Model Agnostic Graph Prompt Learning for Crystal Property Prediction
These models often encode domain-specific knowledge into their graph encoding modules, which increases their parameter size and makes their performance heavily dependent on domain expertise. Added to this, explicitly incorporating all chemical and structural features, that might influence a specific crystal property into the GNN encoder, is a challenging task. In this work, we propose a soft prompt learning framework that captures latent features essential for property prediction, which are not explicitly provided to the GNN.
- ▪These models often encode domain-specific knowledge into their graph encoding modules, which increases their parameter size and makes their performance heavily dependent on domain expertise.
- ▪Added to this, explicitly incorporating all chemical and structural features, that might influence a specific crystal property into the GNN encoder, is a challenging task.
- ▪In this work, we propose a soft prompt learning framework that captures latent features essential for property prediction, which are not explicitly provided to the GNN.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2607.08996 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-13T04:20:37.625Z |
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Computer Science > Machine Learning arXiv:2607.08996 (cs) [Submitted on 9 Jul 2026] Title:Model Agnostic Graph Prompt Learning for Crystal Property Prediction Authors:Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly View a PDF of the paper titled Model Agnostic Graph Prompt Learning for Crystal Property Prediction, by Shrimon Mukherjee and 4 other authors View PDF HTML (experimental) Abstract:Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties. These models often encode domain-specific knowledge into their graph encoding modules, which increases their parameter size and makes their performance heavily dependent on domain expertise.
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