• Lithium–Air Batteries: TiC MXene High Energy Density Cathode for Lithium–Air Battery (Adv. Theory Simul. 9/2018)

    Ab-initio Engineering & Research

    Ab-initio Engineering: We are devoted to design and optimize distributed generations according to the first principle. We aim to develop and design a novel distributed management system based on AI techniques and novel predictive analytics to manage energy storage, renewable energy, distributed generators, and etc.

    Research Interests: Renewable energy and AI will define the future of humanity. My research spectrum covers the discovery of novel energy material, the design of smart battery management system, the study of complex power grids, and development of novel algorithms for complex quantum dynamics, and beyond. We focus on applying the cutting-edge physics-based computational models, novel algorithms, and AI techniques in our research.

Research

Research

  1. Battery SOC/SOH Models

  2. Smart City and Predictive Analytics

  3. Motif-based Complex Network Analysis in Power Systems

  4. HOSVD for Feature Engineering in Machine Learning

  5. Financial Market and Predictive Analytics

• Quantum Dynamics Simulations

1. Tensor Decomposition and Low-rank Approximation in High-Dimensional Non-adiabatic Quantum Dynamics and Exciton Transport Simulation (in collaboration with Batista)

MP/SOFT Multi-dimensional Wavefunction Propagation Scheme

2. Gaussian Process in Condensed Matter Systems.

• Nanophotonics and Enhancement of Excitation Energy Transfer:

1. Simulation of Evanescent Near Field (in collaboration with Prof. An)

2. Excitation Energy Transfer in vicinity of Metal Surfaces

3. Machine Learning Algorithm predicting the physics properties of the nanoparticles, particle heat transfer at the nanosize through evanescent near field.

• Novel Energy Materials

1. MAOX

2. TiC Maxene as Li-Air Battery Cathode

Introduction

The global electricity supply is also being transformed by the rise of variable renewable sources of generation such as wind and solar PV. While this puts electricity at the forefront of the clean-energy transitions, providing access to the nearly 1 billion currently deprived, helping cut air pollution and meet climate goals, these changes will require a new approach to how power systems are designed and how they operate. Otherwise, rising electrification could result in less secure energy systems, underscoring the urgent need for policy action in this critical sector.

Electrification of end uses is a promising pathway to decarbonising energy use

Electricity today accounts for 19% of total final consumption of energy, a share that is set to increase as demand growth for electricity outpaces all other fuels. In the New Policies Scenario, the share reaches 24% in 2040, a far cry from full electrification. While there is considerable scope to push electrification beyond this level, not all end-uses can be readily electrified, such as high-temperature heat demand in industry, long-haul aviation and shipping, where electrification is harder to achieve due to either economic or technical barriers.

Introducing the Future is Electric Scenario (FiES)

Policy choices will have a great impact on how the electricity sector develops in the future, especially what key levers are used to boost electricity demand growth. Policies and regulations play a determining role in unlocking higher electrification: encouraging efforts to accelerate the rollout of electric charging infrastructure for vehicles, simplifying switching to electric heating in both buildings and industry, or pushing to achieve universal access to electricity or improving standards of living.

The analysis this year introduces the Future is Electric Scenario (FiES) to examine what would happen to electricity demand if economic opportunities for electrification were maximized. For instance, in the FiES by 2040, almost half of the car fleet goes electric; electricity makes rapid inroads into heating needs for buildings and industry; a digital economy connects nearly all consumer devices and appliances; and full electricity access is achieved.

Copyright @ International Energy Agency

Read the full report at

The blog: the new economics of energy storage
Team Introduction

Team

Team Leader

Prof. Xin Chen

陕西百人/三秦学者
Center of Nanomaterials for Renewable Energy,
School of Electrical Engineering,
Xi'an Jiaotong University
Visiting Professor, Beijing Computational Science Research Center, 2017
Visiting Scholar, Northwestern University (Mark Ratner), 2015
Postdoc, Chemistry Department and Center for Excitonics, RLE, MIT (Robert Silbey and Jianshu Cao), 2008-2014
Bank of Canada, Department of Financial Markets, 2011-2012
PhD, Yale University (Victor Batista), 2003-2008
MS, UBC, 2000-2003
BS, Nanjing University(强化班), 1995-1999

Team Members

Long Huo, 霍龙 (PhD Candidate, School of Electrical Engineering, 2018)
Yuxuan Sui, 隋宇轩 (Master Student, School of Electrical Engineering, 2016)
Yawei Ma, 马亚伟 (Master Student School of Electrical Engineering, 2016)
Yadong Zhang, 张亚东 (Master Student, School of Electrical Engineering, 2018)

Joint Supervision with Prof. Niu

Zhenyu Wang (PhD Student of EE) National Scholarship Awardee (国家奖学金)

