QUAM-AFM
<|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:'QUAM–AFM is the largest dataset of simulated Atomic Force Microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule,  24 3D image stacks, each consisting of constant-height images simulated for 10 tip-sample distances (in the relevant imaging range and spanning a variation of 1 Å (0.1 nanometers)) with one of the 24 different combination of AFM operational parameters, resulting in a total of 165 million images with a resolution of 256x256 pixels. The 3D stacks are especially appropriate to tackle the goal of chemical identification within AFM experiments by using deep learning techniques.The operational parameters include six different values for the cantilever oscillation amplitude (' . 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" : ["Condensed Matter Physics","Machine learning","Organic Chemistry"]} <|eot_id|>
 Here is the Description of a dataset. Description:'QUAM–AFM is the largest dataset of simulated Atomic Force Microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule,  24 3D image stacks, each consisting of constant-height images simulated for 10 tip-sample distances (in the relevant imaging range and spanning a variation of 1 Å (0.1 nanometers)) with one of the 24 different combination of AFM operational parameters, resulting in a total of 165 million images with a resolution of 256x256 pixels. The 3D stacks are especially appropriate to tackle the goal of chemical identification within AFM experiments by using deep learning techniques.The operational parameters include six different values for the cantilever oscillation amplitude (' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
QUAM–AFM is the largest dataset of simulated Atomic Force Microscopy (AFM) images generated from a selection of 685,513 molecules that span the most relevant bonding structures and chemical species in organic chemistry. QUAM-AFM contains, for each molecule,  24 3D image stacks, each consisting of constant-height images simulated for 10 tip-sample distances (in the relevant imaging range and spanning a variation of 1 Å (0.1 nanometers)) with one of the 24 different combination of AFM operational parameters, resulting in a total of 165 million images with a resolution of 256x256 pixels. The 3D stacks are especially appropriate to tackle the goal of chemical identification within AFM experiments by using deep learning techniques.The operational parameters include six different values for the cantilever oscillation amplitude (
Condensed Matter Physics; Machine learning; Organic Chemistry