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update READMEs
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Hierarchical/README.md

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# Hierarchical training of stanene force field
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After the OTF training is complete, we use MGP force field in LAMMPS for long time-scale simulations.
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Snapshots are picked up from the LAMMPS trajectory and calculated in DFT for validation and re-training.
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1. `add_snap` in mgp-"n": Add snapshots from DFT calculations in mgp-"n-1"
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2. `train_hyps`: Train hyperparameters of GP
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3. `build_mgp`: Use optimized hyps to build MGP
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4. `lmp`: Run LAMMPS with MGP
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5. `uncertainties`: Predict uncertainties
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6. `dft`: Pick up three snapshots for DFT

OTF/README.md

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# On-the-fly training of the stanene force field
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1. `atom_setup.py` constructs the supercell of stanene
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2. `flare_setup.py` initializes parameters for FLARE force field
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3. `qe_setup.py` sets input parameters of Quantum Espresso (QE). Set to use 288 CPUs in total.
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4. `otf_run.py` sets parameters for OTF training and molecular dynamics. Set to run 100 ps in total.
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5. `postprocess.py` analyzes the OTF trajectory and generate a figure of mean square displacement
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To launch the training, use
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```
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python otf_run.py
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```

flare/README.md

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## FLARE source code
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This is the FLARE package folked from [flare](https://github.com/mir-group/flare)

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