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https://github.com/dhw059/LLM-predictor
The strategy of material dataset based on Tokens, utilizing LLM to achieve material prediction, discovery, and design.
https://github.com/dhw059/LLM-predictor
Last synced: 3 days ago
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The strategy of material dataset based on Tokens, utilizing LLM to achieve material prediction, discovery, and design.
- Host: GitHub
- URL: https://github.com/dhw059/LLM-predictor
- Owner: dhw059
- Created: 2024-03-28T10:42:22.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-03-28T10:52:23.000Z (10 months ago)
- Last Synced: 2024-03-28T11:58:04.878Z (10 months ago)
- Language: Python
- Size: 2.36 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome_ai_agents - Llm-Predictor - The strategy of material dataset based on Tokens, utilizing LLM to achieve material prediction, discovery, and design. (Building / Datasets)
- awesome_ai_agents - Llm-Predictor - The strategy of material dataset based on Tokens, utilizing LLM to achieve material prediction, discovery, and design. (Building / Datasets)
README
# [LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions](https://doi.org/10.48550/arXiv.2310.14029)
This repository contains the implementation of the LLM-Prop model. LLM-Prop is an efficiently finetuned large language model (T5 encoder) on crystals text descriptions to predict their properties. Given a text sequence that describes the crystal structure, LLM-Prop encodes the underlying crystal representation from its text description and output its properties such as band gap and volume.
LLM-Prop architecture
## Installation
You can install LLM-Prop by following these steps:
```
git clone https://github.com/vertaix/LLM-Prop.git
cd LLM-Prop
conda create -n requirement.txt
conda activate
```
## Usage
### Training LLM-Prop from scratch
Add the following scripts to [llmprop_train.sh](https://github.com/vertaix/LLM-Prop/tree/main/scripts/llmprop_train.sh)
```bash
#!/usr/bin/env bash
TRAIN_PATH="data/samples/textedge_prop_mp22_train.csv"
VALID_PATH="data/samples/textedge_prop_mp22_valid.csv"
TEST_PATH="data/samples/textedge_prop_mp22_test.csv"
EPOCHS=5 # the default epochs is 200 to get the best performance
TASK_NAME="regression" # the task name can also be set to "classification"
PROPERTY="band_gap" # the property can also be set to "volume" or "is_gap_direct". Note that if the task name is set to classification, only "is_gap_direct" is allowed here. And if the task name is set to regression, only "band_gap" or "volume" is allowed here.
python llmprop_train.py \
--train_data_path $TRAIN_PATH \
--valid_data_path $VALID_PATH \
--test_data_path $TEST_PATH \
--epochs $EPOCHS \
--task_name $TASK_NAME \
--property $PROPERTY
```
Then run ``` bash scripts/llmprop_train.sh ```
### Evaluating the pretrained LLM-Prop
Add the following scripts to [llmprop_evaluate.sh](https://github.com/vertaix/LLM-Prop/tree/main/scripts/llmprop_evaluate.sh)
```bash
#!/usr/bin/env bash
TRAIN_PATH="data/samples/textedge_prop_mp22_train.csv"
TEST_PATH="data/samples/textedge_prop_mp22_test.csv"
TASK_NAME="regression" # the task name can also be set to "classification"
PROPERTY="band_gap" # the property can also be set to "volume" or "is_gap_direct". Note that if the task name is set to classification, only "is_gap_direct" is allowed here. And if the task name is set to regression, only "band_gap" or "volume" is allowed here.
CKPT_PATH="checkpoints/samples/$TASK_NAME/best_checkpoint_for_$PROPERTY.tar.gz" # path to the best model if the property to be predicted
python llmprop_evaluate.py \
--train_data_path $TRAIN_PATH \
--test_data_path $TEST_PATH \
--task_name $TASK_NAME \
--property $PROPERTY \
--checkpoint $CKPT_PATH
```
Then run ``` bash scripts/llmprop_evaluate.sh ```
## Data availability
This work is still under review and the data will be released after the review process.
## Citation
```bibtex
```