Add README.md

EdenGNN-Uni_abacus.ckpt

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-oid sha256:b955929f5808ff11c980e1fd0151ef69bc148946648385e5679f46096424d83f
-size 26543498

EdenGNN-Uni_aug.ckpt

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-version https://git-lfs.github.com/spec/v1
-oid sha256:bf96caf1916787562da607844c5cd297e92d477c7cb19d7534f7d9de1e293316
-size 26560322

EdenGNN-Uni_pseudo.ckpt

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-version https://git-lfs.github.com/spec/v1
-oid sha256:b9ea9e299a575c9260ce3841010803149d9b7bf68dba1871f8e43a153ab4d456
-size 26561290

ase_sort.dat

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config_uni_abacus.yaml

@@ -1,280 +0,0 @@
-# ------------------------------------------------------------------------------
-# EdenGNN Configuration Template
-#
-# Convention:
-# Key:   Value                        # [Tag] Description
-#
-#
-# Tags:
-# [set]: Must be set in your first usage.
-# [check]: Must be checked before running.
-# [check train]: Must be checked in the training stage.
-# [check predict]: Must be checked in the predicting stage.
-# [default]: Works well with default values, modify only if necessary.
-# ------------------------------------------------------------------------------
-
-run:
-  # [default] Global random number seed
-  seed: 42          
-
-  # [default] Hardware device for computation. Options: "cpu", "cuda:0", etc.
-  device: cuda:0       
-
-  # [default] Number of GPUs to use for data distributed training. Experimental usage.
-  num_gpus: 1      
-
-  # [check] "train" or "predict"        
-  mode: predict       
-
-  # [check] Task type. 0: density; 1: augmentation; 2: density & augmentation; 3: hamiltonian. Must match ckpt if any.
-  task: 0          
-
-  # [check] Wandb project     
-  project: edengnn      
-
-  # [check] Name of work, useful if using wandb online mode.
-  name: universal         
-
-  # [check] The final result is stored at: `run.save_dir/run.project/timestamp`.
-  save_dir:
-
-  # [default] Wandb log mode            
-  log: offline
-
-  # [check] path to ckpt file. used for prediction or restarting training
-  checkpoint: /path/to/EdenGNN-Uni_abacus.ckpt
-
-  # [default]
-  resume: False # True only when restarting an interupted training
-
-  # [default]
-  dtype: float32
-
-data:
-  # [check]: 
-  dft_software: "abacus" 
-
-  openmx:
-    # [set]:
-    path_template: absolute/path/to/EdenGNN-main/scripts/openmx/template.dat 
-
-    # [default]: Cutoff energy in [Hartree] for specifying the real space grid 
-    # density. See the scf.energycutoff tag in OpenMX.
-    encut: 220 
-
-    # [check predict]: number of cpus used for restart OpenMX calculations
-    num_proc: 64  
-              
-  vasp:
-    # [check] Should be consistent with the LMAXMIX tag of your VASP calculations
-    lmix_max: 6 
-
-    # [set]
-    path_template: absolute/path/to/EdenGNN-main/scripts/vasp/template.incar
-
-    # [default] in [eV]. will be used in the future to specify the grid shape
-    encut: 520
-
-    # [check train] Directory of scf calculations which contain sub directories 
-    # the name of which are consistent in data.path_train and data.path_val
-    dir:
-
-  abacus:
-    # [check train] Abacus 'suffix' keywords. used for reading the training directories
-    prefix: aiida
-
-    # [default] in [Rydberg]. See Abacus manual. 
-    ecutwfc: 100
-  
-  siesta:
-    # [check train] SIESTA 'SystemLabel' keywords. used for reading the training directories
-    prefix: aiida
-
-    # [check train] filename of the SIESTA output log
-    filename_out: siesta_out
-
-    # [default] in [Rydberg]. See SIESTA manual. 
-    meshcutoff: 300
-
-  # [check predict] the sampling density (in Ang^{-1}) in the Brillouin zone. N_i = |b_i| / dk. 0.15 is accurate enough.
-  dk_bz: 0.15     
-
-  # [check predict] the sampling density along high symmetry lines for plotting band structures.          
-  dk_band: 0.037            
-
-  # [check predict] If true, convert to primitive cell.
-  plot_band: True          
-
-  # [check train]
