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RESEARCH INTERESTS

Research background: My research journey started at National Taiwan University (NTU) when I was a master’s student working with Prof. Yeong-Bin Yang on vibration analysis of a symmetric two-member truss. This truss system can be described by a simple governing equation, but it can display chaos phenomena when geometric nonlinearity is considered. I was fascinated by how a deterministic system can generate complexity from simplicity, known as the butterfly effect. This early research experience motivated me to pursue a Ph.D. degree in Structures and Materials at MIT. While I studied the finite element method (FEM) and structural dynamics at NTU, my research at MIT focused on atomistic modeling and quantum chemical calculations. I worked with Prof. Markus Buehler to investigate the structural, mechanical, and optical properties of eumelanin (natural pigment found in most organisms) and polydopamine (mussel-inspired material for multifunctional coatings) using multiscale modeling. Multiscale modeling is a technique that combines various simulation methods to capture the behavior of materials at different scales (both in length and time), providing links connecting the atomic-scale and macro-scale phenomena. By analyzing the collective behavior of the atoms in a material system, its structure-property relationship could be revealed. At MIT, I developed research expertise in atomistic modeling, including molecular dynamics (MD) simulations and density functional theory (DFT). The research findings in my thesis were published in high-impact journals, including ACS Nano, Nature Communications, and Chemical Science, and highlighted on MIT News.

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Passion for artificial intelligence: Inspired by the success of Google DeepMind’s AlphaGo program in 2016, I saw the potential of AI and ML to advance scientific computing. During my postdoctoral training at MIT, I published two papers on data-driven composites design under the guidance of Prof. Markus Buehler. The first one was in Extreme Mechanics Letters and the second one was in Materials Horizons and received the Materials Horizons Outstanding Paper Award. Both papers were published in 2018 and have received more than 250 citations as of today (Dec 2022). During my postdoctoral training at UC Berkeley, I published four ML-related papers. In 2019, we showed that high-performing designs of graphene nanocomposites can be generated using a data-driven approach (published in Advanced Theory and Simulations as the cover) and wrote a prospective paper on ML for composite materials (published in MRS Communications) and received the MRS Communications Lecture Award. In 2020, we developed a general-purpose inverse design approach using generative inverse design networks (GIDNs). This work (published in Advanced Science) has inspired many follow-up studies around the world in different fields and received more than 120 citations as of today. In 2021, we developed ElastNet, a de novo elastography method combining the theory of elasticity with a deep-learning approach. This work (published in PNAS) shows that representing a target physical quantity field as a continuum and differentiable function by a neural network offers numerous benefits over conventional discrete grid-based representations.

 

I deeply appreciate the guidance from my advisors and mentors at NTU (Prof. Yeong-Bin Yang), MIT (Prof. Markus Buehler), and UC Berkeley (Prof. Daryl Chrzan and Prof. Mark Asta). I also appreciate the financial support that I have received for my study and research. During my Ph.D. study at MIT, I received support via a fellowship from the Taiwanese Government and funding from CRP Henri Tudor in the framework of the BioNanotechnology project. During my postdoctoral training at MIT, I received support from the Office of Naval Research (ONR) and the Multidisciplinary University Research Initiative (MURI). During my postdoctoral training at UC Berkeley, I was fully funded by the Department of Mechanical Engineering.

  • Chun-Teh
    • Jan 22, 2020

Machine learning for composite materials

Updated: Nov 9, 2020


Abstract: Machine learning (ML) has been perceived as a promising tool for the design and discovery of novel materials for a broad range of applications. In this prospective paper, we summarize recent progress in the applications of ML to composite materials modeling and design. An overview of how different types of ML algorithms can be applied to accelerate composite research is presented. This framework is envisioned to revolutionize approaches to design and optimize composites for the next generation of materials with unprecedented properties.

Full paper: CT Chen and GX Gu, Machine learning for composite materials, MRS Communications, 2019, DOI: 10.1557/mrc.2019.32

  • Chun-Teh
    • Jan 22, 2020

Effect of constituent materials on composite performance: exploring design strategies via ML

Updated: Nov 9, 2020


Abstract: Nature assembles a range of biological composites with remarkable mechanical properties despite being composed of relatively weak polymeric and ceramic components. However, the architectures of biomaterials cannot be considered as optimal designs for engineering applications since biomaterials are constantly evolving for multiple functions beyond carrying external loading. Here, it is aimed to develop an intelligent approach to design superior composites from scratch—starting from constituent materials. A systematic computational investigation of the effect of constituent materials (assumed to be perfectly brittle) on the behavior of composites using an integrated approach combining finite element method, molecular dynamics, and machine learning (ML) is reported. It is demonstrated that instead of using brute‐force methods, machine learning is a much more efficient approach and can generate optimal designs with similar performance to those obtained from an exhaustive search. Furthermore, it is shown that the toughening and strengthening mechanism observed in composites at the continuum‐scale by combining stiff and soft constituents is valid for nanomaterials as well. Results show that high‐performing designs of graphene nanocomposites can be generated using our ML approach. This novel ML‐based design framework can be applied to other material systems to study a variety of structure–property relationships over several length‐scales.

Full paper: CT Chen and GX Gu, Effect of constituent materials on composite performance: exploring design strategies via machine learning, Advanced Theory and Simulations, 2019, DOI: 10.1002/adts.201900056

  • Chun-Teh
    • May 2, 2019

Polydopamine and eumelanin models in various oxidation states

Updated: Nov 9, 2020


Abstract: We report a comprehensive ab initio structural investigation of more than 43000 probable molecular structures of polydopamine (PDA) and eumelanin in various oxidation states. With the aid of a computational approach including a brute-force algorithmic generation of chemical isomers and density functional theory, all probable oxidized 5,6-dihydroxyindole (DHI) oligomers, ranging from dimers to tetramers, have been systematically generated and evaluated. We identify a set of the most stable molecular structures of PDA and eumelanin which represent the chemically diverse nature of these materials. Results show that more planar molecular structures have a tendency to be more stable. We also observe that, in some cases, forming cyclic molecular structures could reduce the energy of a DHI tetramer and make it more stable. This finding supports the hypothesis that cyclic molecules could exist in eumelanin-like materials. Additionally, the cyclic molecular models proposed in this work are energetically more favorable than the popular porphyrin-like models in this field.

Full paper: CT Chen and MJ Buehler, Polydopamine and eumelanin models in various oxidation states, Physical Chemistry Chemical Physics, 2018, DOI: 10.1039/C8CP05037F

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