From a9511a67a63ae5058817f8f6409acefc96016228 Mon Sep 17 00:00:00 2001 From: Zhang Xianjie Date: Fri, 19 Aug 2022 13:01:51 +0800 Subject: [PATCH 1/2] The DP-GEN_handson updates from 0.10.3 to 0.10.6 --- .../DP-GEN/learnDoc/DP-GEN_handson.md | 208 ++++++++---------- 1 file changed, 95 insertions(+), 113 deletions(-) diff --git a/source/Tutorials/DP-GEN/learnDoc/DP-GEN_handson.md b/source/Tutorials/DP-GEN/learnDoc/DP-GEN_handson.md index d04eead..b35621d 100644 --- a/source/Tutorials/DP-GEN/learnDoc/DP-GEN_handson.md +++ b/source/Tutorials/DP-GEN/learnDoc/DP-GEN_handson.md @@ -1,5 +1,5 @@ -# Hands-on tutorial for DP-GEN (v0.10.3) +# Hands-on tutorial for DP-GEN (v0.10.6) ## Workflow of the DP-GEN DeeP Potential GENerator (DP-GEN) is a package that implements a concurrent learning scheme to generate reliable DP models. Typically, the DP-GEN workflow contains three processes: init, run, and autotest. @@ -49,7 +49,7 @@ For surface systems, execute ```sh $ dpgen init_surf param.json machine.json ``` -A detailed description for preparing initial data in the standard way can be found at ‘Init’ Section of the [DP-GEN's documentation](https://github.com/deepmodeling/dpgen/blob/master/README.md). +A detailed description for preparing initial data in the standard way can be found at 鈥業nit鈥� Section of the [DP-GEN's documentation](https://docs.deepmodeling.com/projects/dpgen/en/latest/). **Initial data of this tutorial** @@ -74,15 +74,15 @@ $ tree init -L 2 On the screen, you can see ```sh init -├── CH4.POSCAR -├── CH4.POSCAR.01x01x01 -│ ├── 00.place_ele -│ ├── 01.scale_pert -│ ├── 02.md -│ └── param.json -├── INCAR_methane.md -├── INCAR_methane.rlx -└── param.json +鈹溾攢鈹€ CH4.POSCAR +鈹溾攢鈹€ CH4.POSCAR.01x01x01 +鈹� 鈹溾攢鈹€ 00.place_ele +鈹� 鈹溾攢鈹€ 01.scale_pert +鈹� 鈹溾攢鈹€ 02.md +鈹� 鈹斺攢鈹€ param.json +鈹溾攢鈹€ INCAR_methane.md +鈹溾攢鈹€ INCAR_methane.rlx +鈹斺攢鈹€ param.json ``` - Folder CH4.POSCAR.01x01x01 contains the files generated by the DP-GEN init_bulk process. - INCAR_* and CH4.POSCAR are the standard INCAR and POSCAR files for VASP. @@ -110,11 +110,11 @@ We can perform the run process as we expect by specifying the keywords in param. #### param.json - The keywords in param.json can be split into 4 parts： + The keywords in param.json can be split into 4 parts锛� - System and data: used to specify the atom types, initial data, etc. -- Training: mainly used to specify tasks in the training step； -- Exploration: mainly used to specify tasks in the labeling step； +- Training: mainly used to specify tasks in the training step锛� +- Exploration: mainly used to specify tasks in the labeling step锛� - Labeling: mainly used to specify tasks in the labeling step. Here we introduce the main keywords in param.json, taking a gas-phase methane molecule as an example. @@ -217,7 +217,7 @@ Description of example: The training related keys specify the details of training tasks. "numb_models" specifies the number of models to be trained. "default_training_param" specifies the training parameters for `DeePMD-kit`. Here, 4 DP models will be trained. -The training part of DP-GEN is performed by DeePMD-kit, so the keywords