[Paper]
Authors: Jingqi Wang, Jiapeng Liu, Hongshuai Wang, Guolin Ke, Linfeng Zhang, Jianzhong Wu, Zhifeng Gao, Diannan Lu
Schematic illustration of the Uni-MOF framework
Uni-MOF is an innovative framework for large-scale, three-dimensional MOF representation learning, designed for universal multi-gas prediction. Specifically, Uni-MOF serves as a versatile "gas adsorption detector" for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Impressively, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.
- Uni-Core, check its Installation Documentation, this will take several minutes.
To use GPUs within docker you need to install nvidia-docker-2 first. Use the following command to pull the docker image:
docker pull dptechnology/unimol:latest-pytorch1.11.0-cuda11.3For the details of datasets, please refer to Table 1 in our paper.
We use LMDB to store data, you can use the following code snippets to read from the LMDB file.
import lmdb
import numpy as np
import os
import pickle
def read_lmdb(lmdb_path):
env = lmdb.open(
lmdb_path,
subdir=False,
readonly=True,
lock=False,
readahead=False,
meminit=False,
max_readers=256,
)
txn = env.begin()
keys = list(txn.cursor().iternext(values=False))
for idx in keys:
datapoint_pickled = txn.get(idx)
data = pickle.loads(datapoint_pickled)We use pickle protocol 5, so Python >= 3.8 is recommended.
| Model | File Size | Update Date | Download Link |
|---|---|---|---|
| nanoporous material pretrain | 303 MB | May 10 2023 | https://github.com/dptech-corp/Uni-MOF/releases/download/v0.1/unimof_pretrain_best.pt |
| Model | File Size | Update Date | Download Link |
|---|---|---|---|
| hMOF_MOFX_DB | 304 MB | May 10 2023 | https://github.com/dptech-corp/Uni-MOF/releases/download/v0.1/unimof_hMOF_MOFX_DB_finetune_best.pt |
| CoRE_MOFX_DB | 304 MB | May 10 2023 | https://github.com/dptech-corp/Uni-MOF/releases/download/v0.1/unimof_CoRE_MOFX_DB_finetune_best.pt |
| CoRE_MAP_DB | 168 MB | May 10 2023 | https://github.com/dptech-corp/Uni-MOF/releases/download/v0.1/unimof_CoRE_MAP_DB_fintune_best.pt |
- Uni-Core, check its Installation Documentation.
- rdkit==2021.09.5, install via
conda install -y -c conda-forge rdkit==2021.09.5
To use GPUs within docker you need to install nvidia-docker-2 first. Use the following command to pull the docker image:
docker pull dptechnology/unimol:latest-pytorch1.11.0-cuda11.3data_path="./examples/mof" # replace to your data path
save_dir="./save/" # replace to your save path
n_gpu=8
MASTER_PORT=$1
lr=3e-4
wd=1e-4
batch_size=8
update_freq=2
masked_token_loss=1
masked_coord_loss=1
masked_dist_loss=1
dist_threshold=5.0
minkowski_p=2.0
lattice_loss=1
x_norm_loss=0.01
delta_pair_repr_norm_loss=0.01
mask_prob=0.15
noise_type='uniform'
noise=1.0
seed=1
warmup_steps=10000
max_steps=100000
global_batch_size=`expr $batch_size \* $n_gpu \* $update_freq`
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
nohup python -m torch.distributed.launch --nproc_per_node=$n_gpu --master_port=$MASTER_PORT $(which unicore-train) $data_path --user-dir ./unimat --train-subset train --valid-subset valid \
--num-workers 8 --ddp-backend=c10d \
--task unimat --loss unimat --arch unimat_base \
--optimizer adam --adam-betas '(0.9, 0.99)' --adam-eps 1e-6 --clip-norm 1.0 --weight-decay $wd \
--lr-scheduler polynomial_decay --lr $lr --warmup-updates $warmup_steps --total-num-update $max_steps \
--update-freq $update_freq --seed $seed \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 \
--tensorboard-logdir ${save_dir}/tsb \
--max-update $max_steps --log-interval 1000 --log-format simple \
--save-interval-updates 1000 --validate-interval-updates 1000 --keep-interval-updates 10 --no-epoch-checkpoints \
--masked-token-loss $masked_token_loss --masked-coord-loss $masked_coord_loss --masked-dist-loss $masked_dist_loss \
--x-norm-loss $x_norm_loss --delta-pair-repr-norm-loss $delta_pair_repr_norm_loss --lattice-loss $lattice_loss \
--mask-prob $mask_prob --noise-type $noise_type --noise $noise --batch-size $batch_size --dist-threshold $dist_threshold --minkowski-p $minkowski_p \
--required-batch-size-multiple 1 --remove-hydrogen \
--save-dir $save_dir \
--find-unused-parameters \
>> "./logs/${save_dir}.log" &
The above setting is for 8 V100 GPUs, and the batch size is 128 (n_gpu * batch_size * update_freq). You may need to change batch_size or update_freq according to your environment.
