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Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances

JOURNAL OF PHYSICAL CHEMISTRY C(2024)

Carnegie Mellon Univ | Meta Platforms Inc

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Abstract
Graph neural networks (GNNs) have been shown to be astonishingly capable models for molecular property prediction, particularly as surrogates for expensive density functional theory calculations of relaxed energy for novel material discovery. However, one limitation of GNNs in this context is the lack of useful uncertainty prediction methods, as this is critical to the material discovery pipeline. In this work, we show that uncertainty quantification for relaxed energy calculations is more complex than uncertainty quantification for other kinds of molecular property prediction due to the effect that structure optimizations have on the error distribution. We propose that distribution-free techniques are more useful tools for assessing calibration, recalibrating, and developing uncertainty prediction methods for GNNs performing relaxed energy calculations. We also develop a relaxed energy task for evaluating uncertainty methods for equivariant GNNs, based on distribution-free recalibration and using the Open Catalyst Project data set. We benchmark a set of popular uncertainty prediction methods on this task and show that conformal prediction methods, with our novel latent space distance improvements, are the most well-calibrated and efficient approach for relaxed energy calculations. Finally, we demonstrate that our latent space distance method produces results which align with our expectations on an atom clustering example, and on specific equation of state and adsorbate coverage examples from outside the training data set.
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要点】:本文提出BatchGFN,一种基于生成流网络进行批量主动学习的新方法,通过采样与批次奖励成比例的数据点集合,有效构建了高度信息化的批次,实现了推理阶段近最优效用批次的采样。

方法】:BatchGFN利用生成流网络,结合一个合适的奖励函数(如批次与模型参数之间的联合互信息),以原则性方式选择主动学习中信息量大的数据批次。

实验】:通过在玩具回归问题上进行实验,展示了BatchGFN方法能够在每次批次中通过单个前向传播即可采样到近最优效用批次,减少了批量感知算法的计算复杂度,并避免了寻找批次奖励最大化者的贪婪近似。同时,作者还展示了在不同获取步骤中摊销训练的早期结果,这将有助于扩展到实际任务中。具体数据集名称未在摘要中提及。