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PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

About

This repository contains implementations for 2 of the PhAST components presented in the paper:

• PhysEmbedding that allows one to create an embedding vector from atomic numbers that is the concatenation of:
  • A learned embedding for the atom's group
  • A learned embedding for the atom's period
  • A fixed or learned embedding from a set of known physical properties, as reported by mendeleev
  • In the case of the OC20 dataset, a learned embedding for the atom's tag (adsorbate, catalyst surface or catalyst sub-surface)
• Tag-based graph rewiring strategies for the OC20 dataset:

  • remove_tag0_nodes deletes all nodes in the graph associated with a tag 0 and recomputes edges

  • one_supernode_per_graph replaces all tag 0 atoms with a single new atom

  • one_supernode_per_atom_typs replaces all tag 0 atoms of a given element with its own super node

    

Also: https://github.com/vict0rsch/faenet

Getting started

Physical embeddings

import torch
from phast.embedding import PhysEmbedding

z = torch.randint(1, 85, (3, 12)) # batch of 3 graphs with 12 atoms each
phys_embedding = PhysEmbedding(
    z_emb_size=32, # default
    period_emb_size=32, # default
    group_emb_size=32, # default
    properties_proj_size=32, # default is 0 -> no learned projection
    n_elements=85, # default
)
h = phys_embedding(z) # h.shape = (3, 12, 128)

tags = torch.randint(0, 3, (3, 12))
phys_embedding = PhysEmbedding(
    tag_emb_size=32, # default is 0, this is OC20-specific
    final_proj_size=64, # default is 0, no projection, just the concat. of embeds.
)

h = phys_embedding(z, tags) # h.shape = (3, 12, 64)

Graph rewiring

from copy import deepcopy
import torch
from phast.graph_rewiring import (
    remove_tag0_nodes,
    one_supernode_per_graph,
    one_supernode_per_atom_type,
)

data = torch.load("./examples/data/is2re_bs3.pt")  # 3 OC20 IS2RE data samples
print(
    "Data initially contains {} graphs, a total of {} atoms and {} edges".format(
        len(data.natoms), data.ptr[-1], len(data.cell_offsets)
    )
)
rewired_data = remove_tag0_nodes(deepcopy(data))
print(
    "Data without tag-0 nodes contains {} graphs, a total of {} atoms and {} edges".format(
        len(rewired_data.natoms), rewired_data.ptr[-1], len(rewired_data.cell_offsets)
    )
)
rewired_data = one_supernode_per_graph(deepcopy(data))
print(
    "Data with one super node per graph contains a total of {} atoms and {} edges".format(
        rewired_data.ptr[-1], len(rewired_data.cell_offsets)
    )
)
rewired_data = one_supernode_per_atom_type(deepcopy(data))
print(
    "Data with one super node per atom type contains a total of {} atoms and {} edges".format(
        rewired_data.ptr[-1], len(rewired_data.cell_offsets)
    )
)

Data initially contains 3 graphs, a total of 261 atoms and 11596 edges
Data without tag-0 nodes contains 3 graphs, a total of 64 atoms and 1236 edges
Data with one super node per graph contains a total of 67 atoms and 1311 edges
Data with one super node per atom type contains a total of 71 atoms and 1421 edges

Tests

This requires poetry. Make sure to have torch and torch_geometric installed in your environment before you can run the tests. Unfortunately because of CUDA/torch compatibilities, neither torch nor torch_geometric are part of the explicit dependencies and must be installed independently.

git clone git@github.com:vict0rsch/phast.git
poetry install --with dev
pytest --cov=phast --cov-report term-missing

Testing on Macs you may encounter a Library Not Loaded Error