长程相互作用建模能显著提升机器学习势函数的泛化能力。
Generalization of Long-Range Machine Learning Potentials in Complex Chemical Spaces
- 引入长程校正机制，增强模型对复杂化学空间的适应性。
- 在金属有机框架上实现跨分布样本的性能提升，验证了迁移能力突破。
- 适合材料模拟、分子设计等领域研究者参考模型设计策略。
化学空间的庞大使得泛化成为机器学习原子间势（MLIPs）发展的核心挑战。尽管MLIPs可实现接近量子精度的大规模原子模拟，但其应用常受限于对分布外样本的差强人意的迁移能力。本文系统评估了多种带长程校正的MLIP架构在多样化化学空间中的表现，结果表明此类方案不仅提升分布内性能，更关键的是显著增强对未见化学区域的泛化能力。为实现更严格的基准测试，我们提出有偏训练-测试划分策略，明确检验模型在显著不同的化学空间区域的表现。综合成果强调了长程建模在实现可泛化MLIPs中的重要性，并提供诊断化学空间系统性失败的框架。尽管我们在金属有机框架上验证方法，但其适用范围广泛，为构建更鲁棒、可迁移的MLIPs提供了洞见。
原文摘要 · Abstract (English)
The vastness of chemical space makes generalization a central challenge in the development of machine learning interatomic potentials (MLIPs). While MLIPs could enable large-scale atomistic simulations with near-quantum accuracy, their usefulness is often limited by poor transferability to out-of-distribution samples. Here, we systematically evaluate different MLIP architectures with long-range corrections across diverse chemical spaces and show that such schemes are essential, not only for improving in-distribution performance but, more importantly, for enabling significant gains in transferability to unseen regions of chemical space. To enable a more rigorous benchmarking, we introduce biased train-test splitting strategies, which explicitly test the model performance in significantly different regions of chemical space. Together, our findings highlight the importance of long-range modeling for achieving generalizable MLIPs and provide a framework for diagnosing systematic failures across chemical space. Although we demonstrate our methodology on metal-organic frameworks, it is broadly applicable to other materials, offering insights into the design of more robust and transferable MLIPs.
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