标题： 机器学习用于密度泛函近似的准确性 显示英文标题

标题： Machine learning for accuracy in density functional approximations

摘要： 机器学习技术已进入计算化学，作为加速原子模拟和材料设计的不可或缺的工具。 此外，机器学习方法有望提高计算高效的电子结构方法（如密度泛函理论）的预测能力，达到化学精度，并纠正密度泛函方法中的基本错误。 在此，回顾了将机器学习应用于提高密度泛函及相关近似准确性的最新进展。 通过应用有前景的模型到远超出其训练集的系统示例，讨论了在不同化学和材料类别之间具有迁移性的机器学习模型的潜力和挑战。 显示英文摘要

摘要： Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. Here, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets.