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A data-efficient foundation model for porous materials based on expert-guided supervised learning.

Jiawen Zou1, Zirui Lv2,3,4, Weimin Tan1,3,4

  • 1College of Computer Science and Artificial Intelligence, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, PR China.

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Summary
This summary is machine-generated.

Foundation models in materials science can now leverage expert knowledge to significantly reduce data needs. This approach enhances accuracy and generalization for porous materials, outperforming larger models.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Foundation models in materials science demand extensive, costly training data.
  • Existing physical knowledge (e.g., molecular force fields) is underutilized in current models.

Purpose of the Study:

  • To demonstrate how expert knowledge can supervise pre-training, reducing data requirements for foundation models.
  • To develop a foundation model for porous materials that effectively integrates structural and energetic information.

Main Methods:

  • Developed potential energy surface (PES) basis functions as unified descriptors for guest-host interactions.
  • Designed a multi-modal architecture to fuse material structure and PES information.
  • Employed pre-training focused on comprehensive geometric feature learning across spatial scales.

Main Results:

  • Introduced SpbNet, a foundation model for porous materials trained under limited data conditions.
  • SpbNet achieved superior performance on over 50 downstream tasks (adsorption, separation, intrinsic properties) compared to models trained on 20x larger datasets.
  • Demonstrated significant error reduction (over 20%) and strong generalization across diverse porous materials (MOFs, COFs, zeolites).

Conclusions:

  • Expert knowledge supervision is a viable strategy to drastically reduce data requirements for foundation models in materials science.
  • SpbNet offers a powerful and data-efficient approach for predicting properties of porous materials.
  • The model's generalization capabilities highlight its potential for broad applications in materials discovery and design.