PhD student
| Phone: | +49 351 46331425 |
| Room: | HAL 122 , MBZ 106 |
| Group: |
computational materials science and theoretical nanophysics
, digital olfaction , computational infrastructure: software, hardware and robot training support |
|
Download contact:
|
Li Chen obtained his Bachelor degree at Sichuan University major in power engineering in June 2018. Then he moved to TU Dresden in September and obtained his Diploma with the same major in October 2022 following by a short research assistant period. Since 2023 June, he is a PhD student at the chair of material science and nanotechnology under the supervision of Prof. Cuniberti.
His main research is dedicated to developing and applying state-of-the-art machine/deep learning algorithms to deal with complex systems spanning over quantum-mechanical molecular interaction to neuromorphic sensing device systems, leading to various application scenario e.g. molecular design and complex sensing signal processing.
Monosaccharide-Derived Enantioselectivity in SWCNT Chemoresistive VOC Sensing
Highly Sensitive and Selective Zinc-Based Metal–Organic Framework Derivatives Gas Sensors for Trace H2S Detection
MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics
Computational Design of the Electronic Response for Volatile Organic Compounds Interacting with Doped Graphene Substrates
PhD student
| Phone: | +49 351 46331425 |
| Room: | HAL 122 , MBZ 106 |
| Group: |
computational materials science and theoretical nanophysics
, digital olfaction , computational infrastructure: software, hardware and robot training support |
|
Download contact:
|
Li Chen obtained his Bachelor degree at Sichuan University major in power engineering in June 2018. Then he moved to TU Dresden in September and obtained his Diploma with the same major in October 2022 following by a short research assistant period. Since 2023 June, he is a PhD student at the chair of material science and nanotechnology under the supervision of Prof. Cuniberti.
His main research is dedicated to developing and applying state-of-the-art machine/deep learning algorithms to deal with complex systems spanning over quantum-mechanical molecular interaction to neuromorphic sensing device systems, leading to various application scenario e.g. molecular design and complex sensing signal processing.
Monosaccharide-Derived Enantioselectivity in SWCNT Chemoresistive VOC Sensing
Highly Sensitive and Selective Zinc-Based Metal–Organic Framework Derivatives Gas Sensors for Trace H2S Detection
MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics
Computational Design of the Electronic Response for Volatile Organic Compounds Interacting with Doped Graphene Substrates