Undergrad Team

Engineering Team

Chenye Zou, 邹晨晔, School of Electrical Engineering (电气72)
Siyu Chen, 陈思宇, School of Electrical Engineering (电气72)

Quantum Dynamics and NanoPhotonics Team

Wenqing Wang, 王文清, School of Electrical Engineering (电气69)
Rui Tian, 田瑞, Physics Department(物试61)
Xin Wang, 王鑫, Physics Department(光信息61)
Changhao Meng, 孟昌昊, Physics Department(材物 61)

Alum

LuXing Jiang (Undergraduate Student, School of Electrical Engineering, 2016), Robotics Institute, CMU (卡耐基梅隆大学,机器人研究所)

Publications and Software

Publications and Software

Publications

  1. Formation mechanism of rectangular-ambulatory-plane TiO 2 plates: an insight into the role of hydrofluoric acid, Chemical Communications 54 (52), 7191-7194, 2018
  2. Charge-redistribution-induced new active sites on (0 0 1) facets of α-Mn2O3 for significantly enhanced selective catalytic reduction of NOx by NH3, Journal of Catalysis 370, 30-37, 2019
  3. Exploring the MAOX Phases for Next-Generation Energy Conversion Materials, ZY Wang, Xin Chen*, CM Niu, ACS Energy Letters (Submitted)
  4. Layered Hexagonal Oxycarbides, Mn+1AO2Xn (M=Sc, Y, La, Cr and Mo, A=Ca, X=C): Unexpected Photovoltaic Ceramics, Zhenyu Wang, Xin Chen*, Chunming Niu, Journal of Physical Chemistry C, 122, 14240 (2018)
  5. TiC MXene High-energy Density Cathode for Lithium-air Battery, Zhenyu Wang, Xin Chen*, Fei Shen, Xiaogang Hang and Chunming Niu, Advanced Theory and Simulations, 1, 1800059 (2018)
  6. Amrit Poudel, Xin Chen*, and Mark A. Ratner*, “Resonant energy transfer under the influence of the evanescent field from the metal”, The Journal of Chemical Physics 146, 244115 (2017)
  7. A. Poudel, Xin Chen*. M. A. Ratner*, “Enhancement of Resonance Energy Transfer Due to Evanescent-wave from the Metal”, J. Phys. Chem. Lett., 7, 955–960 (2016)
  8. Shuang Gao, Weichen Wang, Laju Bu, Ling Zhou, Xin Chen, Demei Yu, Shengtao Li, Guanghao, Lu, Film-depth-dependent Light Absorption and Charge Transport for Polymer Electronics: A Case Study on Semiconductor/Insulator Blends by Plasma Etching, Advanced Electronic Materials, 2, 1600359 (2016)
  9. Zhenyu Wang, Xin Chen, Yonghong Cheng and Chunming Niu, “Adsorption and Deposition of Li2O2 on the Pristine and Oxidized TiC Surface by First-principles Calculation”, The Journal of Physical Chemistry C, 119, 25684 (2015)
  10. Ziheng Lu, Chi Chen, Zarah Medina Baiyee, Xin Chen, Chunming Niu and Francesco Ciucci, “Defect chemistry and lithium transport in Li3OCl anti-perovskite superionic conductor”, Physical Chemistry Chemical Physics, 17, 32547 (2015)
  11. Xin Chen*, The rigorous stochastic matrix multiplication scheme for the calculations of reduced equilibrium density matrices of open multilevel quantum systems, J. Chem. Phys., 140, 154101 (2014)
  12. Xin Chen*, Jianshu Cao, and Robert J. Silbey. “A Novel Construction of Complex-valued Gaussian Processes with Arbitrary Spectral Density and its Application to Excitation Energy Transfer” J. Chem. Phys. 138, 224104 (2013).
  13. Xin Chen and Robert J. Silbey. “Excitation Energy Transfer in Non-Markovian Dynamical Disordered Environment: Localization, Narrowing and Transfer Efficiency” J. Phys. Chem. B 115, 5499 (2012).
  14. Xin Chen* and Robert J. Silbey. “Effect of Correlation of Local Fluctuations on Exciton Coherence” J. Chem. Phys. 132, 204503(2010).
  15. Xin Chen and Victor Batista. “The MP/SOFT Methodology for Simulations of Non-adiabatic Quantum Dynamics: Application to the Photo-isomerization of the Retinyl Chromophore in Rhodopsin” Photochem. Photobiol. 190, 274 (2007).
  16. Xin Chen and Victor Batista. “Matching Pursuit Split Operator Fourier Transform Simulations of Non-adiabatic Excited State Quantum Dynamics in Pyrazine” J. Chem. Phys. 125, 124313 (2006).
  17. Xin Chen and Victor Batista. “Matching Pursuit/ Split Operator Fourier Transform Computations of Thermal Correlation Functions” J. Chem. Phys. 122, 64102 (2005).
  18. Xin Chen and Mark Thachuk. “Collision-induced alignment of H2O+ drifting in helium” J. Chem. Phys. 124, 174501 (2006).
  19. Xin Chen and Mark Thachuk. “Ground and First-Excited Global Potential Energy Surfaces of H2O+-He Complex: Predictions of Ion Motilities” Int. J. of Quantum Chem. 101, 1 (2005).
  20. Xin Chen, R. Araghi, R. Baranowski, and Mark Thachuk. “Collision-induced alignment of NO+ drifting in argon: Calculated distribution functions and microscopic quadrupole alignment parameters” J. Chem. Phys. 116, 6606 (2002).