-  path_train: # sacd paths of working directories
-
-  # [check train]
-  path_val: # sacd paths of working directories
-
-  # [check predict] txt files storing the input SAD CHGCAR paths for inference
-  path_predict:
-
-  # [default]
-  num_workers: 4
-
-  # [check train] True if run.task = 1
-  use_bin: False 
-
-  # [default] Currently EdenGNN only supports batch size = 1. batch > 1 will 
-  # raise errors.
-  batch_size: 1
-
-optimize:
-  # [check train] loss_train = \sum_i weight_train_i * loss_i
-  # loss_val = \sum_i weight_val_i * loss_i
-  loss_dict:
-    grid_func_out: # charge density
-      weight_train: 1. 
-      weight_val: 1.
-      loss: "L1"
-    aug_tensor: # augmentation occupancy
-      weight_train: 0. 
-      weight_val: 0.
-      loss: "L1"
-    # this is deprecated. total charge errors are logged during training
-    total_charge:
-      #weight_train: 0.0 # deprecated
-      weight_val: 0.0
-      loss: "L2"
-    operator:
-      weight_train: 1.0
-      weight_val: 1.0
-      loss: "L1"
-
-  # [check train]
-  lr: 0.005
-  task: ${run.task}
-
-  # [default] Freeze the parameters of the MLPs of the RBFs. Experimental usage
-  freeze_basis: False
-
-  # [default] Log step of training loss.
-  log_every_n_steps: 20
-
-  # [default] used for training universal models
-  val_check_interval_steps:
-
-  # [default] Large than 1 means DDP training is on. Experimental usage.
-  accumulate_grad_batches: 1
-
-  # [default] Set scheduler.
-  scheduler: 
-    type: ReduceLROnPlateau
-    ReduceLROnPlateau:
-      factor: 0.5 # 
-      patience: 10 # 
-      min_lr: 0.00002
-      threshold: 0.00001
-  max_epochs: 3000
-  max_steps: 1000000 
-
-  # [default] Set optimizer.
-  optimizer:
-    type: AdamW
-    AdamW:
-      epsilon: 1e-8
-      beta1: 0.99
-      beta2: 0.999
-
-  # [default] 0 default; 1 for debug. deprecated
-  # log_level: 0
-
-  # [default]
-  gradient_clip_val: 0.0    
-
-model:
-  compile: False
-  task: ${run.task}
-  lmix_max: ${data.vasp.lmix_max}
-
-  # [check predict] Whether or not predict total charge density. Set True for 
-  # predict VASP CHGCAR. Otherwise set it False.
-  use_sad: False 
-
-  # [check train] Maximum number of probe points per training chunk. Should be
-  # set close to the OOM value to improve speed.
-  # (depends on your GPU, 20,000,000 works for A800 GPU which has 80G VRAM).
-  chunk_size_train: 1000000
-
-  # [check] Maximum number of probe points per prediction chunk.
-  # (depends on your GPU, 20,000,000 works for A800 GPU which has 80G VRAM).
-  chunk_size_predict: 1000000
-
-  atom:
-    # [default]
-    l_max: 6
-
-    # [default]
-    num_types: 100
-
-    edge:
-      # [default]
-      n_radial_basis: 8
-
-      # [default]
-      radial_type: Bessel
-
-      # [default] The maximum distance between neighbor atoms.
-      cutoff: 4.0
-    conv:
-      # [default]
-      num_conv_layers: 3
-
-      # [default]
-      num_radial_filter_neurons: 64
-
-      # [default]
-      num_radial_filter_layers: 2   
-
-      # [default] Irreps of node features in the graph convolutions.
-      irreps_node_features: 64x0e+64x0o+20x1o+20x1e+16x2o+16x2e+8x3o+8x3e+6x4o+6x4e+5x5e+5x5o+4x6e+4x6o
-  probe:
-    # [default]
-    l_max: 6 
-
-    edge:
-      # [default]: The radius in [Angstrom] which determines the neighbor grids 
-      # for atoms.
-      cutoff: 3.0 
-
-      # [default]
-      n_radial_basis: 8 
-
-      # [default]
-      n_z_embedding_dim: 8 
-
-      # [default]
-      n_z_embedding_neurons: 20 # 
-
-      # [default]
-      n_z_embedding_layers: 2 
-
-    conv: 
-      # [default]
-      num_radial_filter_neurons: 20 
-
-      # [default]
-      num_radial_filter_layers: 2 
-
-      # [default] the number of radial basis for each angular quantum number.
-      n_channels: 4 
-  operator:
-    basis: [0, 0, 1, 1, 2, 2, 3]
-    offsite:
-      symmetrize: False
-      cutoff: 12.0
-      n_radial_basis: 8
-      num_radial_filter_layers: 2
-      num_radial_filter_neurons: 64
-
-
-
-wandb:
-  project: ${run.project}
-  name: ${run.name}
-  mode: ${run.log}

