从力与噪声中学习数据高效的粗粒化分子动力学
Learning data efficient coarse-grained molecular dynamics from forces and noise
AI总结：
本文提出将基于力的训练与去噪得分匹配相统一，用于机器学习粗粒化分子动力学力场，在保持力参数化优势的同时将数据需求降低100倍，并在Trp-Cage和NTL9蛋白上验证了方法。
摘要：
机器学习粗粒化（MLCG）分子动力学是模拟生物分子的一种有前景的方案。然而，MLCG模型目前需要来自参考原子级分子动力学的大量数据或大量的训练计算。去噪得分匹配——广受欢迎的扩散模型背后的技术——同时作为一种从噪声中生成样本的机器学习框架而出现。第一类模型通常使用原子力进行训练，而第二类模型通过逆转基于噪声的破坏来提取数据分布。我们统一了这些方法以改进MLCG力场的训练，将数据需求降低了100倍，同时保持了基于力的参数化所特有的优势。这些方法在蛋白质Trp-Cage和NTL9上得到了验证，并已作为开源代码发布。
英文摘要：
Machine-learned coarse-grained (MLCG) molecular dynamics is a promising option for modeling biomolecules. However, MLCG models currently require large amounts of data from reference atomistic molecular dynamics or substantial computation for training. Denoising score matching -- the technology behind the widely popular diffusion models -- has simultaneously emerged as a machine-learning framework for creating samples from noise. Models in the first category are often trained using atomistic forces, while those in the second category extract the data distribution by reverting noise-based corruption. We unify these approaches to improve the training of MLCG force-fields, reducing data requirements by a factor of 100 while maintaining advantages typical to force-based parameterization. The methods are demonstrated on proteins Trp-Cage and NTL9 and published as open-source code.
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