Funding & Support

Biomimetic Nanofluidic Chemical Sensors

We develop computational screening tools for electrochemical affinity (EA) biosensors to accelerate the detection of diverse disease biomarkers at the point of care. By integrating GPU-accelerated molecular dynamics simulations with transformer-based deep learning, we build interface-aware ML prediction models for interfacial conformation and binding affinity, enabling automated, high-throughput screening of large aptamer libraries against diverse biomarkers under realistic electrode-interface conditions.

Bio-Inspired Chemical Sensor
Bio-inspired chemical sensing: from the biological olfactory system to electrochemical biosensors with molecular receptor engineering.

Sub‑projects

  • Interfacial Aptamer Conformation: All-atom MD simulations of DNA aptamers tethered to Au electrodes to understand how electrode interfaces influence conformation and binding.
  • Analyte Accessibility: Free energy calculations to quantify how electrode interfaces influence target availability near tethered receptors.
  • Interface-Aware ML Framework: Transformer-based deep learning to predict electrode-tethered aptamer conformations without new MD simulations for each candidate.
Ionic Neuromorphic Computing for Energy‑Efficient AI

We develop ionic neuromorphic computing systems that mimic the human brain’s iontronic mechanisms to achieve ultra‑low‑power artificial intelligence. Our research focuses on mimicking the brain’s fundamental ion transport processes, such as electrical, chemical, and mechanical gating, memory retention, molecular selectivity, and collective chain reactions.

Ionic Neuromorphic Computing
Schematic of neural signaling in a biological neuron, a software artificial neuron, and physical neuron realized in ionic neuromorphic computing.

Sub‑projects

  • Å‑Scale Ionic Memristors: Synthetic ion channels that switch and retain memory through precisely controlled ion flow.
  • Physical Neural Networks: Building energy‑efficient ionic circuits as AI hardware.
  • Multiscale Simulation & Theory: Molecular simulations to reveal sub‑1 nm ion transport mechanisms and guide device design.
Machine Learning Molecular Dynamics

We develop machine‑learning molecular dynamics (ML‑MD) simulations that enable near‑DFT accuracy with near-MD speed of reactive atomistic simulation. By training neural network force fields on high‑quality ab initio datasets, we aim to facilitate the computational design of next‑generation advanced devices, including brain-inspired ionic computing, molecular separations, bioinspired sensing, and energy generation/storage.

ML-MD Simulation for Energy Applications
Multi‑scale atomistic simulation using machine‑learning force fields to enable energy and molecular biomimetic applications.

Sub‑projects

  • ML‑Driven Molecular Dynamics: Reactive simulations trained on high‑quality DFT datasets for energy materials.
  • Multiscale Energy Applications: Batteries, fuel cells, and osmotic energy‑harvesting.
  • Innovative Biomimetic Devices: Ionic neuromorphic computing, bioinspired sensing/conversion/separation.
Nanopore-Based Molecular Separation for Hydrogen & Rare-Earth Elements

We design bio-inspired nanopore separation systems to selectively extract target molecules, such as rare-earth elements (REEs) and hydrogen gas. Inspired by biological ion channels, we leverage confined electrostatics, molecular binding, entropic barriers, hydration energy, and nanostructured pore geometries to achieve energy-efficient and environmentally friendly molecular separations.

Nanopore-Based Molecular Separation
Bio-inspired nanopore separation for hydrogen and rare-earth element extraction, mimicking the ultra-selective transport of biological ion channels.

Sub‑projects

  • Hydrogen Sieving: Quantum chemistry and MD simulations to design Å‑scale pores for selective H2 separation from gas mixtures.
  • Rare‑Earth Ion Extraction: Free‑energy landscape modeling of rare-earth ion transport through nanostructured membranes.
  • Multi‑Component Competition: Simulation of ion competition and selectivity hierarchies in complex extraction environments.
Generalized Stochastic Transport Theory

We are developing a stochastic transport theory to model and predict the behavior of ion transport through nanopores and membranes. In subatomically localized regions, Boltzmann statistics fail to describe the ensemble-averaged local properties of molecules. By employing Fermi–Dirac statistics, we are creating a generalized stochastic transport framework that accurately reproduces ensemble-averaged molecular dynamics at atomically localized scales.

Fermi-Dirac Transport Theory
Schematic comparison between continuum electrokinetic transport theory and stochastic electrochemical transport theory.

Sub‑projects

  • Fermi–Dirac Transport Theory: Demonstration of the foundational principles of Fermi–Dirac-type transport.
  • Generalized Field Theory of Fermi–Dirac Transport: A continuum-based framework that describes ensemble-averaged molecular dynamics at atomically localized scales.