模块化框架提升材料属性预测精度，适配多样任务
MoMa: A Modular Deep Learning Framework for Material Property Prediction
- 分模块训练后自适应组合，应对材料任务多样性
- 17个数据集上平均性能比最强基线高14%
- 适合需要少样本与持续学习的材料研发场景
材料属性预测的深度学习方法广泛用于推动材料发现。然而，当前普遍采用的预训练-微调范式难以应对材料任务内在的多样性和差异性。为此，我们提出MoMa——一个模块化材料学习框架，先在多种任务上训练专用模块，再针对下游场景自适应组合协同模块。在17个数据集上的评估显示，MoMa相比最强基线平均提升14%。少样本和持续学习实验进一步验证了其在真实场景中的潜力。MoMa开创了模块化材料学习新范式，将开源以促进社区协作。
原文摘要 · Abstract (English)
Deep learning methods for material property prediction have been widely explored to advance materials discovery. However, the prevailing pre-train then fine-tune paradigm often fails to address the inherent diversity and disparity of material tasks. To overcome these challenges, we introduce MoMa, a Modular framework for Materials that first trains specialized modules across a wide range of tasks and then adaptively composes synergistic modules tailored to each downstream scenario. Evaluation across 17 datasets demonstrates the superiority of MoMa, with a substantial 14% average improvement over the strongest baseline. Few-shot and continual learning experiments further highlight MoMa's potential for real-world applications. Pioneering a new paradigm of modular material learning, MoMa will be open-sourced to foster broader community collaboration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。