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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.

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.

Updated: Nov 9, 2020

Abstract: Inspired by the hierarchical structure of nacre and the robust adhesive ability of mussel threads, graphene oxide–polydopamine (GO–PDA) nanocomposites are designed and synthesized to achieve enhanced mechanical properties and to provide additional functionalities. Here we report a joint experimental/computational investigation of GO–PDA nanocomposites, proposing a probable chemical reduction mechanism of PDA to convert GO to reduced GO (rGO), which helps increase the electrical conductivity. The most stable chemical connection between PDA and GO is also proposed. Our artificial nacre-like GO–PDA nanocomposites are shown to have higher tensile strength and toughness compared to natural nacre. The pulling tests conducted by molecular dynamics simulations, which are supported by our experiments, reveal that the enhanced mechanical strength of GO–PDA nanocomposites mainly originates from the additional non-covalent interactions provided by PDA. The humidity-driven shrinking mechanism of GO–PDA nanocomposites due to non-uniform stresses on the GO–PDA sheets is also discovered in our simulations and supported by our experiments. The findings in this work can help improve and tune the properties of GO–PDA nanocomposites and might also apply to other 2D materials.

Full paper: C-T Chen, FJ Martin-Martinez, Shengjie Ling, Zhao Qin, MJ Buehler, Nacre-inspired design of graphene oxide–polydopamine nanocomposites for enhanced mechanical properties and multi-functionalities, Nano Futures, 2017​, DOI: 10.1088/2399-1984/aa6aed

Simulation movie:

Movie: GO–PDA model with 15 wt% water content during pulling test

Updated: Nov 9, 2020

Abstract: A set of computational methods that contains a brute-force algorithmic generation of chemical isomers, molecular dynamics (MD) simulations, and density functional theory (DFT) calculations is reported and applied to investigate nearly 3000 probable molecular structures of polydopamine (PDA) and eumelanin. All probable early-polymerized 5,6-dihydroxyindole (DHI) oligomers, ranging from dimers to tetramers, have been systematically analyzed to find the most stable geometry connections as well as to propose a set of molecular models that represents the chemically diverse nature of PDA and eumelanin. Our results indicate that more planar oligomers have a tendency to be more stable. This finding is in good agreement with recent experimental observations, which suggested that PDA and eumelanin are composed of nearly planar oligomers that appear to be stacked together via π–π interactions to form graphite-like layered aggregates. We also show that there is a group of tetramers notably more stable than the others, implying that even though there is an inherent chemical diversity in PDA and eumelanin, the molecular structures of the majority of the species are quite repetitive. Our results also suggest that larger oligomers are less likely to form. This observation is also consistent with experimental measurements, supporting the existence of small oligomers instead of large polymers as main components of PDA and eumelanin. In summary, this work brings an insight into the controversial structure of PDA and eumelanin, explaining some of the most important structural features, and providing a set of molecular models for more accurate modeling of eumelanin-like materials.

Full paper: C-T Chen, FJ Martin-Martinez, GS Jung, MJ Buehler, Polydopamine and eumelanin molecular structures investigated with ab initio calculations, Chemical Science, 2016, DOI: 10.1039/C6SC04692D

Simulation movies:

Movie: Brute-force algorithmic generator creating checkerboard representations of 216 DHI trimers

Movie: Energy minimization to generate an initial molecular structure

Movie: MD simulation to find the most stable geometry

Updated: Nov 9, 2020

Abstract: Eumelanin is a ubiquitous biological pigment, and the origin of its broadband absorption spectrum has long been a topic of scientific debate. Here, we report a first-principles computational investigation to explain its broadband absorption feature. These computations are complemented by experimental results showing a broadening of the absorption spectra of dopamine solutions upon their oxidation. We consider a variety of eumelanin molecular structures supported by experiments or theoretical studies, and calculate the absorption spectra with proper account of the excitonic couplings based on the Frenkel exciton model. The interplay of geometric order and disorder of eumelanin aggregate structures broadens the absorption spectrum and gives rise to a relative enhancement of absorption intensity at the higher-energy end, proportional to the cube of absorption energy. These findings show that the geometric disorder model is as able as the chemical disorder model, and complements this model, to describe the optical properties of eumelanin.

Full paper: C-T Chen, C Chuang, J Cao, V Ball, D Ruch, MJ Buehler, Excitonic effects from geometric order and disorder explain broadband optical absorption in eumelanin, Nature Communications, 2014, DOI:10.1038/ncomms4859

Simulation movie:

Movie: Self-assembly of eumelanin protomolecules in MD simulation

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