标题： 具有部分贝叶斯神经网络的主动学习和迁移学习用于材料和化学品 显示英文标题

标题： Active and transfer learning with partially Bayesian neural networks for materials and chemicals

摘要： 主动学习是一种迭代过程，通过选择最有信息量的数据点进行探索，对于高效表征材料和化学品属性空间至关重要。神经网络在预测这些属性方面表现出色，但在主动学习驱动的探索中缺乏所需的不确定性量化。完全贝叶斯神经网络将权重视为通过先进马尔可夫链蒙特卡洛方法推断出的概率分布，提供了稳健的不确定性量化，但计算成本较高。在这里，我们表明部分贝叶斯神经网络（PBNNs），其中仅选定层具有概率权重，而其他层保持确定性，可以在较低的计算成本下实现与完全贝叶斯网络相当的准确性和不确定性估计。此外，通过使用基于理论计算预训练的权重初始化先验分布，我们证明PBNNs可以有效利用计算预测来加速实验数据的主动学习。我们在分子属性预测和材料科学任务上验证了这些方法，确立了PBNNs作为在有限、复杂数据集上进行主动学习的实用工具。 显示英文摘要

摘要： Active learning, an iterative process of selecting the most informative data points for exploration, is crucial for efficient characterization of materials and chemicals property space. Neural networks excel at predicting these properties but lack the uncertainty quantification needed for active learning-driven exploration. Fully Bayesian neural networks, in which weights are treated as probability distributions inferred via advanced Markov Chain Monte Carlo methods, offer robust uncertainty quantification but at high computational cost. Here, we show that partially Bayesian neural networks (PBNNs), where only selected layers have probabilistic weights while others remain deterministic, can achieve accuracy and uncertainty estimates on active learning tasks comparable to fully Bayesian networks at lower computational cost. Furthermore, by initializing prior distributions with weights pre-trained on theoretical calculations, we demonstrate that PBNNs can effectively leverage computational predictions to accelerate active learning of experimental data. We validate these approaches on both molecular property prediction and materials science tasks, establishing PBNNs as a practical tool for active learning with limited, complex datasets.