data_path="./cross-system_gas_adsorption_property_prediction" # replace to your data path
save_dir="./save_finetune" # replace to your save path
n_gpu=8
MASTER_PORT=10086
task_name="CoRE" # property prediction task name
num_classes=1
exp_name="mof_v2"
weight_path="./weights/checkpoint.pt" # replace to your ckpt path
lr=3e-4
batch_size=8
epoch=50
dropout=0.2
warmup=0.06
update_freq=2
global_batch_size=`expr $batch_size \* $n_gpu \* $update_freq`
ckpt_dir="${exp_name}_${task_name}_trial"
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
nohup python -m torch.distributed.launch --nproc_per_node=$n_gpu --master_port=$MASTER_PORT $(which unicore-train) $data_path --user-dir ./unimat --task-name $task_name --train-subset train --valid-subset valid,test \
--num-workers 8 --ddp-backend=c10d \
--task unimof_v2 --loss mof_v2_mse --arch unimof_v2 \
--optimizer adam --adam-betas '(0.9, 0.99)' --adam-eps 1e-6 --clip-norm 1.0 \
--lr-scheduler polynomial_decay --lr $lr --warmup-ratio $warmup --max-epoch $epoch --batch-size $batch_size \
--update-freq $update_freq --seed 1 \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 \
--num-classes $num_classes --pooler-dropout $dropout \
--finetune-mol-model ./weights/$weight_path/checkpoint_last.pt \
--log-interval 500 --log-format simple \
--validate-interval-updates 500 --remove-hydrogen \
--save-interval-updates 1000 --keep-interval-updates 10 --no-epoch-checkpoints --keep-best-checkpoints 1 --save-dir ./logs_finetune/$save_dir \
--best-checkpoint-metric valid_r2 --maximize-best-checkpoint-metric \
> ./logs_finetune/$save_dir.log &
data_path="./cross-system_gas_adsorption_property_prediction" # replace to your data path
save_dir="./save_finetune" # replace to your save path
n_gpu=8
MASTER_PORT=10086
task_name="CoRE" # property prediction task name
num_classes=1
exp_name="mof_v2"
weight_path='NoPretrain'
lr=3e-4
batch_size=8
epoch=50
dropout=0.2
warmup=0.06
update_freq=2
global_batch_size=`expr $batch_size \* $n_gpu \* $update_freq`
ckpt_dir="${exp_name}_${task_name}_trial"
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
nohup python -m torch.distributed.launch --nproc_per_node=$n_gpu --master_port=$MASTER_PORT $(which unicore-train) $data_path --user-dir ./unimat --task-name $task_name --train-subset train --valid-subset valid,test \
--num-workers 8 --ddp-backend=c10d \
--task unimof_v2 --loss mof_v2_mse --arch unimof_v2 \
--optimizer adam --adam-betas '(0.9, 0.99)' --adam-eps 1e-6 --clip-norm 1.0 \
--lr-scheduler polynomial_decay --lr $lr --warmup-ratio $warmup --max-epoch $epoch --batch-size $batch_size \
--update-freq $update_freq --seed 1 \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 \
--num-classes $num_classes --pooler-dropout $dropout \
--log-interval 500 --log-format simple \
--validate-interval-updates 500 --remove-hydrogen \
--save-interval-updates 1000 --keep-interval-updates 10 --no-epoch-checkpoints --keep-best-checkpoints 1 --save-dir ./logs_finetune/$save_dir \
--best-checkpoint-metric valid_r2 --maximize-best-checkpoint-metric \
> ./logs_finetune/$save_dir.log &
The prediction of MOF structural feature could be taken as demo. It may take 4 hours to obtain the best model performance [coefficient of determination (R2)] for the test set.
| File | File Size | Update Date | Download Link |
|---|---|---|---|
| Data | 5.77 MB | May 10 2023 | https://github.com/dptech-corp/Uni-MOF/releases/download/v0.1/MOF_structure_data.zip |
| Weight | 303 MB | May 10 2023 | https://github.com/dptech-corp/Uni-MOF/releases/download/v0.1/CoRE_PLD_bset.pt |
data_path="./single-system_gas_adsorption_property_prediction" # replace to your data path
save_dir="./save_finetune" # replace to your save path
n_gpu=8
MASTER_PORT=10086
task_name="CoRE_PLD" # property prediction task name
num_classes=1
exp_name='mof_v1'
weight_path="./weights/checkpoint.pt" # replace to your ckpt path
lr=3e-4
batch_size=8
epoch=50
dropout=0.2
warmup=0.06
update_freq=2
global_batch_size=`expr $batch_size \* $n_gpu \* $update_freq`
export NCCL_ASYNC_ERROR_HANDLING=1
export OMP_NUM_THREADS=1
nohup python $(which unicore-train) $data_path --user-dir ./unimat --task-name $task_name --train-subset train --valid-subset valid,test \
--num-workers 8 --ddp-backend=c10d \
--task unimof_v1 --loss mof_v1_mse --arch unimat_base \
--optimizer adam --adam-betas '(0.9, 0.99)' --adam-eps 1e-6 --clip-norm 1.0 \
--lr-scheduler polynomial_decay --lr $lr --warmup-ratio $warmup --max-epoch $epoch --batch-size $batch_size \
--update-freq $update_freq --seed 1 \
--fp16 --fp16-init-scale 4 --fp16-scale-window 256 \
--num-classes $num_classes --pooler-dropout $dropout \
--finetune-from-model ./weights/$weight_path/checkpoint_last.pt \
--log-interval 100 --log-format simple \
--validate-interval 1 --remove-hydrogen \
--save-interval-updates 1000 --keep-interval-updates 10 --no-epoch-checkpoints --keep-best-checkpoints 1 --save-dir ./logs_finetune/$save_dir \
--best-checkpoint-metric valid_r2 --maximize-best-checkpoint-metric \
> ./logs_finetune/$save_dir.log &
Please kindly cite our papers if you use the data/code/model.
@article{wang2023metal,
title={Metal-organic frameworks meet Uni-MOF: a revolutionary gas adsorption detector},
author={Wang, Jingqi and Liu, Jiapeng and Wang, Hongshuai and Ke, Guolin and Zhang, Linfeng and Wu, Jianzhong and Gao, Zhifeng and Lu, Diannan},
year={2023}
}
This project is licensed under the terms of the MIT license. See LICENSE for additional details.
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