Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
复制标题
将密度泛函理论用于机器学习加速材料发现的测试
DOI：
10.1021/acs.jpclett.1c00631
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发表时间：
2021-05-11
影响因子：
5.7
通讯作者：
Kulik, Heather J.
中科院分区：
文献类型：
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作者：
Duan, Chenru;Liu, Fang;Kulik, Heather J.
机器学习（ML）加速发现已经开始提供克服计算材料设计组合挑战所需的效率进步。然而，ML加速发现既继承了来自密度泛函理论（DFT）的训练数据的偏差，又导致了许多注定失败的尝试计算。许多引人注目的功能材料和催化过程涉及应变化学键，开壳自由基和双自由基，或金属有机键到开壳过渡金属中心。虽然有前途的目标，这些材料提出了独特的挑战，电子结构的方法和组合的挑战，他们的发现。在这个视角中，我们描述了在准确性、效率和方法方面所需的进步，这些进步超出了传统的基于DFT的ML工作流中的典型特征。这些挑战已经开始通过训练的ML模型来解决，以预测多种方法的结果或它们之间的差异，从而实现定量敏感性分析。对于高通量筛选中的给定数据点，DFT要被信任，它必须通过一系列测试。预测计算成功的可能性并检测强相关性的存在的ML模型将使快速诊断和适应策略成为可能。这些“决策引擎”代表了迈向自主工作流程的第一步，这些工作流程避免了对基于DFT的材料发现的鲁棒性进行专家确定的需要。
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the biases of training data derived from density functional theory (DFT) and leads to many attempted calculations that are doomed to fail. Many compelling functional materials and catalytic processes involve strained chemical bonds, open-shell radicals and diradicals, or metal-organic bonds to open-shell transition-metal centers. Although promising targets, these materials present unique challenges for electronic structure methods and combinatorial challenges for their discovery. In this Perspective, we describe the advances needed in accuracy, efficiency, and approach beyond what is typical in conventional DFT-based ML workflows. These challenges have begun to be addressed through ML models trained to predict the results of multiple methods or the differences between them, enabling quantitative sensitivity analysis. For DFT to be trusted for a given data point in a high-throughput screen, it must pass a series of tests. ML models that predict the likelihood of calculation success and detect the presence of strong correlation will enable rapid diagnoses and adaptation strategies. These "decision engines" represent the first steps toward autonomous workflows that avoid the need for expert determination of the robustness of DFT-based materials discoveries.