Joyjit Chattoraj

Jian Cheng Wong, Chin Chun Ooi, Joyjit Chattoraj et al.

Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases. For many scientific and engineering problems involving geometrical design, it is desirable for the surrogate models to precisely describe the change in geometry and predict the consequences. In that context, we develop graph neural networks (GNNs) as fast surrogate models for physics simulation, which allow us to directly train the models on 2/3D geometry designs that are represented by an unstructured mesh or point cloud, without the need for any explicit or hand-crafted parameterization. We utilize an encoder-processor-decoder-type architecture which can flexibly make prediction at both node level and graph level. The performance of our proposed GNN-based surrogate model is demonstrated on 2 example applications: feature designs in the domain of additive engineering and airfoil design in the domain of aerodynamics. The models show good accuracy in their predictions on a separate set of test geometries after training, with almost instant prediction speeds, as compared to O(hour) for the high-fidelity simulations required otherwise.

Manna Dai, Yang Jiang, Feng Yang et al.

Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventional numerical methods for free-form metasurfaces are time-consuming and demand specialized expertise. Alternatively, recent studies demonstrate that deep learning has great potential to accelerate and refine metasurface designs. Here, we present XGAN, an extended generative adversarial network (GAN) with a surrogate for high-quality free-form metasurface designs. The proposed surrogate provides a physical constraint to XGAN so that XGAN can accurately generate metasurfaces monolithically from input spectral responses. In comparative experiments involving 20000 free-form metasurface designs, XGAN achieves 0.9734 average accuracy and is 500 times faster than the conventional methodology. This method facilitates the metasurface library building for specific spectral responses and can be extended to various inverse design problems, including optical metamaterials, nanophotonic devices, and drug discovery.

Joyjit Chattoraj, Brahim Hamadicharef, Teo Shi Chang et al.

While the Water-Gas Shift (WGS) reaction plays a crucial role in hydrogen production for fuel cells, finding suitable catalysts to achieve high yields for low-temperature WGS reactions remains a persistent challenge. Artificial Intelligence (AI) has shown promise in accelerating catalyst design by exploring vast candidate spaces, however, two key gaps limit its effectiveness. First, AI models primarily train on numerical data, which fail to capture essential text-based information, such as catalyst synthesis methods. Second, the cross-disciplinary nature of catalyst design requires seamless collaboration between AI, theory, experiments, and numerical simulations, often leading to communication barriers. To address these gaps, we present AceWGS, a Large Language Models (LLMs)-aided framework to streamline WGS catalyst design. AceWGS interacts with researchers through natural language, answering queries based on four features: (i) answering general queries, (ii) extracting information about the database comprising WGS-related journal articles, (iii) comprehending the context described in these articles, and (iv) identifying catalyst candidates using our proposed AI inverse model. We presented a practical case study demonstrating how AceWGS can accelerate the catalyst design process. AceWGS, built with open-source tools, offers an adjustable framework that researchers can readily adapt for a range of AI-accelerated catalyst design applications, supporting seamless integration across cross-disciplinary studies.

Joyjit Chattoraj, Jian Cheng Wong, Zhang Zexuan et al.

In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed generative AI technique, has proven instrumental in synthesizing airfoil designs. However, a common limitation of GAN is the inherent lack of smoothness in the generated airfoil surfaces. To address this issue, we present a GAN model featuring a customized loss function built to produce seamlessly contoured airfoil designs. Additionally, our model demonstrates a substantial increase in design diversity compared to a conventional GAN augmented with a post-processing smoothing filter.