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DOI | 10.1126/science.aaw1147 |
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning | |
Noe, Frank1,2,3; Olsson, Simon1; Koehler, Jonas1; Wu, Hao1,4 | |
2019-09-06 | |
发表期刊 | SCIENCE |
ISSN | 0036-8075 |
EISSN | 1095-9203 |
出版年 | 2019 |
卷号 | 365期号:6457页码:1001-+ |
文章类型 | Article |
语种 | 英语 |
国家 | Germany; USA; Peoples R China |
英文摘要 | Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples in "one shot:" vast computational effort is invested for simulating these systems in small steps, e.g., using molecular dynamics. Combining deep learning and statistical mechanics, we developed Boltzmann generators, which are shown to generate unbiased one-shot equilibrium samples of representative condensed-matter systems and proteins. Boltzmann generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free-energy differences and discovery of new configurations are demonstrated, providing a statistical mechanics tool that can avoid rare events during sampling without prior knowledge of reaction coordinates. |
领域 | 地球科学 ; 气候变化 ; 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000484732700040 |
WOS关键词 | PANCREATIC TRYPSIN-INHIBITOR ; MONTE-CARLO METHOD ; FREE-ENERGY ; TRANSITION ; DYNAMICS |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/202248 |
专题 | 地球科学 资源环境科学 气候变化 |
作者单位 | 1.FU Berlin, Dept Math & Comp Sci, Arnimallee 6, D-14195 Berlin, Germany; 2.FU Berlin, Dept Phys, Arnimallee 14, D-14195 Berlin, Germany; 3.Rice Univ, Dept Chem, POB 1892, Houston, TX 77005 USA; 4.Tongji Univ, Sch Math Sci, Shanghai 200092, Peoples R China |
推荐引用方式 GB/T 7714 | Noe, Frank,Olsson, Simon,Koehler, Jonas,et al. Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning[J]. SCIENCE,2019,365(6457):1001-+. |
APA | Noe, Frank,Olsson, Simon,Koehler, Jonas,&Wu, Hao.(2019).Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning.SCIENCE,365(6457),1001-+. |
MLA | Noe, Frank,et al."Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning".SCIENCE 365.6457(2019):1001-+. |
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