Doctoral Researchers

B13: Li Chen

E-Mail: li.chen2@tu-dresden.de
Phone: +49 351 46331425
Office: HAL 122 / MBZ 106

I am motivated by the question of how artificial intelligence can accelerate scientific discovery beyond human intuition. My research aims to use machine learning to navigate complex molecular spaces for inverse design, and to uncover hidden physical patterns in gas sensor signals. Ultimately, I seek to build data-driven models that not only perform well, but also provide interpretable insights into underlying scientific mechanisms.

portrait photo Li Chen
Project Topic: Neuromorphic sensing via 2D-materials-nanoparticle networks (B13)

Supervisors:

Mentor: Prof. Gianaurelio Cuniberti
Co-Mentor: Prof. Andreas Fery

Accurate identification of gas type and concentrations, particularly gas mixtures, is essential for applications ranging from environmental monitoring to medical diagnostics. To overcome the challenges in the complex sensing scenario, we aim at developing robust neuromorphic sensing systems to interpret the signals under harsh sensing environment.

The expected result is an AI-driven neuromorphic sensing framework capable of accurate identification and quantification of single gases and gas mixtures under harsh conditions. By leveraging temporal and nonlinear signal features, the system is anticipated to achieve higher accuracy, improved selectivity, and lower energy consumption compared with conventional machine-learning approaches.

 

Education
(2014 - 2018) Bachelor at Sichuan University
(2018 - 2022)

Master at TU Dresden

  • including a short research stay at the Chair of Materials Science and Nanotechnology
(2023 – now) PhD student at TU Dresden

 

Publications
  • Li Chen et al. Computational Design of the Electronic Response for Volatile Organic Compounds Interacting with Doped Graphene Substrates, Nanomaterials. 2024 14 (22), 1778

  • Li Chen et al. MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics, Scientific data 2025 12 (1), 324

  • Li Chen et al. Interpretable Machine Learning for Quantum-Informed Property Predictions in Artificial Sensing Materials, arXiv 2026 preprint arXiv:2601.00503

  • Wei Wang, Li Chen et al. Highly Sensitive and Selective Zinc-Based Metal–Organic Framework Derivatives Gas Sensors for Trace H2S Detection, ACS sensors 2025 10 (10), 7584-7598

  • A Shitrit, Y Sukhran, N Tverdokhleb, Li Chen et al. Monosaccharide‐Derived Enantioselectivity in SWCNT Chemoresistive VOC Sensing, Chemistry–A European Journal 2025 31 (72), e02553