The main goal of our research is to establish an integrated, intelligent and insightful approach for accelerating new materials design and optimization, by advancing in the areas of multi-scale modeling, multi-physics modeling and machine learning.
Our research involves a very wide range of materials modeling and simulation methods, from atomistic to continuum scale, including density functional theory, molecular dynamics, coarse-grained molecular dynamics, phase field model, finite element method and etc. The cooperation of computational methods at different scales enables accurate and systematic predictions on a variety of materials properties (such as electronic, mechanical, transport, interfacial and structural properties). In addition, this allows investigation of material synthesizability and stability, to promote the realization of promising new materials.
We list several representative material systems we have studied or we are currently working on, which can generally be categorized into the classes of low-dimensional materials, energy materials and carbonaceous materials.
Here we would like to briefly show a few examples from our recent work, and many of them were done in close collaboration with other research groups. Certainly they did not cover the entire spectrum of our research. If you would like to know more, please check our latest publication list or simply send us an email.
Polymer electrolyte design
Here we aimed to accelerate the design of highly conductive polymer electrolyte materials for all-solid-state battery applications. Specifically, we first converted the conventional chemical species space to a continuous design space via the coarse-graining approach. Then we adopted the Bayesian optimization method for an efficient screening, to identify the collective effects of molecular level material properties on the ionic conductivity, based on which we would be able to achieve the global optimization of the system conductivity.
Y. Wang#,*, T. Xie#, A. France-Lanord, A. Berkley, J. A. Johnson, Y. Shao-Horn and J. C. Grossman*, Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics, Chem. Mater., 32(10), 4144-4151 (2020).
Nanowire structural change
Here we developed a 3D multi-phase field model, which is capable of capturing the dynamical process of nanowire vapor-liquid-solid growth as well as reproducing the experimental wire geometries. With this model, we clarified the formation mechanisms of several abnormal semiconductor nanowire structures, and predicted the conditions at the onset of these structural changes.
Y. Wang, Tomas Sikola and Miroslav Kolibal*, Collector Droplet Behavior during Formation of Nanowire Junctions, J. Phys. Chem. Lett., 11, 6498-6504 (2020).
Y. Wang*, P. C. McIntyre and W. Cai*, Phase Field Model for Morphological Transition in Nanowire Vapor-Liquid-Solid Growth, Cryst. Growth Des., 17(4), 2211-2217 (2017).
Y. Li#, Y. Wang#,*, S. Ryu, A. F. Marshall, W. Cai and P. C. McIntyre, Spontaneous, Defect-Free Kinking via Capillary Instability during Vapor-Liquid-Solid Nanowire Growth, Nano Lett., 16(3), 1713-1718 (2016).
Y. Wang, S. Ryu, P. C. McIntyre and W. Cai*, A Three-Dimensional Phase Field Model for Nanowire Growth by the Vapor-Liquid-Solid Mechanism, Modelling Simul. Mater. Sci. Eng., 22, 055005 (2014).
Here we demonstrated our ability of developing an empirical interatomic potential well fitted to the binary phase diagram. With an accurate potential, we can perform molecular dynamics simulations to reveal the atomistic mechanisms of many interesting phenomena. For example, from the simulation trajectories, we can identify the interface morphology of gold-catalyzed silicon epitaxial growth; or we can capture the grain coarsening process inside a polycrystalline nanoparticle.
B. Jin, Y. Wang, Z. Liu, A. France-Lanord, J. C. Grossman, C. Jin* and R. Tang*, Revealing the Cluster-Cloud and Its Role in Nanocrystallization, Adv. Mater., 30, 1808225 (2019).
Y. Wang, A. Santana* and W. Cai, Atomistic Mechanisms of Orientation and Temperature Dependence in Gold-Catalyzed Silicon Growth, J. Appl. Phys., 122(8), 085106 (2017).
Y. Wang, A. Santana* and W. Cai, Au-Ge MEAM Potential Fitted to the Binary Phase Diagram, Modelling Simul. Mater. Sci. Eng., 25, 025004 (2016).