Interaction without an interaction module

The prevailing way to model a many-body system is to encode every component and then let a module capture interaction. We show that the explicit interaction module is harmful when the number of components increases. We use a simple plastic encoder that compresses a component’s interaction contribution into its representation, and combine the representations by linear aggregation. This modelling approach is simple in form yet delivers unexpectedly excellent performance: it matches the prevailing explicit route under most conditions and exceeds it when a mixture carries more components than the training set covered. Moreover, an encoder produced this way has a clear advantage over per-component descriptors when it is transferred to a similar task: it greatly reduces the data requirements.

Authors

Publication Details

Journal
ChemRxiv
Published
2026-10-30
DOI
https://doi.org/10.26434/chemrxiv.15010330/v4
Primary Topic
Machine Learning in Materials Science
Type
preprint

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preprint

Interaction without an interaction module

Lingbo Ji, Baojiang He, Zhan Qu, Xiao Yang et al.
2 links
ChemRxiv
Machine Learning in Materials Science
preprint

Interaction without an interaction module

Lingbo Ji, Baojiang He, Zhan Qu, Xiao Yang, Guanghao Song, Xiankuan Huo, Jun Hu, Zihan Xu, Feiyu Li, Yanan Yang
preprint en

Abstract

The prevailing way to model a many-body system is to encode every component and then let a module capture interaction. We show that the explicit interaction module is harmful when the number of components increases. We use a simple plastic encoder that compresses a component’s interaction contribution into its representation, and combine the representations by linear aggregation. This modelling approach is simple in form yet delivers unexpectedly excellent performance: it matches the prevailing explicit route under most conditions and exceeds it when a mixture carries more components than the training set covered. Moreover, an encoder produced this way has a clear advantage over per-component descriptors when it is transferred to a similar task: it greatly reduces the data requirements.

ChemRxiv
Tencent
Machine Learning in Materials Science
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