config_uni_aug.yaml

@@ -1,280 +0,0 @@
-# ------------------------------------------------------------------------------
-# EdenGNN Configuration Template
-#
-# Convention:
-# Key:   Value                        # [Tag] Description
-#
-#
-# Tags:
-# [set]: Must be set in your first usage.
-# [check]: Must be checked before running.
-# [check train]: Must be checked in the training stage.
-# [check predict]: Must be checked in the predicting stage.
-# [default]: Works well with default values, modify only if necessary.
-# ------------------------------------------------------------------------------
-
-run:
-  # [default] Global random number seed
-  seed: 42          
-
-  # [default] Hardware device for computation. Options: "cpu", "cuda:0", etc.
-  device: cuda:0       
-
-  # [default] Number of GPUs to use for data distributed training. Experimental usage.
-  num_gpus: 1      
-
-  # [check] "train" or "predict"        
-  mode: predict       
-
-  # [check] Task type. 0: density; 1: augmentation; 2: density & augmentation; 3: hamiltonian. Must match ckpt if any.
-  task: 1          
-
-  # [check] Wandb project     
-  project: edengnn      
-
-  # [check] Name of work, useful if using wandb online mode.
-  name: universal         
-
-  # [check] The final result is stored at: `run.save_dir/run.project/timestamp`.
-  save_dir:
-
-  # [default] Wandb log mode            
-  log: offline
-
-  # [check] path to ckpt file. used for prediction or restarting training
-  checkpoint: /path/to/EdenGNN-Uni_aug.ckpt
-
-  # [default]
-  resume: False # True only when restarting an interupted training
-
-  # [default]
-  dtype: float32
-
-data:
-  # [check]: 
-  dft_software: "vasp" 
-
-  openmx:
-    # [set]:
-    path_template: absolute/path/to/EdenGNN-main/scripts/openmx/template.dat 
-
-    # [default]: Cutoff energy in [Hartree] for specifying the real space grid 
-    # density. See the scf.energycutoff tag in OpenMX.
-    encut: 220 
-
-    # [check predict]: number of cpus used for restart OpenMX calculations
-    num_proc: 64  
-              
-  vasp:
-    # [check] Should be consistent with the LMAXMIX tag of your VASP calculations
-    lmix_max: 6 
-
-    # [set]
-    path_template: absolute/path/to/EdenGNN-main/scripts/vasp/template.incar
-
-    # [default] in [eV]. will be used in the future to specify the grid shape
-    encut: 520
-
-    # [check train] Directory of scf calculations which contain sub directories 
-    # the name of which are consistent in data.path_train and data.path_val
-    dir:
-
-  abacus:
-    # [check train] Abacus 'suffix' keywords. used for reading the training directories
-    prefix: aiida
-
-    # [default] in [Rydberg]. See Abacus manual. 
-    ecutwfc: 100
-  
-  siesta:
-    # [check train] SIESTA 'SystemLabel' keywords. used for reading the training directories
-    prefix: aiida
-
-    # [check train] filename of the SIESTA output log
-    filename_out: siesta_out
-
-    # [default] in [Rydberg]. See SIESTA manual. 
-    meshcutoff: 300
-
-  # [check predict] the sampling density (in Ang^{-1}) in the Brillouin zone. N_i = |b_i| / dk. 0.15 is accurate enough.
-  dk_bz: 0.15     
-
-  # [check predict] the sampling density along high symmetry lines for plotting band structures.          
-  dk_band: 0.037            
-
-  # [check predict] If true, convert to primitive cell.
-  plot_band: True          
-
-  # [check train]
-  path_train: # sacd paths of working directories
-
-  # [check train]
-  path_val: # sacd paths of working directories
-
-  # [check predict] txt files storing the input SAD CHGCAR paths for inference
-  path_predict:
-
-  # [default]
-  num_workers: 4
-
-  # [check train] True if run.task = 1
-  use_bin: False 
-
