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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: Biomimicry, adapting and implementing nature's designs provides an adequate first-order solution to achieving superior mechanical properties. However, the design space is too vast even using biomimetic designs as prototypes for optimization. Here, we propose a new approach to design hierarchical materials using machine learning, trained with a database of hundreds of thousands of structures from finite element analysis, together with a self-learning algorithm for discovering high-performing materials where inferior designs are phased out for superior candidates. Results show that our approach can create microstructural patterns that lead to tougher and stronger materials, which are validated through additive manufacturing and testing. We further show that machine learning can be used as an alternative method of coarse-graining – analyzing and designing materials without the use of full microstructural data. This novel paradigm of smart additive manufacturing can aid in the discovery and fabrication of new material designs boasting orders of magnitude increase in computational efficacy over conventional methods.

Full paper: GX Gu, C-T Chen, DJ Richmond, MJ Buehler, Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment, Materials Horizons, 2018, DOI: 10.1039/C8MH00653A

Updated: Nov 9, 2020

Abstract: Composites are widely used to create tunable materials to achieve superior mechanical properties. Brittle materials fail catastrophically in the presence of cracks. Incorporating softer constituents into brittle materials can alleviate stress concentration, leading to tougher and stronger composites. However, searching for the optimal designs of composites is extremely challenging due to the astronomical number of possible material and geometry combinations. Here, we apply machine learning to a composite system and demonstrate its capacity to accurately and efficiently predict mechanical properties including toughness and strength. The method we used incorporates machine learning techniques to generate optimal designs with orders of magnitude better than the mean properties of the input training data, and at a much lower computational cost compared to exhaustive methods. Additionally, the method can further rebuild the detailed performances of the designs, even when this information is lost in the training process. The results demonstrate the ability of machine learning to search for optimal designs with very limited training data. To demonstrate the application of machine learning to composite design, we optimize a large-scale system not tractable by an exhaustive brute force approach and show that it is a promising tool towards composite design. This work offers a new perspective in the exploration of design spaces and accelerating the discovery of new functional, customizable composites.

Full paper: GX Gu, C-T Chen, MJ Buehler, De novo composite design based on machine learning algorithm, Extreme Mechanics Letters, 2018, DOI: 10.1016/j.eml.2017.10.001

Updated: Nov 9, 2020

Abstract: Graphene and other two-dimensional materials have unique physical and chemical properties of broad relevance. It has been suggested that the transformation of these atomically planar materials to three-dimensional (3D) geometries by bending, wrinkling, or folding could significantly alter their properties and lead to novel structures and devices with compact form factors, but strategies to enable this shape change remain limited. We report a benign thermally responsive method to fold and unfold monolayer graphene into predesigned, ordered 3D structures. The methodology involves the surface functionalization of monolayer graphene using ultrathin noncovalently bonded mussel-inspired polydopamine and thermoresponsive poly(N-isopropylacrylamide) brushes. The functionalized graphene is micropatterned and self-folds into ordered 3D structures with reversible deformation under a full control by temperature. The structures are characterized using spectroscopy and microscopy, and self-folding is rationalized using a multiscale molecular dynamics model. Our work demonstrates the potential to design and fabricate ordered 3D graphene structures with predictable shape and dynamics. We highlight applicability by encapsulating live cells and creating nonlinear resistor and creased transistor devices.

Full paper: W Xu, Z Qin, C-T Chen, HR Kwag, Q Ma, A Sarkar, MJ Buehler, DH Gracias, Ultrathin thermoresponsive self-folding 3D graphene, Science Advances, 2017, DOI: 10.1126/sciadv.1701084

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