here are the same as those of DeePMD-kit and will not be explained here. A detailed explanation of those keywords can be found at [DeePMD-kit’s documentation](https://docs.deepmodeling.com/projects/deepmd/en/master/). +The training part of DP-GEN is performed by DeePMD-kit, so the keywords here are the same as those of DeePMD-kit and will not be explained here. A detailed explanation of those keywords can be found at [DeePMD-kit鈥檚 documentation](https://docs.deepmodeling.com/projects/deepmd/en/master/). **Exploration** @@ -252,7 +252,7 @@ Description of keywords: |     "press" | list | Pressure (Bar) in MD |     "trj_freq" | int | Frequency of trajectory saved in MD. | |     "nsteps" | int | Running steps of MD. | -|     "ensembles" | str | Determining which ensemble used in MD, options include “npt” and “nvt”. | +|     "ensembles" | str | Determining which ensemble used in MD, options include 鈥渘pt鈥� and 鈥渘vt鈥�. | Description of example: @@ -276,7 +276,7 @@ Description of keywords: | Key | Type | Description | |-------------------|-----------------|--------------------------------------------------------------------------------------------------------------------------| -| "fp_style" | String | Software for First Principles. Options include “vasp”, “pwscf”, “siesta” and “gaussian” up to now. | +| "fp_style" | String | Software for First Principles. Options include 鈥渧asp鈥�, 鈥減wscf鈥�, 鈥渟iesta鈥� and 鈥済aussian鈥� up to now. | | "shuffle_poscar" | Boolean | | | "fp_task_max" | Integer | Maximum of structures to be calculated in 02.fp of each iteration. | | "fp_task_min" | Integer | Minimum of structures to calculate in 02.fp of each iteration. | @@ -455,9 +455,9 @@ dpgen.log INCAR_methane iter.000000 machine.json param.json record.dpgen ```sh $ tree iter.000000/ -L 1 ./iter.000000/ -├── 00.train -├── 01.model_devi -└── 02.fp +鈹溾攢鈹€ 00.train +鈹溾攢鈹€ 01.model_devi +鈹斺攢鈹€ 02.fp ``` - 00.train: several (default 4) DP models are trained on existing data. - 01.model_devi: new configurations are generated using the DP models obtained in 00.train. @@ -468,16 +468,16 @@ First, we check the folder `iter.000000`/ `00.train`. ```sh $ tree iter.000000/00.train -L 1 ./iter.000000/00.train/ -├── 000 -├── 001 -├── 002 -├── 003 -├── data.init -> /root/dpgen_example -├── data.iters -├── graph.000.pb -> 000/frozen_model.pb -├── graph.001.pb -> 001/frozen_model.pb -├── graph.002.pb -> 002/frozen_model.pb -└── graph.003.pb -> 003/frozen_model.pb +鈹溾攢鈹€ 000 +鈹溾攢鈹€ 001 +鈹溾攢鈹€ 002 +鈹溾攢鈹€ 003 +鈹溾攢鈹€ data.init -> /root/dpgen_example +鈹溾攢鈹€ data.iters +鈹溾攢鈹€ graph.000.pb -> 000/frozen_model.pb +鈹溾攢鈹€ graph.001.pb -> 001/frozen_model.pb +鈹溾攢鈹€ graph.002.pb -> 002/frozen_model.pb +鈹斺攢鈹€ graph.003.pb -> 003/frozen_model.pb ``` - Folder 00x contains the input and output files of the DeePMD-kit, in which a model is trained. @@ -486,17 +486,17 @@ We may randomly select one of them, like 000. ```sh $ tree iter.000000/00.train/000 -L 1 ./iter.000000/00.train/000 -├── checkpoint -├── frozen_model.pb -├── input.json -├── lcurve.out -├── model.ckpt-400000.data-00000-of-00001 -├── model.ckpt-400000.index -├── model.ckpt-400000.meta -├── model.ckpt.data-00000-of-00001 -├── model.ckpt.index -├── model.ckpt.meta -└── train.log +鈹溾攢鈹€ checkpoint +鈹溾攢鈹€ frozen_model.pb +鈹溾攢鈹€ input.json +鈹溾攢鈹€ lcurve.out +鈹溾攢鈹€ model.ckpt-400000.data-00000-of-00001 +鈹溾攢鈹€ model.ckpt-400000.index +鈹溾攢鈹€ model.ckpt-400000.meta +鈹溾攢鈹€ model.ckpt.data-00000-of-00001 +鈹溾攢鈹€ model.ckpt.index +鈹溾攢鈹€ model.ckpt.meta +鈹斺攢鈹€ train.log ``` - `input.json` is the settings for DeePMD-kit for the current task. @@ -511,21 +511,21 @@ Then, we check the folder iter.000000/ 01.model_devi. ```sh $ tree iter.000000/01.model_devi -L 1 ./iter.000000/01.model_devi/ -├── confs -├── graph.000.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.000.pb -├── graph.001.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.001.pb -├── graph.002.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.002.pb -├── graph.003.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.003.pb -├── task.000.000000 -├── task.000.000001 -├── task.000.000002 -├── task.000.000003 -├── task.000.000004 -├── task.000.000005 -├── task.000.000006 -├── task.000.000007 -├── task.000.000008 -└── task.000.000009 +鈹溾攢鈹€ confs +鈹溾攢鈹€ graph.000.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.000.pb +鈹溾攢鈹€ graph.001.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.001.pb +鈹溾攢鈹€ graph.002.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.002.pb +鈹溾攢鈹€ graph.003.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.003.pb +鈹溾攢鈹€ task.000.000000 +鈹溾攢鈹€ task.000.000001 +鈹溾攢鈹€ task.000.000002 +鈹溾攢鈹€ task.000.000003 +鈹溾攢鈹€ task.000.000004 +鈹溾攢鈹€ task.000.000005 +鈹溾攢鈹€ task.000.000006 +鈹溾攢鈹€ task.000.000007 +鈹溾攢鈹€ task.000.000008 +鈹斺攢鈹€ task.000.000009 ``` - Folder confs contains the initial configurations for LAMMPS MD converted from POSCAR you set in "sys_configs" of param.json. @@ -534,11 +534,11 @@ $ tree iter.000000/01.model_devi -L 1 ```sh $ tree iter.000000/01.model_devi/task.000.000001 ./iter.000000/01.model_devi/task.000.000001 -├── conf.lmp -> ../confs/000.0001.lmp -├── input.lammps -├── log.lammps -├── model_devi.log -└── model_devi.out +鈹溾攢鈹€ conf.lmp -> ../confs/000.0001.lmp +鈹溾攢鈹€ input.lammps +鈹溾攢鈹€ log.lammps +鈹溾攢鈹€ model_devi.log +鈹斺攢鈹€ model_devi.out ``` - `conf.lmp`, linked to `000.0001.lmp` in folder confs, serves as the initial configuration of MD. @@ -562,23 +562,23 @@ Finally, we check the folder iter.000000/ 02.fp. ``` $ tree iter.000000/02.fp -L 1 ./iter.000000/02.fp -├── data.000 -├── task.000.000000 -├── task.000.000001 -├── task.000.000002 -├── task.000.000003 -├── task.000.000004 -├── task.000.000005 -├── task.000.000006 -├── task.000.000007 -├── task.000.000008 -├── task.000.000009 -├── task.000.000010 -├── task.000.000011 -├── candidate.shuffled.000.out -├── POTCAR.000 -├── rest_accurate.shuffled.000.out -└── rest_failed.shuffled.000.out +鈹溾攢鈹€ data.000 +鈹溾攢鈹€ task.000.000000 +鈹溾攢鈹€ task.000.000001 +鈹溾攢鈹€ task.000.000002 +鈹溾攢鈹€ task.000.000003 +鈹溾攢鈹€ task.000.000004 +鈹溾攢鈹€ task.000.000005 +鈹溾攢鈹€ task.000.000006 +鈹溾攢鈹€ task.000.000007 +鈹溾攢鈹€ task.000.000008 +鈹溾攢鈹€ task.000.000009 +鈹溾攢鈹€ task.000.000010 +鈹溾攢鈹€ task.000.000011 +鈹溾攢鈹€ candidate.shuffled.000.out +鈹溾攢鈹€ POTCAR.000 +鈹溾攢鈹€ rest_accurate.shuffled.000.out +鈹斺攢鈹€ rest_failed.shuffled.000.out ``` - `POTCAR` is the input file for VASP generated according to `"fp_pp_files"` of param.json. @@ -658,21 +658,21 @@ After all iterations, we check the structure of dpgen_example/run ```sh $ tree ./ -L 2 ./ -├── dpgen.log -├── INCAR_methane -├── iter.000000 -│ ├── 00.train -│ ├── 01.model_devi -│ └── 02.fp -├── iter.000001 -│ ├── 00.train -│ ├── 01.model_devi -│ └── 02.fp -├── iter.000002 -│ └── 00.train -├── machine.json -├── param.json -└── record.dpgen +鈹溾攢鈹€ dpgen.log +鈹溾攢鈹€ INCAR_methane +鈹溾攢鈹€ iter.000000 +鈹� 鈹溾攢鈹€ 00.train +鈹� 鈹溾攢鈹€ 01.model_devi +鈹� 鈹斺攢鈹€ 02.fp +鈹溾攢鈹€ iter.000001 +鈹� 鈹溾攢鈹€ 00.train +鈹� 鈹溾攢鈹€ 01.model_devi +鈹� 鈹斺攢鈹€ 02.fp +鈹溾攢鈹€ iter.000002 +鈹� 鈹斺攢鈹€ 00.train +鈹溾攢鈹€ machine.json +鈹溾攢鈹€ param.json +鈹斺攢鈹€ record.dpgen ``` and contents of `dpgen.log`. @@ -689,27 +689,9 @@ $ cat cat dpgen.log | grep system ``` It can be found that 3010 structures are generated in `iter.000001`, in which no structure is collected for first-principle calculations. Therefore, the final models are not updated in iter.000002/00.train. - -### Auto-test - -To verify the accuracy of the DP model, users can calculate a simple set of properties and compare the results with those of a DFT or traditional empirical force field. DPGEN's autotest module supports the calculation of a variety of properties, such as - -- 00.equi:(default task) the equilibrium state； - -- 01.eos: the equation of state； - -- 02.elastic: the elasticity like Young's module； - -- 03.vacancy: the vacancy formation energy； - -- 04.interstitial: the interstitial formation energy； - -- 05.surf: the surface formation energy. - - ## Summary Now, users have learned the basic usage of the DP-GEN. For further information, please refer to the recommended links. -1. GitHub website：https://github.com/deepmodeling/dpgen -2. Papers：https://deepmodeling.com/blog/papers/dpgen/ +1. GitHub website锛歨ttps://github.com/deepmodeling/dpgen +2. Papers锛歨ttps://deepmodeling.com/blog/papers/dpgen/ From bdedf3f74d98b70ef0a2c6e754174c57dc0e5e77 Mon Sep 17 00:00:00 2001 From: Zhang Xianjie Date: Fri, 19 Aug 2022 13:34:32 +0800 Subject: [PATCH 2/2] The DP-GEN_handson updates from 0.10.3 to 0.10.6 --- .../DP-GEN/learnDoc/DP-GEN_handson.md | 188 +++++++++--------- 1 file changed, 94 insertions(+), 94 deletions(-) diff --git a/source/Tutorials/DP-GEN/learnDoc/DP-GEN_handson.md b/source/Tutorials/DP-GEN/learnDoc/DP-GEN_handson.md index b35621d..9a6f88e 100644 --- a/source/Tutorials/DP-GEN/learnDoc/DP-GEN_handson.md +++ b/source/Tutorials/DP-GEN/learnDoc/DP-GEN_handson.md @@ -49,7 +49,7 @@ For surface systems, execute ```sh $ dpgen init_surf param.json machine.json ``` -A detailed description for preparing initial data in the standard way can be found at 鈥業nit鈥� Section of the [DP-GEN's documentation](https://docs.deepmodeling.com/projects/dpgen/en/latest/). +A detailed description for preparing initial data in the standard way can be found at ‘Init’ Section of the [DP-GEN's documentation](https://docs.deepmodeling.com/projects/dpgen/en/latest/). **Initial