-  # [default] Currently EdenGNN only supports batch size = 1. batch > 1 will 
-  # raise errors.
-  batch_size: 1
-
-optimize:
-  # [check train] loss_train = \sum_i weight_train_i * loss_i
-  # loss_val = \sum_i weight_val_i * loss_i
-  loss_dict:
-    grid_func_out: # charge density
-      weight_train: 1. 
-      weight_val: 1.
-      loss: "L1"
-    aug_tensor: # augmentation occupancy
-      weight_train: 0. 
-      weight_val: 0.
-      loss: "L1"
-    # this is deprecated. total charge errors are logged during training
-    total_charge:
-      #weight_train: 0.0 # deprecated
-      weight_val: 0.0
-      loss: "L2"
-    operator:
-      weight_train: 1.0
-      weight_val: 1.0
-      loss: "L1"
-
-  # [check train]
-  lr: 0.005
-  task: ${run.task}
-
-  # [default] Freeze the parameters of the MLPs of the RBFs. Experimental usage
-  freeze_basis: False
-
-  # [default] Log step of training loss.
-  log_every_n_steps: 20
-
-  # [default] used for training universal models
-  val_check_interval_steps:
-
-  # [default] Large than 1 means DDP training is on. Experimental usage.
-  accumulate_grad_batches: 1
-
-  # [default] Set scheduler.
-  scheduler: 
-    type: ReduceLROnPlateau
-    ReduceLROnPlateau:
-      factor: 0.5 # 
-      patience: 10 # 
-      min_lr: 0.00002
-      threshold: 0.00001
-  max_epochs: 3000
-  max_steps: 1000000 
-
-  # [default] Set optimizer.
-  optimizer:
-    type: AdamW
-    AdamW:
-      epsilon: 1e-8
-      beta1: 0.99
-      beta2: 0.999
-
-  # [default] 0 default; 1 for debug. deprecated
-  # log_level: 0
-
-  # [default]
-  gradient_clip_val: 0.0    
-
-model:
-  compile: False
-  task: ${run.task}
-  lmix_max: ${data.vasp.lmix_max}
-
-  # [check predict] Whether or not predict total charge density. Set True for 
-  # predict VASP CHGCAR. Otherwise set it False.
-  use_sad: False 
-
-  # [check train] Maximum number of probe points per training chunk. Should be
-  # set close to the OOM value to improve speed.
-  # (depends on your GPU, 20,000,000 works for A800 GPU which has 80G VRAM).
-  chunk_size_train: 1000000
-
-  # [check] Maximum number of probe points per prediction chunk.
-  # (depends on your GPU, 20,000,000 works for A800 GPU which has 80G VRAM).
-  chunk_size_predict: 1000000
-
-  atom:
-    # [default]
-    l_max: 6
-
-    # [default]
-    num_types: 100
-
-    edge:
-      # [default]
-      n_radial_basis: 8
-
-      # [default]
-      radial_type: Bessel
-
-      # [default] The maximum distance between neighbor atoms.
-      cutoff: 4.0
-    conv:
-      # [default]
-      num_conv_layers: 3
-
-      # [default]
-      num_radial_filter_neurons: 64
-
-      # [default]
-      num_radial_filter_layers: 2   
-
-      # [default] Irreps of node features in the graph convolutions.
-      irreps_node_features: 64x0e+64x0o+20x1o+20x1e+16x2o+16x2e+8x3o+8x3e+6x4o+6x4e+5x5e+5x5o+4x6e+4x6o
-  probe:
-    # [default]
-    l_max: 6 
-
-    edge:
-      # [default]: The radius in [Angstrom] which determines the neighbor grids 
-      # for atoms.
-      cutoff: 4.0 
-
-      # [default]
-      n_radial_basis: 8 
-
-      # [default]
-      n_z_embedding_dim: 8 
-
-      # [default]
-      n_z_embedding_neurons: 20 # 
-
-      # [default]
-      n_z_embedding_layers: 2 
-
-    conv: 
-      # [default]
-      num_radial_filter_neurons: 20 
-
-      # [default]
-      num_radial_filter_layers: 2 
-
-      # [default] the number of radial basis for each angular quantum number.
-      n_channels: 4 
-  operator:
-    basis: [0, 0, 1, 1, 2, 2, 3]
-    offsite:
-      symmetrize: False
-      cutoff: 12.0
-      n_radial_basis: 8
-      num_radial_filter_layers: 2
-      num_radial_filter_neurons: 64
-
-
-
-wandb:
-  project: ${run.project}
-  name: ${run.name}
-  mode: ${run.log}

