现有机器学习势函数依赖DFT数据，难以真实模拟复杂材料。
Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
- 用更精准的耦合簇理论生成训练数据，覆盖更多材料空间
- 构建大规模基准测试工具，揭示模型内部工作原理
- 开发高效模型以准确模拟多种材料性质，推动实际应用
通用机器学习原子间势函数（MLIPs）可加速材料发现的模拟。但当前研究未能有效推动其应用，原因包括：1. 过度依赖密度泛函理论（DFT）生成训练数据；2. MLIPs在多样材料的大规模分子动力学（MD）模拟中无法可靠准确运行；3. 对其内在能力理解有限。为解决这些问题，我们主张：1. 采用更精确的模拟方法（如耦合簇理论）生成大规模训练数据，覆盖广泛的材料设计空间；2. 构建基于大规模基准测试、可视化与可解释性分析的MLIP评测工具，深入理解其内部机制；3. 开发计算高效的MLIP，实现对多种材料性质的高精度分子动力学模拟。这些跨学科方向有助于推动MLIP在设备级复杂材料中的真实应用。
原文摘要 · Abstract (English)
Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated simulations for materials discovery. However, current research efforts fail to impactfully utilize MLIPs due to: 1. Overreliance on Density Functional Theory (DFT) for MLIP training data creation; 2. MLIPs' inability to reliably and accurately perform large-scale molecular dynamics (MD) simulations for diverse materials; 3. Limited understanding of MLIPs' underlying capabilities. To address these shortcomings, we aargue that MLIP research efforts should prioritize: 1. Employing more accurate simulation methods for large-scale MLIP training data creation (e.g. Coupled Cluster Theory) that cover a wide range of materials design spaces; 2. Creating MLIP metrology tools that leverage large-scale benchmarking, visualization, and interpretability analyses to provide a deeper understanding of MLIPs' inner workings; 3. Developing computationally efficient MLIPs to execute MD simulations that accurately model a broad set of materials properties. Together, these interdisciplinary research directions can help further the real-world application of MLIPs to accurately model complex materials at device scale.
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