Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered ${\mathrm{SnO}}_2(110)\text{−}(4\times 1)$ reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns.

中文翻译：

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密度泛函理论结构优化中的原子势飞行机器学习

机器学习（ML）用于导出局部稳定性信息，用于与最近发现的系统有关的密度泛函理论计算 ${锡}_{\mathrm{2个}}（110）\text{-}（4\times \mathrm{1个}）$重建。使用进化算法（EA）对ML模型进行训练，以了解在全局最小能量搜索过程中收集的（结构，总能量）关系。在构建时，ML模型用于指导EA，从而加快了EA成功的总体速度。对模型中出现的局部原子电势的检查进一步显示出化学上直观的模式。