Teaching oxidation states to neural networks

The accurate description of redox reactions remains a challenge for first-principles calculations, but it has been shown that extended Hubbard functionals (DFT+U+V) can provide a reliable approach, mitigating self-interaction errors, in materials with strongly localized d or f electrons. Here, we first show that DFT+U+V molecular dynamics is capable to follow the adiabatic evolution of oxidation states over time, using representative Li-ion cathode materials. In turn, this allows to develop redox-aware machine-learned potentials. We show that considering atoms with different oxidation states (as accurately predicted by DFT+U+V) as distinct species in the training leads to potentials that are able to identify the correct ground state and pattern of oxidation states for redox elements present. This is achieved, e.g., trough a combinatorial search for the lowest energy configuration. This brings the advantages of machine-learned potential to key technological applications (e.g., rechargeable batteries), which require an accurate description of the evolution of redox states.

对氧化还原反应的精确描述始终是量子力学基本原理计算中的难题，然而，研究表明扩展的Hubbard泛函（DFT+U+V）能够提供一种可靠的方法，在具有强局域d或f电子的材料中，此方法能够缓解自洽相互作用误差。在本研究中，我们首先证实DFT+U+V分子动力学能够追踪氧化态随时间的绝热演化，并采用典型的锂离子正极材料进行验证。进而，这为开发氧化还原敏感的机器学习势场提供了可能。我们发现，在训练过程中，将具有不同氧化态的原子（由DFT+U+V准确预测）视为独立物种，能够得到能够识别存在于其中的氧化还原元素的正确基态和氧化态模式的势场。例如，通过组合搜索最低能量配置来实现。这为需要准确描述氧化还原态演化的关键技术应用（例如，可充电电池）带来了机器学习势场的优势。