Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we ...Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.展开更多
为了适应微电网并网运行的需求,配电网正朝着主动控制与管理的方向发展。本文介绍了一种新型的柔性联络开关(Flexible tie switch, FTS),它可以代替传统的分段隔离开关或柔性联络开关,适用于配电网的主动潮流控制。FTS可以通过优化现有...为了适应微电网并网运行的需求,配电网正朝着主动控制与管理的方向发展。本文介绍了一种新型的柔性联络开关(Flexible tie switch, FTS),它可以代替传统的分段隔离开关或柔性联络开关,适用于配电网的主动潮流控制。FTS可以通过优化现有的网络容量,以便容纳更多的分布式发电单元。为了分析其有效性,首先根据FTS在dq坐标系中的简化数学模型,采用典型的双闭环控制算法,在PSCAD中研究了FTS的动态功率控制性能,并将最小网络损失和电压曲线优化被设定为潮流控制的目标。考虑到电压偏差约束、FTS的容量限制以及功率损耗,建立了以FTS为控制变量的多目标优化函数。当负荷的功率约束较为复杂的情况下,利用粒子群算法来寻找最佳解决方案。结果表明,当FTS被用于配电网的潮流控制时,系统网损明显降低,电压曲线也得到了优化。展开更多
基金supported by the Basic Science Center Project of National Natural Science Foundation of China(52388201)the National Natural Science Foundation of China(12334003)+4 种基金the National Science Fund for Distinguished Young Scholars(12025405)the National Key Basic Research and Development Program of China(2023YFA1406400)the Beijing Advanced Innovation Center for Future Chip(ICFC)the Beijing Advanced Innovation Center for Materials Genome Engineeringfunded by the Shuimu Tsinghua Scholar program。
文摘Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.
文摘为了适应微电网并网运行的需求,配电网正朝着主动控制与管理的方向发展。本文介绍了一种新型的柔性联络开关(Flexible tie switch, FTS),它可以代替传统的分段隔离开关或柔性联络开关,适用于配电网的主动潮流控制。FTS可以通过优化现有的网络容量,以便容纳更多的分布式发电单元。为了分析其有效性,首先根据FTS在dq坐标系中的简化数学模型,采用典型的双闭环控制算法,在PSCAD中研究了FTS的动态功率控制性能,并将最小网络损失和电压曲线优化被设定为潮流控制的目标。考虑到电压偏差约束、FTS的容量限制以及功率损耗,建立了以FTS为控制变量的多目标优化函数。当负荷的功率约束较为复杂的情况下,利用粒子群算法来寻找最佳解决方案。结果表明,当FTS被用于配电网的潮流控制时,系统网损明显降低,电压曲线也得到了优化。
