Checked in five ET models

README.md

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-# spice-models
-Models trained on the SPICE dataset
+# SPICE-Models
+Models trained on the SPICE dataset.

five-et/README.md

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+This directory contains the five equivariant transformer models described in (insert reference when available).
+
+The script `createSpiceDataset.py` converts the dataset file SPICE.hdf5 downloaded from https://github.com/openmm/spice-dataset/releases
+to the format used by [TorchMD-Net](https://github.com/torchmd/torchmd-net).  It generates a new file SPICE-processed.hdf5
+which was used for training.
+
+The file `hparams.yaml` contains the configuration used for training the models.  All models used identical settings
+except that `seed` was set to a different value for each one (the numbers 1 through 5).  Note that although the file
+specifies `num_epochs: 1000`, training was halted after 24 hours (when the training job reached the end of its allocated
+time).  This corresponded to 118 epochs.
+
+The files ending in `.ckpt` are checkpoint files for TorchMD-Net 0.2.4 containing the trained models.  They should
+hopefully work with later versions as well, but that may not be guaranteed.  They can be loaded like this:
+
+```python
+from torchmdnet.models.model import load_model
+model = load_model('model1.ckpt')
+```
+
+The `device` argument to `load_model()` can be used to specify a device to load it on.  For example,
+
+```python
+model = load_model('model1.ckpt', device=torch.device('cuda:0'))
+```
+
+To compute energy and forces for a molecular conformation, invoke the model's `forward()` method.  It takes two arguments:
+a tensor of length `n_atoms` and dtype `long` containing the atom types, and a tensor of shape `(n_atoms, 3)` and dtype
+`float32` containing the atom positions in angstroms.  It returns two arguments: the potential energy in kJ/mol, and
+the force on each atom in kJ/mol/angstrom.  Atom types are defined by the element and formal charge of each atom.  The
+mapping is defined in `createSpiceDataset.py` with this dictionary:
+
+```python
+typeDict = {('Br', -1): 0, ('Br', 0): 1, ('C', -1): 2, ('C', 0): 3, ('C', 1): 4, ('Ca', 2): 5, ('Cl', -1): 6,
+            ('Cl', 0): 7, ('F', -1): 8, ('F', 0): 9, ('H', 0): 10, ('I', -1): 11, ('I', 0): 12, ('K', 1): 13,
+            ('Li', 1): 14, ('Mg', 2): 15, ('N', -1): 16, ('N', 0): 17, ('N', 1): 18, ('Na', 1): 19, ('O', -1): 20,
+            ('O', 0): 21, ('O', 1): 22, ('P', 0): 23, ('P', 1): 24, ('S', -1): 25, ('S', 0): 26, ('S', 1): 27}
+```
+
+For example, the following computes the energy and forces for a pair of ions (Cl- and Na+) positioned 3 angstroms apart.
+
+```python
+types = torch.tensor([6, 19], dtype=torch.long)
+pos = torch.tensor([[0, 0, 0], [0, 3, 0]], dtype=torch.float32)
+energy, forces = model.forward(types, pos)
+```