data of this tutorial** @@ -74,15 +74,15 @@ $ tree init -L 2 On the screen, you can see ```sh init -鈹溾攢鈹€ CH4.POSCAR -鈹溾攢鈹€ CH4.POSCAR.01x01x01 -鈹� 鈹溾攢鈹€ 00.place_ele -鈹� 鈹溾攢鈹€ 01.scale_pert -鈹� 鈹溾攢鈹€ 02.md -鈹� 鈹斺攢鈹€ param.json -鈹溾攢鈹€ INCAR_methane.md -鈹溾攢鈹€ INCAR_methane.rlx -鈹斺攢鈹€ param.json +├── CH4.POSCAR +├── CH4.POSCAR.01x01x01 +│ ├── 00.place_ele +│ ├── 01.scale_pert +│ ├── 02.md +│ └── param.json +├── INCAR_methane.md +├── INCAR_methane.rlx +└── param.json ``` - Folder CH4.POSCAR.01x01x01 contains the files generated by the DP-GEN init_bulk process. - INCAR_* and CH4.POSCAR are the standard INCAR and POSCAR files for VASP. @@ -110,11 +110,11 @@ We can perform the run process as we expect by specifying the keywords in param. #### param.json - The keywords in param.json can be split into 4 parts锛� + The keywords in param.json can be split into 4 parts： - System and data: used to specify the atom types, initial data, etc. -- Training: mainly used to specify tasks in the training step锛� -- Exploration: mainly used to specify tasks in the labeling step锛� +- Training: mainly used to specify tasks in the training step； +- Exploration: mainly used to specify tasks in the labeling step； - Labeling: mainly used to specify tasks in the labeling step. Here we introduce the main keywords in param.json, taking a gas-phase methane molecule as an example. @@ -217,7 +217,7 @@ Description of example: The training related keys specify the details of training tasks. "numb_models" specifies the number of models to be trained. "default_training_param" specifies the training parameters for `DeePMD-kit`. Here, 4 DP models will be trained. -The training part of DP-GEN is performed by DeePMD-kit, so the keywords here are the same as those of DeePMD-kit and will not be explained here. A detailed explanation of those keywords can be found at [DeePMD-kit鈥檚 documentation](https://docs.deepmodeling.com/projects/deepmd/en/master/). +The training part of DP-GEN is performed by DeePMD-kit, so the keywords here are the same as those of DeePMD-kit and will not be explained here. A detailed explanation of those keywords can be found at [DeePMD-kit’s documentation](https://docs.deepmodeling.com/projects/deepmd/en/master/). **Exploration** @@ -252,7 +252,7 @@ Description of keywords: |     "press" | list | Pressure (Bar) in MD |     "trj_freq" | int | Frequency of trajectory saved in MD. | |     "nsteps" | int | Running steps of MD. | -|     "ensembles" | str | Determining which ensemble used in MD, options include 鈥渘pt鈥� and 鈥渘vt鈥�. | +|     "ensembles" | str | Determining which ensemble used in MD, options include “npt” and “nvt”. | Description of example: @@ -276,7 +276,7 @@ Description of keywords: | Key | Type | Description | |-------------------|-----------------|--------------------------------------------------------------------------------------------------------------------------| -| "fp_style" | String | Software for First Principles. Options include 鈥渧asp鈥�, 鈥減wscf鈥�, 鈥渟iesta鈥� and 鈥済aussian鈥� up to now. | +| "fp_style" | String | Software for First