用于密度泛函理论哈密顿量预测的自洽性训练
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
AI总结：
针对密度泛函理论哈密顿量预测中标注数据不足的问题，提出基于自洽性原理的无监督训练方法，利用无标签数据提升了模型在数据稀缺和分布外场景下的泛化能力与计算效率。
摘要：
在密度泛函理论中预测平均场哈密顿量矩阵，是利用机器学习解决分子科学问题的基础方法。然而，其适用性受限于训练用的标注数据不足。在本研究中，我们指出哈密顿量预测具有自洽性原理，基于此我们提出了自洽性训练，这是一种不需要标注数据的精确训练方法。该方法通过以下优势将此任务与预测其他分子属性区分开来：(1) 它使模型能够在大量无标签数据上训练，从而解决数据稀缺挑战并增强泛化能力；(2) 它比运行 DFT 生成监督训练标签更高效，因为它将 DFT 计算分摊到一组查询上。我们在实验上证明了该方法在数据稀缺和分布外场景中具有更好的泛化能力，以及在 DFT 标注上具有更高的效率。这些优势将哈密顿量预测的适用性推向了更大的规模。
英文摘要：
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its applicability is limited by insufficient labeled data for training. In this work, we highlight that Hamiltonian prediction possesses a self-consistency principle, based on which we propose self-consistency training, an exact training method that does not require labeled data. It distinguishes the task from predicting other molecular properties by the following benefits: (1) it enables the model to be trained on a large amount of unlabeled data, hence addresses the data scarcity challenge and enhances generalization; (2) it is more efficient than running DFT to generate labels for supervised training, since it amortizes DFT calculation over a set of queries. We empirically demonstrate the better generalization in data-scarce and out-of-distribution scenarios, and the better efficiency over DFT labeling. These benefits push forward the applicability of Hamiltonian prediction to an ever-larger scale.
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