Software

1. Low-rank approximation of high dimensional Wavepacket Simulations
2. MP/SOFT implement in Julia
3. Excitation Energy Transfer Simulation in couple with Metal Surface of Evanescent field
4. Battery SOC/SOH Predictive/Simulation Analytic
5. Financial Index/Price Predictive Analytic
6. Smart City/Complex City Simulation

Recent

News

Aug | 2016 | 上海

Invited Talk at 37th Progress in Electromagnetics Research Symposium (PIERS)

Summer | 2016 | Yale University

The 2016 TSRC Summer School on Fundamental Science Alternative Energy organized by Prof. Batista and Brudvig at Yale (6/21–6/25) calls for sign-up

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Our Paper on the resonant energy transfer is accepted by the Journal of Physical Chemistry Letters.

April | 2016 | 上海

Invited Talk, Physics Department, Fudan University. Host, Prof. Zhenghu An

Aug | 2016 | Philadelphia

Oral Talk, 252nd ACS National Meeting & Exposition

Aug | 2016 | Seattle

Oral Talk, Theory and Applications of Computational Chemistry (TACC)

Dec | 2016 | 深圳

长邀请报告, 2016复杂体系计算统计力学研讨会会后

Jan | 2017 | 北京

Invited Talk, Chemistry Department, Beijing University, Host, Prof. Jian Liu

April | 2017 | San Francisco

Oral Talk, 253nd ACS National Meeting & Exposition

April | 2017 | 南京

Invited Talk, 理论与计算化学研究所, Nanjing University

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蒋陆行(311班, 2014), 被卡耐基梅隆大学(CMU)ECE 和 Robotics Institue 两个program录取. 将去 Robotics Institue进行人工智能相关的研究学习。 蒋陆行作为本科实现生,在我们研究小组参与了电池状态(SOC)的预测的理论和深度学生算法研究工作。Congrats!!!

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马亚伟 被中科曙光“先进计算菁英班”录取

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张亚东关于SOC预测的毕设课题获得优秀

May | 2018 | 北京

邀请报告, Mini-workshop on Nonadiabatic Chemistry,北京大学

Spet | 2018 | 上海

邀请报告,电子的非平衡态及应用,东方科技论坛

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我们的论文” TiC MXene High Energy Density Cathode for Lithium–Air Battery " 被 Advanced Theory and Simulations 接受作为封面

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我们的论文“Layered Hexagonal Oxycarbides, Mn+1AO2Xn : Unexpected Photovoltaic Ceramics”被JPCC 接受

Join Us

Recruitment

undergrad
2-3年级本科

Master
硕士研究生

Ph.D
博士研究生

Postdoc
博士后

我们欢迎本科2-3年纪学生来我们小组参加实习研究。我们当前有人工智能应用和量子力学模拟/光子材料研究两个方面有5个电气和物理方向的本科2-3年级学生。大家可以参考团队(Team)信息。
i. 如果对人工智能应用方向感兴趣,申请人可以参考 qinren.tech 招聘页
ii.如果对量子动力学/光子材料/光伏材料(MAOX)模拟感兴趣,请直接联系我, xin.chen.nj @ gmail.com。你需要有一定的量子力学,固体物理和编程的背景。
我每年会有2个硕士,2个博士位置。如果你有理论物理,计算模拟,软件工程,应用数学方面的背景,且对复杂网络模拟,新光伏材料计算预测,电池储能管理等感兴趣,欢迎来咨询。也欢迎博士毕业生来询问博士后位置。

We constantly have research positions for sophomore/junior undergraduate students with physics and programming backgrounds. There are two directions: 1. AI application in battery management and smart city (pls refer to the recruitment page in qinren.tech), 2. Quantum Dynamics/Photonics/PV Material Simulations.

Join My Team!

Contact
Facilities

Facilities

1. HPC server (32 Dual CPU(2650 v3) Nodes/640 Cores)

2. GPU HPC Server (8 Telsa K80 GPUs) with the Python 3.6 and Tensorflow/Pytorch Programming Environment
3. COMSOL 5.4a (Parallel version), Wave Optic Module
4. Computational Chemistry and Material Simulation software’s: VASP, ADF, Gaussian, Lampps, etc.