Sim2Real transfer for catalyst activity prediction

Yuta Yahagi, Kiichi Obuchi, Fumihiko Kosaka, Kota Matsui

Submission Track: Short Paper

Submission Category: AI-Guided Design

Keywords: Catalyst design, Transfer learning, Small data, Sim2Real, Reverse water-gas shift, Density functional theory

TL;DR: The paper proposes a sim2real transfer method that significantly enhances catalyst activity predictions with minimal experimental data.

Abstract:

Sim2real transfer, knowledge transfer from computational data to experimental data, has received increasing attention as a promising solution to small data problems in materials.
We proposed a sim2real transfer method that significantly enhances catalyst activity predictions by harnessing the knowledge of catalyst chemistry.
The proposed method transforms the feature space of source computational data into that of target experimental data, and then solves the problem as a homogeneous transfer learning.
Through the demonstration, we confirmed that transfer learning model exhibits positive transfer on accuracy and robustness.
Notably, significantly high accuracy was achieved despite using a few (less than 10) target data, whose accuracy is compatible with a full scratch model with more than 70 target data.
This result indicates that the proposed method leverages the prediction performance with few target data, which helps saving the number of trials in real laboratories.

AI4Mat Journal Track: Yes

Submission Number: 28