Synthesis Action Retrieval

• classifies words tokens into following action categories:

  Starting, Mixing, Heating, Cooling, Purification, Shaping, Reaction, Miscellaneous, NotOperation

• extracts firing temperatures, times, and environment through dependency tree parsing

Installation:

git clone https://github.com/CederGroupHub/synthesis-action-retriever.git
cd synthesis-action-retriever
python setup.py install

Initilization:

from synthesis_action_retriever import SynthActionRetriever

w2v_model = 'path-to-folder/models/your_word2vec_model'
classifier_model = 'path-to-folder/models/your_trained_classification_model'
spacy_model = 'path-to-folder/models/your_spacy_model'

SAR = SynthActionExtractor(w2v_model, classifier_model, spacy_model)

Functions:

synthesis_action_retriever.py

• get_action_labels(sentence_tokens):

    finds actions tokens and classifies them
  
    :param sentence: list of sentence tokens
    :returns: list of actionss tuples (token_id, actionn_type) found in the sentence
  

conditions_extraction.py

• get_times_toks(sentence_tokens):

   finds tokens corresponding to time values
   
   :param sentence tokens: list of sentence tokens
   :returns: token_id, value, unit of time
  

• get_temperatures_toks(sentence_tokens):

   finds tokens corresponding to temperature values
   
   :param sentence tokens: list of sentence tokens
   :returns: token_id, value, unit of temperature
  

• get_environment_toks(sentence_tokens, materials):

   finds tokens corresponding to environment values
   
   :param sentence tokens: list of sentence tokens
   :param materials: list of materials in sentence
   :returns: token_id, value, unit of environment 
  

build_graph.py

• build_graph(sentence_tokens, action_tags, materials):

   builds synthesis workflow provided sentence tokens, action tags and materials list (optionally)
   :param sentence_tokens: list of strings
   :param action_tags: list of strings of same length as sentence_tokens
   :param materials: (optional) list of {"text": material, "tok_ids": list of tok ids in sentence}
   :return: list of dict        
  

Example:

import os
import json
from pprint import pprint
from synthesis_action_retriever.synthesis_action_retriever import SynthActionRetriever
from synthesis_action_retriever.build_graph import GraphBuilder
from synthesis_action_retriever.utils import make_spacy_tokens

dir_path = "path-to-models"
w2v_model = "path-to-w2v_model"
ext_model = "path-to-ext_model"

sar = SynthActionRetriever(
    embedding_model=os.path.join(dir_path, w2v_model),
    extractor_model=os.path.join(dir_path, ext_model)
)
gb = GraphBuilder()

with open('./data/example_sentences.json', 'r') as fp:
    examples = json.load(fp)

graph = []
for sent in examples:
    spacy_tokens = make_spacy_tokens(sent["sentence"])
    actions = sar.get_action_labels(spacy_tokens)
    graph.append(gb.build_graph(spacy_tokens, actions, sent["materials"]))

refined_graph = gb.refine_graph(graph, examples)
pprint(refined_graph)

Citation:

If you find the codes and data useful, please cite our paper:

@Article{D1DD00034A,
author ="Wang, Zheren and Cruse, Kevin and Fei, Yuxing and Chia, Ann and Zeng, Yan and Huo, Haoyan and He, Tanjin and Deng, Bowen and Kononova, Olga and Ceder, Gerbrand",
title  ="ULSA: unified language of synthesis actions for the representation of inorganic synthesis protocols",
journal  ="Digital Discovery",
year  ="2022",
pages  ="-",
publisher  ="RSC",
doi  ="10.1039/D1DD00034A",