MOFormer: Self-Supervised Transformer model for Metal-Organic Framework
Property Prediction
- URL: http://arxiv.org/abs/2210.14188v1
- Date: Tue, 25 Oct 2022 17:29:42 GMT
- Title: MOFormer: Self-Supervised Transformer model for Metal-Organic Framework
Property Prediction
- Authors: Zhonglin Cao, Rishikesh Magar, Yuyang Wang, and Amir Barati Farimani
- Abstract summary: Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separation.
Finding the optimal MOFs for specific applications requires an efficient and accurate search over an enormous number of potential candidates.
We propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs.
- Score: 7.367477168940467
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity
that can be used for applications in energy storage, water desalination, gas
storage, and gas separation. However, the chemical space of MOFs is close to an
infinite size due to the large variety of possible combinations of building
blocks and topology. Discovering the optimal MOFs for specific applications
requires an efficient and accurate search over an enormous number of potential
candidates. Previous high-throughput screening methods using computational
simulations like DFT can be time-consuming. Such methods also require
optimizing 3D atomic structure of MOFs, which adds one extra step when
evaluating hypothetical MOFs. In this work, we propose a structure-agnostic
deep learning method based on the Transformer model, named as MOFormer, for
property predictions of MOFs. The MOFormer takes a text string representation
of MOF (MOFid) as input, thus circumventing the need of obtaining the 3D
structure of hypothetical MOF and accelerating the screening process.
Furthermore, we introduce a self-supervised learning framework that pretrains
the MOFormer via maximizing the cross-correlation between its
structure-agnostic representations and structure-based representations of
crystal graph convolutional neural network (CGCNN) on >400k publicly available
MOF data. Using self-supervised learning allows the MOFormer to intrinsically
learn 3D structural information though it is not included in the input.
Experiments show that pretraining improved the prediction accuracy of both
models on various downstream prediction tasks. Furthermore, we revealed that
MOFormer can be more data-efficient on quantum-chemical property prediction
than structure-based CGCNN when training data is limited. Overall, MOFormer
provides a novel perspective on efficient MOF design using deep learning.
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