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update links

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  1. app.py +4 -4
app.py CHANGED
@@ -587,7 +587,7 @@ def main():
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  * UMA models are based on Density Functional Theory (DFT) training datasets. DFT simulations are a commonly used quantum chemistry method to simulate and understand behavior at the atomic scale.
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  * UMA models are large mixture-of-linear-experts graph networks models trained on billions of atoms across five open-science simulation datasets released by the FAIR Chemistry team over the past 5 years. This demo uses the small UMA model with 146M total parameters, 32 experts, and 6M active parameters at any time to predict across all of these domains.
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- Read the UMA paper for details or download the UMA model and FAIR Chemistry repository to use this yourself!
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  """
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  )
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  with gr.Accordion("Should I trust UMA?", open=False):
@@ -685,7 +685,7 @@ def main():
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  gr.Markdown(
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  """
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- OMol25 comprises over 100 million calculations covering small molecules, biomolecules, metal complexes, and electrolytes.
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  **Relevant applications:** Biology, organic chemistry, protein folding, small-molecule pharmaceuticals, organic liquid properties, homogeneous catalysis
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@@ -731,7 +731,7 @@ def main():
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  gr.Markdown(
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  """
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- OC20 comprises >100 million calculations of small molecules adsorbed on catalyst surfaces formed from materials in the Materials Project.
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  **Relevant applications:** Renewable energy, catalysis, fuel cells, energy conversion, sustainable fertilizer production, chemical refining, plastics synthesis/upcycling
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@@ -747,7 +747,7 @@ def main():
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  gr.Markdown(
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  """
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- ODAC23 comprises >10 million calculations of CO2/H2O molecules adsorbed in Metal Organic Frameworks sampled from various open databases like CoreMOF.
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  **Relevant applications:** Direct air capture, carbon capture and storage, CO2 conversion, catalysis
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  * UMA models are based on Density Functional Theory (DFT) training datasets. DFT simulations are a commonly used quantum chemistry method to simulate and understand behavior at the atomic scale.
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  * UMA models are large mixture-of-linear-experts graph networks models trained on billions of atoms across five open-science simulation datasets released by the FAIR Chemistry team over the past 5 years. This demo uses the small UMA model with 146M total parameters, 32 experts, and 6M active parameters at any time to predict across all of these domains.
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+ Read the UMA paper for details or [download the UMA model](https://huggingface.co/facebook/UMA) and [FAIR Chemistry repository](https://github.com/facebookresearch/fairchem) to use this yourself!
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  """
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  )
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  with gr.Accordion("Should I trust UMA?", open=False):
 
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  gr.Markdown(
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  """
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+ [OMol25](https://arxiv.org/abs/2505.08762) comprises over 100 million calculations covering small molecules, biomolecules, metal complexes, and electrolytes.
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  **Relevant applications:** Biology, organic chemistry, protein folding, small-molecule pharmaceuticals, organic liquid properties, homogeneous catalysis
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  gr.Markdown(
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  """
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+ [OC20](https://pubs.acs.org/doi/abs/10.1021/acscatal.0c04525) comprises >100 million calculations of small molecules adsorbed on catalyst surfaces formed from materials in the Materials Project. It was updated to total energy predictions for the UMA release.
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  **Relevant applications:** Renewable energy, catalysis, fuel cells, energy conversion, sustainable fertilizer production, chemical refining, plastics synthesis/upcycling
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  gr.Markdown(
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  """
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+ [ODAC23](https://pubs.acs.org/doi/full/10.1021/acscentsci.3c01629) comprises >10 million calculations of CO2/H2O molecules adsorbed in Metal Organic Frameworks sampled from various open databases like CoreMOF. It was updated to total energy predictions for the UMA release.
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  **Relevant applications:** Direct air capture, carbon capture and storage, CO2 conversion, catalysis
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