Principles. Options include “vasp”, “pwscf”, “siesta” and “gaussian” up to now. | | "shuffle_poscar" | Boolean | | | "fp_task_max" | Integer | Maximum of structures to be calculated in 02.fp of each iteration. | | "fp_task_min" | Integer | Minimum of structures to calculate in 02.fp of each iteration. | @@ -455,9 +455,9 @@ dpgen.log INCAR_methane iter.000000 machine.json param.json record.dpgen ```sh $ tree iter.000000/ -L 1 ./iter.000000/ -鈹溾攢鈹€ 00.train -鈹溾攢鈹€ 01.model_devi -鈹斺攢鈹€ 02.fp +├── 00.train +├── 01.model_devi +└── 02.fp ``` - 00.train: several (default 4) DP models are trained on existing data. - 01.model_devi: new configurations are generated using the DP models obtained in 00.train. @@ -468,16 +468,16 @@ First, we check the folder `iter.000000`/ `00.train`. ```sh $ tree iter.000000/00.train -L 1 ./iter.000000/00.train/ -鈹溾攢鈹€ 000 -鈹溾攢鈹€ 001 -鈹溾攢鈹€ 002 -鈹溾攢鈹€ 003 -鈹溾攢鈹€ data.init -> /root/dpgen_example -鈹溾攢鈹€ data.iters -鈹溾攢鈹€ graph.000.pb -> 000/frozen_model.pb -鈹溾攢鈹€ graph.001.pb -> 001/frozen_model.pb -鈹溾攢鈹€ graph.002.pb -> 002/frozen_model.pb -鈹斺攢鈹€ graph.003.pb -> 003/frozen_model.pb +├── 000 +├── 001 +├── 002 +├── 003 +├── data.init -> /root/dpgen_example +├── data.iters +├── graph.000.pb -> 000/frozen_model.pb +├── graph.001.pb -> 001/frozen_model.pb +├── graph.002.pb -> 002/frozen_model.pb +└── graph.003.pb -> 003/frozen_model.pb ``` - Folder 00x contains the input and output files of the DeePMD-kit, in which a model is trained. @@ -486,17 +486,17 @@ We may randomly select one of them, like 000. ```sh $ tree iter.000000/00.train/000 -L 1 ./iter.000000/00.train/000 -鈹溾攢鈹€ checkpoint -鈹溾攢鈹€ frozen_model.pb -鈹溾攢鈹€ input.json -鈹溾攢鈹€ lcurve.out -鈹溾攢鈹€ model.ckpt-400000.data-00000-of-00001 -鈹溾攢鈹€ model.ckpt-400000.index -鈹溾攢鈹€ model.ckpt-400000.meta -鈹溾攢鈹€ model.ckpt.data-00000-of-00001 -鈹溾攢鈹€ model.ckpt.index -鈹溾攢鈹€ model.ckpt.meta -鈹斺攢鈹€ train.log +├── checkpoint +├── frozen_model.pb +├── input.json +├── lcurve.out +├── model.ckpt-400000.data-00000-of-00001 +├── model.ckpt-400000.index +├── model.ckpt-400000.meta +├── model.ckpt.data-00000-of-00001 +├── model.ckpt.index +├── model.ckpt.meta +└── train.log ``` - `input.json` is the settings for DeePMD-kit for the current task. @@ -511,21 +511,21 @@ Then, we check the folder iter.000000/ 01.model_devi. ```sh $ tree iter.000000/01.model_devi -L 1 ./iter.000000/01.model_devi/ -鈹溾攢鈹€ confs -鈹溾攢鈹€ graph.000.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.000.pb -鈹溾攢鈹€ graph.001.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.001.pb -鈹溾攢鈹€ graph.002.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.002.pb -鈹溾攢鈹€ graph.003.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.003.pb -鈹溾攢鈹€ task.000.000000 -鈹溾攢鈹€ task.000.000001 -鈹溾攢鈹€ task.000.000002 -鈹溾攢鈹€ task.000.000003 -鈹溾攢鈹€ task.000.000004 -鈹溾攢鈹€ task.000.000005 -鈹溾攢鈹€ task.000.000006 -鈹溾攢鈹€ task.000.000007 -鈹溾攢鈹€ task.000.000008 -鈹斺攢鈹€ task.000.000009 +├── confs +├── graph.000.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.000.pb +├── graph.001.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.001.pb +├── graph.002.