config_uni_density.yaml

@@ -1,280 +0,0 @@
-# ------------------------------------------------------------------------------
-# EdenGNN Configuration Template
-#
-# Convention:
-# Key:   Value                        # [Tag] Description
-#
-#
-# Tags:
-# [set]: Must be set in your first usage.
-# [check]: Must be checked before running.
-# [check train]: Must be checked in the training stage.
-# [check predict]: Must be checked in the predicting stage.
-# [default]: Works well with default values, modify only if necessary.
-# ------------------------------------------------------------------------------
-
-run:
-  # [default] Global random number seed
-  seed: 42          
-
-  # [default] Hardware device for computation. Options: "cpu", "cuda:0", etc.
-  device: cuda:0       
-
-  # [default] Number of GPUs to use for data distributed training. Experimental usage.
-  num_gpus: 1      
-
-  # [check] "train" or "predict"        
-  mode: predict       
-
-  # [check] Task type. 0: density; 1: augmentation; 2: density & augmentation; 3: hamiltonian. Must match ckpt if any.
-  task: 0          
-
-  # [check] Wandb project     
-  project: edengnn      
-
-  # [check] Name of work, useful if using wandb online mode.
-  name: universal         
-
-  # [check] The final result is stored at: `run.save_dir/run.project/timestamp`.
-  save_dir:
-
-  # [default] Wandb log mode            
-  log: offline
-
-  # [check] path to ckpt file. used for prediction or restarting training
-  checkpoint: /path/to/EdenGNN-Uni_density.ckpt
-
-  # [default]
-  resume: False # True only when restarting an interupted training
-
-  # [default]
-  dtype: float32
-
-data:
-  # [check]: 
-  dft_software: "vasp" 
-
-  openmx:
-    # [set]:
-    path_template: absolute/path/to/EdenGNN-main/scripts/openmx/template.dat 
-
-    # [default]: Cutoff energy in [Hartree] for specifying the real space grid 
-    # density. See the scf.energycutoff tag in OpenMX.
-    encut: 220 
-
-    # [check predict]: number of cpus used for restart OpenMX calculations
-    num_proc: 64  
-              
-  vasp:
-    # [check] Should be consistent with the LMAXMIX tag of your VASP calculations
-    lmix_max: 6 
-
-    # [set]
-    path_template: absolute/path/to/EdenGNN-main/scripts/vasp/template.incar
-
-    # [default] in [eV]. will be used in the future to specify the grid shape
-    encut: 520
-
-    # [check train] Directory of scf calculations which contain sub directories 
-    # the name of which are consistent in data.path_train and data.path_val
-    dir:
-
-  abacus:
-    # [check train] Abacus 'suffix' keywords. used for reading the training directories
-    prefix: aiida
-
-    # [default] in [Rydberg]. See Abacus manual. 
-    ecutwfc: 100
-  
-  siesta:
-    # [check train] SIESTA 'SystemLabel' keywords. used for reading the training directories
-    prefix: aiida
-
-    # [check train] filename of the SIESTA output log
-    filename_out: siesta_out
-
-    # [default] in [Rydberg]. See SIESTA manual. 
-    meshcutoff: 300
-
-  # [check predict] the sampling density (in Ang^{-1}) in the Brillouin zone. N_i = |b_i| / dk. 0.15 is accurate enough.
-  dk_bz: 0.15     
-
-  # [check predict] the sampling density along high symmetry lines for plotting band structures.          
-  dk_band: 0.037            
-
-  # [check predict] If true, convert to primitive cell.
-  plot_band: True          
-
-  # [check train]
-  path_train: # sacd paths of working directories
-
-  # [check train]
-  path_val: # sacd paths of working directories
-
-  # [check predict] txt files storing the input SAD CHGCAR paths for inference
-  path_predict:
-
-  # [default]
-  num_workers: 4
-
-  # [check train] True if run.task = 1
-  use_bin: False 
-
-  # [default] Currently EdenGNN only supports batch size = 1. batch > 1 will 
-  # raise errors.
