Machine Learning for Potential Energy Surface Construction: A Benchmark Set
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Outline=======We assembled data from Potential Energy Surface (PES) construction invoking the n-mode representation. The database contains all single point calculations (SPCs) we carried out on the CCSD(F12*)(T)/cc-pVDZ-F12 level of theory. However, we also were interested in different delta-learning schemes:- DZERO Learning the CCSD(F12*)(T) energy- DSCF Learning the difference of CCSD(F12*)(T) and SCF (HF)- DMP2 Learning the difference of CCSD(F12*)(T) and RI-MP2- DMP2F12 Learning the difference of CCSD(F12*)(T) and RI-MP2-F12And we use the listed names in the files as identifier.Furthermore, the data is partitioned according to the mode combination level (MCL) used in the n-mode expansion- 1M: Only 1-mode couplings are considered- 2M: 1-mode and' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Machine Learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Outline=======We assembled data from Potential Energy Surface (PES) construction invoking the n-mode representation. The database contains all single point calculations (SPCs) we carried out on the CCSD(F12*)(T)/cc-pVDZ-F12 level of theory. However, we also were interested in different delta-learning schemes:- DZERO Learning the CCSD(F12*)(T) energy- DSCF Learning the difference of CCSD(F12*)(T) and SCF (HF)- DMP2 Learning the difference of CCSD(F12*)(T) and RI-MP2- DMP2F12 Learning the difference of CCSD(F12*)(T) and RI-MP2-F12And we use the listed names in the files as identifier.Furthermore, the data is partitioned according to the mode combination level (MCL) used in the n-mode expansion- 1M: Only 1-mode couplings are considered- 2M: 1-mode and' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Outline=======We assembled data from Potential Energy Surface (PES) construction invoking the n-mode representation. The database contains all single point calculations (SPCs) we carried out on the CCSD(F12*)(T)/cc-pVDZ-F12 level of theory. However, we also were interested in different delta-learning schemes:- DZERO Learning the CCSD(F12*)(T) energy- DSCF Learning the difference of CCSD(F12*)(T) and SCF (HF)- DMP2 Learning the difference of CCSD(F12*)(T) and RI-MP2- DMP2F12 Learning the difference of CCSD(F12*)(T) and RI-MP2-F12And we use the listed names in the files as identifier.Furthermore, the data is partitioned according to the mode combination level (MCL) used in the n-mode expansion- 1M: Only 1-mode couplings are considered- 2M: 1-mode and
Machine Learning