five-et/createSpiceDataset.py

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+import numpy as np
+from openff.toolkit.topology import Molecule
+from openmm.unit import *
+from collections import defaultdict
+import h5py
+
+typeDict = {('Br', -1): 0, ('Br', 0): 1, ('C', -1): 2, ('C', 0): 3, ('C', 1): 4, ('Ca', 2): 5, ('Cl', -1): 6,
+            ('Cl', 0): 7, ('F', -1): 8, ('F', 0): 9, ('H', 0): 10, ('I', -1): 11, ('I', 0): 12, ('K', 1): 13,
+            ('Li', 1): 14, ('Mg', 2): 15, ('N', -1): 16, ('N', 0): 17, ('N', 1): 18, ('Na', 1): 19, ('O', -1): 20,
+            ('O', 0): 21, ('O', 1): 22, ('P', 0): 23, ('P', 1): 24, ('S', -1): 25, ('S', 0): 26, ('S', 1): 27}
+
+infile = h5py.File('SPICE.hdf5')
+
+# First pass: group the samples by total number of atoms.
+
+groupsByAtomCount = defaultdict(list)
+for name in infile:
+    group = infile[name]
+    count = len(group['atomic_numbers'])
+    groupsByAtomCount[count].append(group)
+
+# Create the output file.
+
+filename = 'SPICE-processed.hdf5'
+outfile = h5py.File(filename, 'w')
+
+# One pass for each number of atoms, creating a group for it.
+
+print(sorted(list(groupsByAtomCount.keys())))
+posScale = 1*bohr/angstrom
+energyScale = 1*hartree/item/(kilojoules_per_mole)
+forceScale = energyScale/posScale
+for count in sorted(groupsByAtomCount.keys()):
+    print(count)
+    smiles = []
+    pos = []
+    types = []
+    energy = []
+    forces = []
+    for g in groupsByAtomCount[count]:
+        molSmiles = g['smiles'][0]
+        mol = Molecule.from_mapped_smiles(molSmiles, allow_undefined_stereo=True)
+        molTypes = [typeDict[(atom.element.symbol, atom.formal_charge/elementary_charge)] for atom in mol.atoms]
+        assert len(molTypes) == count
+        for i, atom in enumerate(mol.atoms):
+            assert atom.atomic_number == g['atomic_numbers'][i]
+        numConfs = g['conformations'].shape[0]
+        for i in range(numConfs):
+            smiles.append(molSmiles)
+            pos.append(g['conformations'][i])
+            types.append(molTypes)
+            energy.append(g['formation_energy'][i])
+            forces.append(g['dft_total_gradient'][i])
+    group = outfile.create_group(f'samples{count}')
+    group.create_dataset('smiles', data=smiles, dtype=h5py.string_dtype())
+    group.create_dataset('types', data=np.array(types), dtype=np.int8)
+    group.create_dataset('pos', data=np.array(pos)*posScale, dtype=np.float32)
+    group.create_dataset('energy', data=np.array(energy)*energyScale, dtype=np.float32)
+    group.create_dataset('forces', data=-np.array(forces)*forceScale, dtype=np.float32)

five-et/hparams.yaml

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+activation: silu
+aggr: add
+atom_filter: -1
+attn_activation: silu
+batch_size: 128
+charge: false
+conf: null
+coord_files: null
+cutoff_lower: 0.0
+cutoff_upper: 10.0
+dataset: HDF5
+dataset_arg: null
+dataset_root: SPICE-processed.hdf5
+derivative: true
+distance_influence: both
+distributed_backend: ddp
+early_stopping_patience: 20
+ema_alpha_dy: 1.0
+ema_alpha_y: 1.0
+embed_files: null
+embedding_dimension: 128
+energy_files: null
+energy_weight: 1.0
+force_files: null
+force_weight: 1.0
+inference_batch_size: 128
+load_model: null
+log_dir: model1b
+lr: 0.0005
+lr_factor: 0.5
+lr_metric: train_loss
+lr_min: 1.0e-07
+lr_patience: 0
+lr_warmup_steps: 0
+max_num_neighbors: 100
+max_z: 28
+model: equivariant-transformer
+neighbor_embedding: true
+ngpus: -1
+num_epochs: 1000
+num_heads: 8
+num_layers: 6
+num_nodes: 1
+num_rbf: 64
+num_workers: 16
+output_model: Scalar
+precision: 32
+prior_model: null
+rbf_type: expnorm
+redirect: true
+reduce_op: add
+reset_trainer: false
+save_interval: 1
+seed: 1
+spin: false
+splits: null
+standardize: false
+test_interval: 10
+test_size: 0.0
+train_size: null
+trainable_rbf: true
+val_size: 0.05
+weight_decay: 0.0