-  batch_size: 1
-
-optimize:
-  # [check train] loss_train = \sum_i weight_train_i * loss_i
-  # loss_val = \sum_i weight_val_i * loss_i
-  loss_dict:
-    grid_func_out: # charge density
-      weight_train: 1. 
-      weight_val: 1.
-      loss: "L1"
-    aug_tensor: # augmentation occupancy
-      weight_train: 0. 
-      weight_val: 0.
-      loss: "L1"
-    # this is deprecated. total charge errors are logged during training
-    total_charge:
-      #weight_train: 0.0 # deprecated
-      weight_val: 0.0
-      loss: "L2"
-    operator:
-      weight_train: 1.0
-      weight_val: 1.0
-      loss: "L1"
-
-  # [check train]
-  lr: 0.005
-  task: ${run.task}
-
-  # [default] Freeze the parameters of the MLPs of the RBFs. Experimental usage
-  freeze_basis: False
-
-  # [default] Log step of training loss.
-  log_every_n_steps: 20
-
-  # [default] used for training universal models
-  val_check_interval_steps:
-
-  # [default] Large than 1 means DDP training is on. Experimental usage.
-  accumulate_grad_batches: 1
-
-  # [default] Set scheduler.
-  scheduler: 
-    type: ReduceLROnPlateau
-    ReduceLROnPlateau:
-      factor: 0.5 # 
-      patience: 10 # 
-      min_lr: 0.00002
-      threshold: 0.00001
-  max_epochs: 3000
-  max_steps: 1000000 
-
-  # [default] Set optimizer.
-  optimizer:
-    type: AdamW
-    AdamW:
-      epsilon: 1e-8
-      beta1: 0.99
-      beta2: 0.999
-
-  # [default] 0 default; 1 for debug. deprecated
-  # log_level: 0
-
-  # [default]
-  gradient_clip_val: 0.0    
-
-model:
-  compile: False
-  task: ${run.task}
-  lmix_max: ${data.vasp.lmix_max}
-
-  # [check predict] Whether or not predict total charge density. Set True for 
-  # predict VASP CHGCAR. Otherwise set it False.
-  use_sad: False 
-
-  # [check train] Maximum number of probe points per training chunk. Should be
-  # set close to the OOM value to improve speed.
-  # (depends on your GPU, 20,000,000 works for A800 GPU which has 80G VRAM).
-  chunk_size_train: 1000000
-
-  # [check] Maximum number of probe points per prediction chunk.
-  # (depends on your GPU, 20,000,000 works for A800 GPU which has 80G VRAM).
-  chunk_size_predict: 1000000
-
-  atom:
-    # [default]
-    l_max: 6
-
-    # [default]
-    num_types: 100
-
-    edge:
-      # [default]
-      n_radial_basis: 8
-
-      # [default]
-      radial_type: Bessel
-
-      # [default] The maximum distance between neighbor atoms.
-      cutoff: 4.0
-    conv:
-      # [default]
-      num_conv_layers: 3
-
-      # [default]
-      num_radial_filter_neurons: 64
-
-      # [default]
-      num_radial_filter_layers: 2   
-
-      # [default] Irreps of node features in the graph convolutions.
-      irreps_node_features: 64x0e+64x0o+20x1o+20x1e+16x2o+16x2e+8x3o+8x3e+6x4o+6x4e+5x5e+5x5o+4x6e+4x6o
-  probe:
-    # [default]
-    l_max: 6 
-
-    edge:
-      # [default]: The radius in [Angstrom] which determines the neighbor grids 
-      # for atoms.
-      cutoff: 4.0 
-
-      # [default]
-      n_radial_basis: 8 
-
-      # [default]
-      n_z_embedding_dim: 8 
-
-      # [default]
-      n_z_embedding_neurons: 20 # 
-
-      # [default]
-      n_z_embedding_layers: 2 
-
-    conv: 
-      # [default]
-      num_radial_filter_neurons: 20 
-
-      # [default]
-      num_radial_filter_layers: 2 
-
-      # [default] the number of radial basis for each angular quantum number.
-      n_channels: 4 
-  operator:
-    basis: [0, 0, 1, 1, 2, 2, 3]
-    offsite:
-      symmetrize: False
-      cutoff: 12.0
-      n_radial_basis: 8
-      num_radial_filter_layers: 2
-      num_radial_filter_neurons: 64
-
-
-
-wandb:
-  project: ${run.project}
-  name: ${run.name}
-  mode: ${run.log}

twist/mos2.ckpt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b8b7759012f4622cf710f53ba3acc00533abd708c75445e88d388528bc3841e0
-size 26533682