Samsung AI Challenge for Scientific Discovery

• Date: 2021.08.04 ~ 2021.09.27 18:00
• Task: Molecular property prediction (The gap between S1 and T1 energy)
• Result: 21st place / 220 teams

Overview

Generating node-level feature / edge type

• Using only atom type (13 dim)
• Each atom is embedded into 256 dimensional vector by a simple linear transformation
• There are 6 edge type : the number of combinations of (bond type, bond stereo)

Relational Graph Convolutional Network (RGCN)

• Total 8 RGCN layers each of which has 256 channels
• Skip-connections
• Using the sum of node representations followed by one hidden layer MLP as the graph representation

Readout phase

• Multi layers perceptron with 2 hidden layers (1,024 dim, 512 dim)
• Dropout with p=0.3
• Directly predicting ST1 gap

10-Fold ensembling

• Taking the simple average of 10 models

Run

1. Data preparation

• dir_data: the directory where train.csv, dev.csv, and test.csv are stored
• dir_output: the directory where the preprocessed tgm.data.Data files will be stored.

python gnn_preprocess.py --dir_data './data' --dir_output './outputs/rgcn'

2. Training a single model and predicting test data

• Modify TrainConfig in config.py
• It gives public score about 0.127

python train.py