Probabilistic prediction of material stability: integrating convex hulls into active learning
Probabilistic prediction of material stability: integrating convex hulls into active learning
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DOI：
10.1039/d4mh00432a
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发表时间：
2024-08-05
影响因子：
13.3
通讯作者：
Toberer，Eric
中科院分区：
文献类型：
--
作者：
Novick，Andrew;Cai，Diana;Toberer，Eric
主动学习是有效探索复杂空间、寻找材料科学多种用途的宝贵工具。然而，由于其全局性质，相图凸包的确定并不完全适合传统的主动学习方法。具体来说，材料的热力学稳定性不仅仅是其自身能量的函数，而是需要来自所有其他竞争成分和相的能量信息。在这里，我们提出了凸包感知主动学习（CAL），这是一种新颖的贝叶斯算法，它选择实验来最小化凸包的不确定性。 CAL 优先考虑靠近或位于船体上的组合，从而在快速确定与凸包无关的其他组合中留下显着的不确定性。因此，与仅关注能量的方法相比，可以用明显更少的观测来预测凸包。这种贝叶斯方法的本质是凸包和所有后续预测（例如稳定性和化学势）的不确定性量化。通过提供更高的搜索效率和不确定性量化，CAL 可以轻松纳入新兴的基于不确定性的热力学预测工作流程范例中。
Active learning is a valuable tool for efficiently exploring complex spaces, finding a variety of uses in materials science. However, the determination of convex hulls for phase diagrams does not neatly fit into traditional active learning approaches due to their global nature. Specifically, the thermodynamic stability of a material is not simply a function of its own energy, but rather requires energetic information from all other competing compositions and phases. Here we present convex hull-aware active learning (CAL), a novel Bayesian algorithm that chooses experiments to minimize the uncertainty in the convex hull. CAL prioritizes compositions that are close to or on the hull, leaving significant uncertainty in other compositions that are quickly determined to be irrelevant to the convex hull. The convex hull can thus be predicted with significantly fewer observations than approaches that focus solely on energy. Intrinsic to this Bayesian approach is uncertainty quantification in both the convex hull and all subsequent predictions (e.g., stability and chemical potential). By providing increased search efficiency and uncertainty quantification, CAL can be readily incorporated into the emerging paradigm of uncertainty-based workflows for thermodynamic prediction.