.png){kind=icon}
返回
GPU-Accelerated Graph-Based Semiempirical Quantum Chemistry
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jctc.5c00936.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph-based electronic structure theory offers a scalable approach to study large, complex atomistic systems using distributed and hybrid computational platforms. We demonstrate the coupling of graph-based linear scaling electronic structure theory, as implemented in the Scalable Ecosystem, Driver, and Analyzer for Complex Chemistry Simulations (SEDACS), with semiempirical quantum chemistry methods as implemented in the PySEQM code, with Graphics Processing Unit (GPU) acceleration. This powerful combination enables efficient, scalable electronic structure calculations over many nodes, significantly reducing computational cost while naturally harnessing parallelism. Detailed analyses of parallelization efficiency, computational accuracy, and communication overheads are provided, highlighting an order-of-magnitude speedup for systems of up to 10,000 atoms.
Keyword:
MATRIX-MATRIX MULTIPLICATION
LINEAR SCALING COMPUTATION
ELECTRONIC-STRUCTURE
DENSITY-MATRIX
GRAMICIDIN-S
MODELING PHOTOPHYSICS
NDDO APPROXIMATIONS
SPARSE
IMPLEMENTATION
OPTIMIZATION
期刊
IF:
5.5
论文数:
1.1W
被引数:
5.4W
机构
引用论文
.png){kind=icon}
Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics具有半经验量子力学的动态响应化学哈密顿量的深度学习
OAAI
OAAI分享收藏分享收藏Highly Resolved Spectra of Gas-Phase Gramicidin S: A Benchmark for Peptide Structure Calculations
PREAI
分享收藏分享收藏Optimization of parameters for semiempirical methods VI: more modifications to the NDDO approximations and re-optimization of parameters半经验方法的参数优化VI: 对NDDO近似值的更多修改和参数的重新优化OAAI
OAAI分享收藏
查看更多内容