Professor

Phùng Thị Việt Bắc

Affiliation: VinUniversity, Hanoi, Viet Nam

Research Management Office / College of Engineering and Computer Science / Center for Environmental Intelligence

Introduction

Dr. Phung Thi Viet Bac received her PhD in Computational Science and Physics from Kanazawa University, Japan in 2009, and her MSc and BSc in Chemistry from Vietnam National University, Hanoi and Hanoi National University of Education in 2005 and 2002, respectively. After receiving her PhD, she continued her postdoctoral research at Kanazawa University and the National Institute of Industrial Science and Technology (AIST) from 2009 to 2012. Dr. Viet Bac’s research focuses on Material Simulation Design, applications in sensors, energy conversion and storage. In 2013, she was a researcher at the Japan Advanced Institute of Science and Technology (JAIST) working in the fields of photovoltaic cells, low-dimensional materials and nano-devices. After that, Dr. Viet Bac spent 4 years teaching and researching at Fukui University, Japan. From 2018 to 2023, she worked at Vietnam Japan University – Vietnam National University, Hanoi as a lecturer and researcher. She led a research group on Multi-scale Material Design and Simulation at the Institute for Sustainable Science. She was the founding Executive Scientific Secretary of the VinFuture Prize Foundation, established in 2021. From 2023, she joined VinUniversity as a Head of Research Management Office and faculty member of the College of Engineering and Computer Science. She currently serves as Director of Research and Innovation and Chief Executive Officer of the Center for Environmental Intelligence (CEI). Her research focuses on the design and development of advanced materials for next-generation energy storage batteries, recycled materials, green hydrogen production, and sustainable clean energy solutions. Dr. Viet Bac is a member of the American Chemical Society (ACS) and the Materials Research Society (MRS), USA.

Affiliation: VinUniversity, Hanoi, Viet Nam

Research Output

Learnable features for predicting properties of metal-organic frameworks with deep neural networks

Nguyen Van-Quyen, Le Phuoc-Anh, Nguyen Phi Long, Pham Tien-Lam, Bac Phung Thi Viet, Novoselov Kostya S., El Ghaoui Laurent
  • density functional theory
  • DFT
  • machine learning
  • materials informatics
  • metal-organic frameworks
  • MOFs
  • neural network model

One-step preparation of Ni–Co binary metal sulfides on reduced graphene oxide for all-solid-state supercapacitor devices with enhanced electrochemical performance

Manh Thao Pham, Nguyen Van Nghia, Phung Viet Bac T., Ngo Thi Lan, Ngo Quy Quyen, Le The Son, Doan Tien Phat, Tran Quang Dat, Nguyen Van Tuan, To Van Nguyen
  • All-solid-state supercapacitor
  • High energy density
  • Hybrid supercapacitor
  • Synergistic effect

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Keyphrases

  • Biomass
  • Energy storage
  • Pore structure
  • Pseudo-capacitive
  • Sugarcane bagasse
  • Adsorption
  • Benzene
  • Butanone
  • Ethanol
  • Propanal
  • VOC
  • WS2, DFT
  • CuBDC
  • CuBTC
  • Gel polymer electrolyte
  • Metal organic frameworks
  • Supercapacitors
  • Anchoring materials
  • Density functional theory
  • MXenes
  • Sodium sulfide clusters
  • Sodium-sulfur batteries
  • Chemical Engineering
  • Environmental engineering
  • Water geochemistry
  • Water resources engineering
  • cathode materials
  • O3-type layered structure
  • performance
  • sodium-ion batteries
  • sol–gel process
  • zinc doping
  • ammonia
  • co-adsorption
  • DFT calculations
  • graphene/hexagonal boron nitride heterostructures
  • water
  • Hybridized device
  • Hydrovoltaic capacitor
  • Piezoelectric
  • Sensor system
  • Solid-state battery
  • density functional theory
  • DFT
  • machine learning
  • materials informatics
  • metal-organic frameworks
  • MOFs
  • neural network model
  • All-solid-state supercapacitor
  • High energy density
  • Hybrid supercapacitor
  • Synergistic effect

Computer Science

  • Forestry