Update README.md

README.md

@@ -19,7 +19,7 @@ datasets:
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This model is part of [this](https://chemrxiv.org/engage/chemrxiv/article-details/67adc1fc81d2151a0244de56) publication. It is used for translating chemical synthesis procedures given in natural language (en) to "action graphs", i.e., a simple markup language listing synthesis actions from a pre-defined controlled vocabulary along with the process parameters.
+This model is part of [this](https://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00063G) publication. It is used for translating chemical synthesis procedures given in natural language (en) to "action graphs", i.e., a simple markup language listing synthesis actions from a pre-defined controlled vocabulary along with the process parameters.
 
 ## Model Details
 
@@ -29,7 +29,7 @@ The model was fine-tuned on a dataset containing chemical synthesis procedures f
 
 
 - **Developed by:** Bastian Ruehle
-- **Funded by:** [Federal Institute fo Materials Research and Testing (BAM)](www.bam.de)
+- **Funded by:** [Federal Institute fo Materials Research and Testing (BAM)](https://www.bam.de)
 - **Model type:** LED (Longformer Encoder-Decoder)
 - **Language(s) (NLP):** en
 - **License:** [MIT](https://opensource.org/license/mit)
@@ -40,11 +40,11 @@ The model was fine-tuned on a dataset containing chemical synthesis procedures f
 <!-- Provide the basic links for the model. -->
 
 - **Repository:** The repository accompanying this model can be found [here](https://github.com/BAMresearch/MAPz_at_BAM/tree/main/Minerva-Workflow-Generator)
-- **Paper:** The papers accompanying this model can be found [here](https://chemrxiv.org/engage/chemrxiv/article-details/67adc1fc81d2151a0244de56) and [here](https://pubs.acs.org/doi/full/10.1021/acsnano.4c17504)
+- **Paper:** The papers accompanying this model can be found [here](https://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00063G) and [here](https://pubs.acs.org/doi/full/10.1021/acsnano.4c17504)
 
 ## Uses
 
-The model is integrated into a [node editor app](https://chemrxiv.org/engage/chemrxiv/article-details/67adc1fc81d2151a0244de56) for generating workflows from synthesis procedures given in natural language for the Self-Driving Lab platform [Minerva](https://pubs.acs.org/doi/full/10.1021/acsnano.4c17504).
+The model is integrated into a [node editor app](https://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00063G) for generating workflows from synthesis procedures given in natural language for the Self-Driving Lab platform [Minerva](https://pubs.acs.org/doi/full/10.1021/acsnano.4c17504).
 
 ### Direct Use
 
@@ -56,7 +56,7 @@ Even though it is not the intended way of using the model, it can be used "stand
 
 <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
 
-The model was intended to be used with the [node editor app](https://chemrxiv.org/engage/chemrxiv/article-details/67adc1fc81d2151a0244de56) for the Self-Driving Lab platform [Minerva](https://pubs.acs.org/doi/full/10.1021/acsnano.4c17504).
+The model was intended to be used with the [node editor app](https://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00063G) for the Self-Driving Lab platform [Minerva](https://pubs.acs.org/doi/full/10.1021/acsnano.4c17504).
 
 ### Out-of-Scope Use
 
@@ -98,9 +98,9 @@ if __name__ == '__main__':
     rawtext = """<Insert your Synthesis Procedure here>"""
 
     # model_id = 'bruehle/BigBirdPegasus_Llama'
-    # model_id = 'bruehle/LED-Base-16384_Llama'
+    model_id = 'bruehle/LED-Base-16384_Llama'  # or use any of the other models
     # model_id = 'bruehle/BigBirdPegasus_Chemtagger'
-    model_id = 'bruehle/LED-Base-16384_Chemtagger'  # or use any of the other models
+    # model_id = 'bruehle/LED-Base-16384_Chemtagger'
     
     if 'BigBirdPegasus' in model_id:
         max_length = 512
@@ -128,7 +128,7 @@ Models were trained on A100-80GB GPUs for 885’225 steps (5 epochs) on the trai
 
 #### Preprocessing
 
-More information on data pre- and postprocessing can be found [here](https://chemrxiv.org/engage/chemrxiv/article-details/67adc1fc81d2151a0244de56).
+More information on data pre- and postprocessing can be found [here](https://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00063G).
 
 
 #### Training Hyperparameters
@@ -145,7 +145,7 @@ More information on data pre- and postprocessing can be found [here](https://che
 
 <!-- This should link to a Dataset Card if possible. -->
 
-Example outputs for experimental procedures from the domains of materials science, organic chemistry, inorganic chemistry, and a patent that were not part of the training or evaluation dataset can be found [here](https://chemrxiv.org/engage/chemrxiv/article-details/67adc1fc81d2151a0244de56).
+Example outputs for experimental procedures from the domains of materials science, organic chemistry, inorganic chemistry, and a patent that were not part of the training or evaluation dataset can be found [here](https://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00063G).
 
 ## Technical Specifications
 
@@ -155,7 +155,7 @@ Longformer Encoder-Decoder Model for Text2Text/Seq2Seq Generation.
 
 ### Compute Infrastructure
 
-Trained on HPC GPU nodes of the [Federal Institute fo Materials Research and Testing (BAM)](www.bam.de).
+Trained on HPC GPU nodes of the [Federal Institute fo Materials Research and Testing (BAM)](https://www.bam.de).
 
 #### Hardware
 
@@ -171,13 +171,13 @@ Python 3.12
 
 **BibTeX:**
 
-@article{Ruehle_2025, title={Natural Language Processing for Automated Workflow and Knowledge Graph Generation in Self-Driving Labs}, DOI={10.26434/chemrxiv-2025-0p7xx}, journal={ChemRxiv}, author={Ruehle, Bastian}, year={2025}}
+@article{Ruehle_2025, title={Natural Language Processing for Automated Workflow and Knowledge Graph Generation in Self-Driving Labs}, DOI={10.1039/D5DD00063G}, journal={DigitalDiscovery}, author={Ruehle, Bastian}, year={2025}}
 
 @article{doi:10.1021/acsnano.4c17504, author = {Zaki, Mohammad and Prinz, Carsten and Ruehle, Bastian}, title = {A Self-Driving Lab for Nano- and Advanced Materials Synthesis}, journal = {ACS Nano}, volume = {19}, number = {9}, pages = {9029-9041}, year = {2025}, doi = {10.1021/acsnano.4c17504}, note ={PMID: 39995288}, URL = {https://doi.org/10.1021/acsnano.4c17504}, eprint = {https://doi.org/10.1021/acsnano.4c17504}}
 
 **APA:**
 
-Ruehle, B. (2025). Natural Language Processing for Automated Workflow and Knowledge Graph Generation in Self-Driving Labs. ChemRxiv. doi:10.26434/chemrxiv-2025-0p7xx
+Ruehle, B. (2025). Natural Language Processing for Automated Workflow and Knowledge Graph Generation in Self-Driving Labs. DigitalDiscovery. doi:10.1039/D5DD00063G
 
 Zaki, M., Prinz, C. & Ruehle, B. (2025). A Self-Driving Lab for Nano- and Advanced Materials Synthesis. ACS Nano, 19(9), 9029-9041. doi:10.1021/acsnano.4c17504