add AlchemBERT results

benchmarks/matbench_v0.1_AlchemBERT/info.json

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+{
+  "authors": "Xiaotong Liu, Xingchen Liu and Xiaodong Wen",
+  "algorithm": "AlchemBERT",
+  "algorithm_long": "Using natural language to describe a material structure or chemical formula, then employing BERT for regression or classification. Currently, only a very simple description method has been tested, and there is room for further improvement.",
+  "bibtex_refs": "@article{liu2024alchembert, title={AlchemBERT: Exploring Lightweight Language Models for Materials Informatics}, author={Liu, X and Liu, X and Wen, X}, year={2024}, journal={ChemRxiv}, note={This content is a preprint and has not been peer-reviewed}, doi={10.26434/chemrxiv-2024-r4dnl}, url={https://chemrxiv.org/engage/chemrxiv/article-details/67540a28085116a133a62b85}}",
+  "notes": "Please refer to the README of https://gitee.com/liuxiaotong15/alchemBERT",
+  "requirements": {"python":  ["fire==0.6.0", "lightning==2.2.3", "matbench==0.6", "matplotlib==3.7.0", "numpy==1.23.5", "pandas==1.5.3", "pymatgen==2024.10.22", "scikit_learn==1.2.1", "tensorflow==2.16.1", "torch==2.2.1+cu118", "transformers==4.40.1"]}
+}

benchmarks/matbench_v0.1_AlchemBERT/readme.py

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+"""
+This script contains code for AlchemBERT.
+Our code is hosted at https://gitee.com/liuxiaotong15/alchemBERT.
+
+If needed, you can contact us to obtain the trained models for each task 
+and each fold, totaling 25GB.
+"""
+
+if __name__ == "__main__":
+    print("Our code is hosted at https://gitee.com/liuxiaotong15/alchemBERT.")
+    print("If needed, you can contact us to obtain the pre-trained models for each task and each fold, totaling 25GB.")
+