In this context, generalizing deep learning electronic structure calculation to the PW basis would be of critical importance to future development of the field. Fig. 1: Idea of the deep learning DFT Hamiltonian under the PW basis and its applications to twisted bilayer graphene. a, The PW ...
The deep learning DFT Hamiltonian (DeepH) method has been developed to improve the efficiency of electronic-structure calculation, which shows great potential to address the accuracy-efficiency dilemma of DFT8,9. A substantial generalization of the method is required to study a broad class of magne...
这给深度学习电子结构计算方法带来了更高的精度和更好的泛化能力,并打通了其利用电子结构大数据作深度学习的通道。 相关研究以「Generalizing deep learning electronic structure calculation to the plane-wave basis」为题,于 10 月 3 日发布在《Nature Computational Science》上。 论文链接:https://www.nature.com/...
该成果以“Deep-learningelectronic-structure calculation of magnetic superstructures ”为题发表在4月26日的《自然·计算科学》(NatureComputational Science),并入选为期刊封面文章。同期,该杂志还发表了以“Adeep-learning method for studying magnetic superstructures ”为题的Research Briefing、以“Computationally probing...
DeepH框架的方法介绍和示例研究结果以“Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation”为题发表在6月23日的《自然·计算科学》(Nature Computational Science)。在同一期中,该杂志以研究简报(Research Briefing)形式介绍了这一成果,附有专家和编辑评论,以及Be...
Deep Learning Market Overview The global deep learning market size is estimated to grow from USD 6.4 billion in 2025 to USD 34.5 billion by 2035, representing a CAGR of 18.3% during the forecast period till 2035. Since the mid-twentieth century, computing devices have continually been explored...
A deep learning package for many-body potential energy representation and molecular dynamics Python1.7k548 abacus-developabacus-developPublic Forked fromabacusmodeling/abacus-develop An electronic structure package based on either plane wave basis or numerical atomic orbitals. ...
structure behind vast amounts of data (Lecun et al., 2015). In recent years, deep learning has gained popularity due to its capability of learning high level abstractions through its hierarchical architectures (Ain et al., 2017). Several studies have applied deep learning techniques to analyze...
Deep learning is on the rise in the machine learning community, because the traditional shallow learning architectures have proved unfit for the more challenging tasks of machine learning and strong artificial intelligence (AI). The surge in and wide availability of increased computing power, coupled ...
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperfor...