Main research directions

van der Waals technology

Quantum transport

Transport in double-gated graphite devices

Van der Waals technology and a better theoretical understanding (from graphene physics) allowed us to unravel interesting physics even in such an old material as graphite

Twistronics in 3D (graphite)

Aligning graphite with hBN results in topological Lifshitz transitions, Brown-Zak quantum oscillations, and Hofstadter butterfly 

ABC (Rhombohedral) graphite: probably the simplest topological insulator with gapped bulk and conducting surfaces! It shows clear signatures of strong electronic correlations – no need for moiré! 

Correlations in rhombohedral graphite

Nanoconfined water and nanofluidics

2D nanocapillaries enabled by van der Waals technology

Isolated two-dimensional (2D) crystals can be assembled into designer structures layer-by-atomic-layer in a precisely chosen sequence. Using van der Waals (vdW) vdW technology, we have reported the creation of two-dimensional nanocapillary. It can be viewed as if individual atomic planes were pulled out of a bulk crystal leaving an atomically thin void behind (see figure). This technology offers the smallest possible empty spaces that can vary from just a few angstroms in height up to many nanometers on demand. 

We will be exploring the nanoconfinement effects based on such nanocapillaries system. 

A range of research activities regarding mass transport at the nanoscale:

-- properties of nanoconfined water

-- graphene-liquid interface 

-- ionic transport

-- gas selectivity

Aimed at osmotic energy harvesting

Novel van der Waals materials growth

Our combined machine learning capability with 2D materials growth technique lead to novel material discovery. We produce high quality crystals for academic research, internally and internationally. 

Machine learning enabled materials discovery

Millions of computational materials harboring unique physics and properties await exploration at open science data centers. Future materials science is set to be revolutionized by AI and digital data driven approaches. We aim to leverage AI algorithms, such as variational autoencoders, graph neural networks, and interpretable learning, to autonomously discover materials.