蒋彬 -九游官网首页进入

男， 中国科学技术大学， 教授/研究员/教授级高工或同等级别

学习/工作经历

2003.9-2007.6 南京大学化学化工学院 化学 学士
2007.9-2012.6 南京大学化学化工学院 化学 博士
2012.8-2015.11 美国新墨西哥大学 博士后
2015.12至今 中国科学技术大学 教授

研究领域和兴趣

化学反应动力学

主要业绩

蒋彬教授致力于发展高精度、高维度、线性标度的神经网络势能面与多原子分子内自由度有效耦合的态-态表面散射量子动力学方法，揭示了多原子分子普遍存在的表面诱导选择性振动传能的微观机制，预言了振动激发对多原子分子多通道解离产物分支比的调控，为分子与金属表面振动传能提供了新的见解。在 science, nat. chem., nat. commun., pnas, jacs, prl, angew. chem., chem. sci., jacs au等高水平期刊发表论文160余篇，被引7700余次，h-index 49。2017年获基金委优秀青年基金支持，2023年获杰出青年基金支持，2021年度获中国化学会唐敖庆理论化学青年奖，2023年获中国化学会张存浩化学动力学青年科学家奖。

代表成果

(1) chen, jialu; zhou, xueyao; zhang, yaolong; jiang, bin*; vibrational control of selective bond cleavage in dissociative chemisorption of methanol on cu(111), nature communications, 2018, 9: 0-4039.
(2) zhang, liang; jiang, bin*; efficient vibrational energy redistribution between stretching modes: state-to-state quantum scattering of h2o from cu(111), physical review letters, 2019, 123(10): 106001.
(3) yin, rongrong; jiang, bin*, mechanical vibrational relaxation of no scattering from metal and insulator surfaces: when and why they are different, physical review letters, 2021, 126, 156101.
(4) zhang, yaolong; xia, junfan; jiang, bin* ; physically motivated recursively embedded atom neural networks: incorporating local completeness and nonlocality, physical review letters, 2021, 127(15): 156002
(5) box, connor l.#; zhang, yaolong#; yin, rongrong; jiang, bin*; maurer, reinhard j.*; determining the effect of hot electron dissipation on molecular scattering experiments at metal surfaces, jacs au, 2021, 1(2): 164-173.
(6) zhang, yaolong; maurer, reinhard j*; guo, hua; jiang, bin*; hot-electron effects during reactive scattering of h2 from ag(111): the interplay between mode-specific electronic friction and the potential energy landscape, chemical science, 2019, 10(4): 1089-1097.
(7) zhang, yaolong; hu, ce; jiang, bin*; embedded atom neural network potentials: efficient and accurate machine learning with a physically inspired representation, the journal of physical chemistry letters, 2019, 10(17): 4962-4967.
(8) zhang, y.; jiang, b*. universal machine learning for the response of atomistic systems to external fields. nature communications 2023, 14 (1), 6424. doi: 10.1038/s41467-023-42148-y.
(9) li, c.; li, y.*; jiang, b*. first-principles surface reaction rates by ring polymer molecular dynamics and neural network potential: role of anharmonicity and lattice motion. chemical science 2023, 14 (19), 5087-5098, 10.1039/d2sc06559b. doi: 10.1039/d2sc06559b.
(10) zhang, y.; lin, q.; jiang, b.* atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: efficiency, representability, and generalization. wires computational molecular science 2023, 13 (3), e1645. doi: https://doi.org/10.1002/wcms.1645.

*以上信息由高级会员个人更新和维护。