Machine learning interatomic potentials, particularly ones based on deep neural networks, have taken significant strides in accelerating first-principles simulations, expanding the length and time scales of the simulations with accuracies akin to first-principles simulations. Notwithstanding their success in accurately describing the physical properties of pristine ionic systems with multiple oxidation states, herein we show that an implementation of deep neural network potentials (DNPs) yield vacancy formation energies in MgO with a significant ∼3 eV error. In contrast, we show that moment tensor potentials can accurately describe all properties of the oxide, including vacancy formation energies. We show that the vacancy formation energy errors in DNPs correlate with the strength of ionic interactions in the system as evidenced by contrasting MgO with the less ionic systems Cu_xO_y and Ag_xO_y. Our findings suggest that descriptors employed in the DNP may be inadequate and cannot accurately describe vacancies in ionic systems.

中文翻译：

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克服机器学习中的不准确之处：离子空位模拟的原子间势实现


机器学习原子间势，尤其是基于深度神经网络的原子间势，在加速第一性原理模拟方面取得了重大进展，以类似于第一性原理模拟的精度扩展了模拟的长度和时间尺度。尽管他们成功地准确描述了具有多种氧化态的原始离子系统的物理性质，但本文我们表明，深度神经网络电位 （DNP） 的实现在 MgO 中产生空位形成能，具有显着的 ∼3 eV 误差。相比之下，我们表明矩张量势可以准确描述氧化物的所有性质，包括空位形成能。我们表明，DNP 中的空位形成能量误差与系统中离子相互作用的强度相关，通过将 MgO 与离子较少的系统 Cu_xO_y 和 Ag_xO_y 进行对比来证明这一点。我们的研究结果表明，DNP 中使用的描述符可能不充分，并且无法准确描述离子系统中的空位。