nablaDFT

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name: SchNet  # model name, used for output database name
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dataset_name: test_opt # used for output database name
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job_type: optimize
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pretrained: SchNet_train_large # name of pretrained split or 'null'
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ckpt_path: null # path to checkpoint for training resume or test run
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input_db: ./test_optim_database.db # relative path to input database
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output_dir: ./traj_optimize # relative path to directory for output database
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batch_size: 32
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fmax: 1e-5 # condition for max norm of gradients
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steps: 500 # number of optimization steps
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device: "cuda:0"  # "cpu" or "cuda:id"
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defaults:
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  - _self_
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  - model: schnet.yaml
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  - calculator: spk_calculator.yaml
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  - optimizer: batchwise_lbfgs.yaml
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# need this to set working dir as current dir
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hydra:
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  output_subdir: null
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  run:
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    dir: .
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original_work_dir: ${hydra:runtime.cwd}
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seed: 23
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