bcc, fcc, and diamond) and bonding types (metallic and covalent). This dataset comprises only the Mo configurations  
+<br>Additional details stored in dataset columns prepended with "dataset_".  
+### Dataset authors  
+Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Jörg Behler, Gábor Csányi, Alexander V. Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong  
+### Publication  
+https://doi.org/10.1021/acs.jpca.9b08723  
+### Original data link 
+https://github.com/materialsvirtuallab/mlearn  
+### License  
+BSD-3-Clause  
+### Number of unique molecular configurations  
+23  
+### Number of atoms  
+1189  
+### Elements included  
+Mo  
+### Properties included  
+energy, atomic forces, cauchy stress