About

Weilu Gao is an Assistant Professor in the Department of Electrical & Computer Engineering at the University of Utah. His research focuses on photonics, optoelectronics, nanomaterials, and nanostructures. His work involves developing advanced photonic and optoelectronic devices, exploring nanomaterials and nanostructures, and contributing to the understanding and application of these technologies in various fields. As part of his academic role, he engages in research that advances the development of nanomaterials and nanostructures, with an emphasis on their integration into practical devices and systems.

Research topics

• Nanotechnology
• Materials science
• Computer Science
• Engineering
• Environmental science
• Optoelectronics
• Chemistry
• Process engineering
• Optics

Selected publications

• Mesh effect in diffractive optical processor

  2026-01-28

  articleSenior author

  A diffractive optical processor (DOP), built from multiple cascaded, programmable free-space diffractive optical components, is capable of performing a broad range of machine learning (ML) tasks in high-throughput and energy-efficient manners. However, transferring simulation-trained designs to hardware with high fidelity remains challenging because differentiable surrogate models often simplify underlying physics for efficient computing, leading to systematic performance degradation upon deployment. Here, we identify a previously ignored factor contributing to this simulation-hardware mismatch, namely the mesh effect arising from how physical hardware features are discretized in differentiable models. This mesh effect becomes pronounced when the unit size of the programmable elements is not commensurate with the operation wavelength. We develop an analytic description of the mesh effect and validate it through numerical studies and experiments on two representative ML tasks, including handwritten-digit classification and solving the two-dimensional Darcy-flow partial differential equation. We show that the common practice of directly using the physical pixel size as the simulation discretization degrades experimental accuracy. To address this, we develop a fully differentiable fine-meshing strategy that refines the computational grids in simulation models, improving deployment fidelity and reducing discrepancy between simulations and experiments. These results highlight discretization-aware training as a practical route to robust, scalable optical learning hardware.

  PublisherDOI

• A wafer-scale ultrasensitive programmable chiroptical sensor

  arXiv (Cornell University) · 2026-01-16

  preprintOpen accessSenior author

  Chiroptical enantioselective sensing is gaining traction across various applications. However, intrinsic molecular chiroptical responses are weak, and existing amplification approaches add synthesis, manufacturing, or operational complexity that limits sensitivity, scalability, and dynamic control. Here, we present a fundamentally new sensing paradigm merging adsorption-driven chirality induction with wafer-scale optical transduction in a programmable heterostructure containing twisted aligned carbon nanotubes (CNTs) and phase change materials (PCMs). Chiral molecules adsorb onto CNTs to form chiroptically active composites that are macroscopically assembled by alignment and rotational stacking, yielding large ultraviolet circular dichroism (CD). We resolve molecule concentration and handedness in a single device without lithography, hotspot delivery, or differential protocols, achieving sub-$μ$M sensitivity for CD-silent glucose and chiral amino acids enabled by $>10^5\,\mathrm{M^{-1}}$ adsorption constants. We validate adsorption using molecular dynamics simulations, reproduce experimental results using chiral transfer matrix simulations, and realize sensor programmability by tuning the PCM layer. This platform enables cost-effective in-situ enantiomer monitoring in aqueous environments.

  PublisherDOI

• A wafer-scale ultrasensitive programmable chiroptical sensor

  ArXiv.org · 2026-01-16

  articleOpen accessSenior author

  Chiroptical enantioselective sensing is gaining traction across various applications. However, intrinsic molecular chiroptical responses are weak, and existing amplification approaches add synthesis, manufacturing, or operational complexity that limits sensitivity, scalability, and dynamic control. Here, we present a fundamentally new sensing paradigm merging adsorption-driven chirality induction with wafer-scale optical transduction in a programmable heterostructure containing twisted aligned carbon nanotubes (CNTs) and phase change materials (PCMs). Chiral molecules adsorb onto CNTs to form chiroptically active composites that are macroscopically assembled by alignment and rotational stacking, yielding large ultraviolet circular dichroism (CD). We resolve molecule concentration and handedness in a single device without lithography, hotspot delivery, or differential protocols, achieving sub-$μ$M sensitivity for CD-silent glucose and chiral amino acids enabled by $>10^5\,\mathrm{M^{-1}}$ adsorption constants. We validate adsorption using molecular dynamics simulations, reproduce experimental results using chiral transfer matrix simulations, and realize sensor programmability by tuning the PCM layer. This platform enables cost-effective in-situ enantiomer monitoring in aqueous environments.

  PublisherOA PDF

• Chip-Scale Aligned Chiral Carbon Nanotubes Exhibiting Giant Second Harmonic Generation

  ACS Nano · 2026-05-18

  preprintOpen access

  pm/V for a perfectly aligned CNT crystal. Our calculations based on many-body theory correctly estimate the spectrum and magnitude of such excitonically enhanced optical nonlinearity. These results are promising for the development of scalable chiral-CNT electronics and nonlinear photonics.

  PublisherOA PDFDOI

• Mesh effect in diffractive optical processor

  2026-01-28

  articleSenior author

  A diffractive optical processor (DOP), built from multiple cascaded, programmable free-space diffractive optical components, is capable of performing a broad range of machine learning (ML) tasks in high-throughput and energy-efficient manners. However, transferring simulation-trained designs to hardware with high fidelity remains challenging because differentiable surrogate models often simplify underlying physics for efficient computing, leading to systematic performance degradation upon deployment. Here, we identify a previously ignored factor contributing to this simulation-hardware mismatch, namely the mesh effect arising from how physical hardware features are discretized in differentiable models. This mesh effect becomes pronounced when the unit size of the programmable elements is not commensurate with the operation wavelength. We develop an analytic description of the mesh effect and validate it through numerical studies and experiments on two representative ML tasks, including handwritten-digit classification and solving the two-dimensional Darcy-flow partial differential equation. We show that the common practice of directly using the physical pixel size as the simulation discretization degrades experimental accuracy. To address this, we develop a fully differentiable fine-meshing strategy that refines the computational grids in simulation models, improving deployment fidelity and reducing discrepancy between simulations and experiments. These results highlight discretization-aware training as a practical route to robust, scalable optical learning hardware.

  PublisherDOI

• A multimodal large language model for materials science

  Nature Machine Intelligence · 2026-04-24

  articleOpen access

  Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics and beyond. Integrating material structure data with language-based information through multimodal large language models (LLMs) offers great potential to support these efforts by enhancing human–artificial intelligence interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multimodal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat uses a bridging module to effectively align a pretrained universal machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat greatly improves performance in material property prediction and human–artificial intelligence interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis. Tang et al. introduce MatterChat, a multimodal framework effectively integrating material structural data with large language models. It achieves high-precision property predictions and provides interpretable reasoning to accelerate materials discovery.

  PublisherOA PDFDOI

• Carbon-Nanotube/β-Ga₂O₃ Heterojunction PIN Diodes

  ACS Applied Electronic Materials · 2025-08-15 · 2 citations

  article

  β-Ga2O3 is gaining attention as a promising semiconductor for next-generation high-power, high-efficiency, and high-temperature electronic devices, thanks to its exceptional material properties. However, challenges such as the lack of viable p-type doping have hindered its full potential, particularly in the development of ambipolar devices. This work introduces a heterojunction diode (HD) that combines p-type carbon nanotubes (CNTs) with i- and n-type β-Ga2O3 to overcome these limitations. For the first time, a CNT/β-Ga2O3 hetero-p-n-junction diode is fabricated. Compared to a traditional Schottky barrier diode (SBD) with the same β-Ga2O3 epilayer, the CNT/β-Ga2O3 HD demonstrates significant improvements, including a higher rectifying ratio (1.2 × 1011 ), a larger turn-on voltage (1.96 V), a drastically reduced leakage current at temperatures up to 300 °C, and a 26.7% increase in breakdown voltage. Notably, the CNT/β-Ga2O3 HD exhibits a low ideality factor of 1.02, signifying an ideal interface between the materials. These results underline the potential of CNT/β-Ga2O3 heterojunctions for electronic applications, offering a promising solution to the current limitations in β-Ga2O3-based devices.

  PublisherDOI

• MatterChat: A Multi-Modal LLM for Material Science

  ArXiv.org · 2025-02-18 · 5 citations

  preprintOpen access

  Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material structure data with language-based information through multi-modal large language models (LLMs) offers great potential to support these efforts by enhancing human-AI interaction. However, a key challenge lies in integrating atomic structures at full resolution into LLMs. In this work, we introduce MatterChat, a versatile structure-aware multi-modal LLM that unifies material structural data and textual inputs into a single cohesive model. MatterChat employs a bridging module to effectively align a pretrained machine learning interatomic potential with a pretrained LLM, reducing training costs and enhancing flexibility. Our results demonstrate that MatterChat significantly improves performance in material property prediction and human-AI interaction, surpassing general-purpose LLMs such as GPT-4. We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.

  PublisherOA PDFDOI

• Free‐space Optical Computing Systems

  Annalen der Physik · 2025-03-22 · 2 citations

  articleOpen accessSenior authorCorresponding

  Abstract Free‐space optical systems are emerging as a hardware platform for high‐throughput and energy‐efficient computing. In this review, the pioneering works are first introduced to lay the foundation for the principles and architectures of systems. The modern hardware implementations of two types of optical computing systems, matrix, and vector multiplication systems and diffractive optical neural network systems, are covered from material, device, and system perspectives. Further, the system deployment to various applications is also discussed. This review serves as an introduction and guideline to the current progress of developing and utilizing free‐space optical computing systems in various domains.

  PublisherOA PDFDOI

• MatterChat: A Multi-Modal LLM for Material Science

  Research Square · 2025-05-20 · 11 citations

  preprintOpen access
  PublisherOA PDFDOI

Recent grants

• Reconfigurable Diffractive Optical Neural Networks with Phase Change Material based Photonic Device

  NSF · $375k · 2023–2027

• Excitonic electroabsorption effects in macroscopically aligned carbon nanotubes

  NSF · $251k · 2023–2026

Frequent coauthors

• Junichiro Kono

  Rice University

  93 shared

• Cunxi Yu

  University of Maryland, College Park

  30 shared

• Ruiyang Chen

  26 shared

• Minhan Lou

  University of Utah

  26 shared

• Yingheng Tang

  University of Utah

  23 shared

• Jichao Fan

  23 shared

• Kazuhiro Yanagi

  22 shared

• Yohei Yomogida

  Hokkaido University

  20 shared

Labs

• Weilu Gao LaboratoryPI

Education

• Ph.D., Electrical and Computer Engineering

  Rice University

  2016