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.002.pb +├── graph.003.pb -> /root/dpgen_example/run/iter.000000/00.train/graph.003.pb +├── task.000.000000 +├── task.000.000001 +├── task.000.000002 +├── task.000.000003 +├── task.000.000004 +├── task.000.000005 +├── task.000.000006 +├── task.000.000007 +├── task.000.000008 +└── task.000.000009 ``` - Folder confs contains the initial configurations for LAMMPS MD converted from POSCAR you set in "sys_configs" of param.json. @@ -534,11 +534,11 @@ $ tree iter.000000/01.model_devi -L 1 ```sh $ tree iter.000000/01.model_devi/task.000.000001 ./iter.000000/01.model_devi/task.000.000001 -鈹溾攢鈹€ conf.lmp -> ../confs/000.0001.lmp -鈹溾攢鈹€ input.lammps -鈹溾攢鈹€ log.lammps -鈹溾攢鈹€ model_devi.log -鈹斺攢鈹€ model_devi.out +├── conf.lmp -> ../confs/000.0001.lmp +├── input.lammps +├── log.lammps +├── model_devi.log +└── model_devi.out ``` - `conf.lmp`, linked to `000.0001.lmp` in folder confs, serves as the initial configuration of MD. @@ -562,23 +562,23 @@ Finally, we check the folder iter.000000/ 02.fp. ``` $ tree iter.000000/02.fp -L 1 ./iter.000000/02.fp -鈹溾攢鈹€ data.000 -鈹溾攢鈹€ task.000.000000 -鈹溾攢鈹€ task.000.000001 -鈹溾攢鈹€ task.000.000002 -鈹溾攢鈹€ task.000.000003 -鈹溾攢鈹€ task.000.000004 -鈹溾攢鈹€ task.000.000005 -鈹溾攢鈹€ task.000.000006 -鈹溾攢鈹€ task.000.000007 -鈹溾攢鈹€ task.000.000008 -鈹溾攢鈹€ task.000.000009 -鈹溾攢鈹€ task.000.000010 -鈹溾攢鈹€ task.000.000011 -鈹溾攢鈹€ candidate.shuffled.000.out -鈹溾攢鈹€ POTCAR.000 -鈹溾攢鈹€ rest_accurate.shuffled.000.out -鈹斺攢鈹€ rest_failed.shuffled.000.out +├── data.000 +├── task.000.000000 +├── task.000.000001 +├── task.000.000002 +├── task.000.000003 +├── task.000.000004 +├── task.000.000005 +├── task.000.000006 +├── task.000.000007 +├── task.000.000008 +├── task.000.000009 +├── task.000.000010 +├── task.000.000011 +├── candidate.shuffled.000.out +├── POTCAR.000 +├── rest_accurate.shuffled.000.out +└── rest_failed.shuffled.000.out ``` - `POTCAR` is the input file for VASP generated according to `"fp_pp_files"` of param.json. @@ -658,21 +658,21 @@ After all iterations, we check the structure of dpgen_example/run ```sh $ tree ./ -L 2 ./ -鈹溾攢鈹€ dpgen.log -鈹溾攢鈹€ INCAR_methane -鈹溾攢鈹€ iter.000000 -鈹� 鈹溾攢鈹€ 00.train -鈹� 鈹溾攢鈹€ 01.model_devi -鈹� 鈹斺攢鈹€ 02.fp -鈹溾攢鈹€ iter.000001 -鈹� 鈹溾攢鈹€ 00.train -鈹� 鈹溾攢鈹€ 01.model_devi -鈹� 鈹斺攢鈹€ 02.fp -鈹溾攢鈹€ iter.000002 -鈹� 鈹斺攢鈹€ 00.train -鈹溾攢鈹€ machine.json -鈹溾攢鈹€ param.json -鈹斺攢鈹€ record.dpgen +├── dpgen.log +├── INCAR_methane +├── iter.000000 +│ ├── 00.train +│ ├── 01.model_devi +│ └── 02.fp +├── iter.000001 +│ ├── 00.train +│ ├── 01.model_devi +│ └── 02.fp +├── iter.000002 +│ └── 00.train +├── machine.json +├── param.json +└── record.dpgen ``` and contents of `dpgen.log`. @@ -692,6 +692,6 @@ It can be found that 3010 structures are generated in `iter.000001`, in which no ## Summary Now, users have learned the basic usage of the DP-GEN. For further information, please refer to the recommended links. -1. GitHub website锛歨ttps://github.com/deepmodeling/dpgen -2. Papers锛歨ttps://deepmodeling.com/blog/papers/dpgen/ +1. GitHub website：https://github.com/deepmodeling/dpgen +2. Papers：https://deepmodeling.com/blog/papers/dpgen/