Increasing energy demand along with an increased risk of global climate change has led to an enhanced
significance of materials science research. In recent decades, a multitude of novel low dimensional materials,
such as graphene, graphene oxide, and their composites, have been synthesized to overcome this challenge.
Although still in infancy, these nanomaterials, have shown great promise for the development of new
technologies that will help meet our future sustainable energy and transport. Nevertheless, developing and
implementing new materials technologies takes many years. Data sciences, especially machine learning
approaches, can reduce this materials discovery-deployment cycle by finding hidden patterns in material
behavior and allow quick computational screening of promising material designs to guide experimentation.
In this talk, we would describe the role of computational materials science, particularly data science,
machine learning, and atomistic simulations, to design and explore novel materials for sustainable energy
and lightweight transportation applications. The following representative examples will be discussed: (a)
design of champion materials for solar-driven CO2 reduction, thereby mimicking photosynthesis; (b) role of
2D materials in improving capacity and performance of lithium-ion and lithium-sulfur batteries; (c) strong
size dependent mechanical behavior of graphene oxide monolayer and nanosheets; and (d) defectengineering of graphene and other low-dimensional materials for photocatalytic watersplitting and hydrogen
storage. These works will also serve as an example of how we can combine data science with atomistic
modeling and experimental testing to provide a more coherent and in-depth understanding.
Our Key Relevant Publications:
[1] Han, G.F. et al. (2021), “Mechanochemistry for ammonia synthesis under mild conditions”, Nature Nanotechnology
16 (3), 325-330.
[2] Z Lu, ZW Chen, CV Singh (2021), “Neural network-assisted development of high-entropy alloy catalysts:
Decoupling ligand and coordination effects”, Matter 3 (4), 1318-1333.
[3] T Cui, S Mukherjee, 6 others, CV Singh, T Filleter (2020), "Fatigue of graphene", Nature Materials.
[4] Qian, C. et al. (2019),"Catalytic CO2 reduction by palladium-decorated silicon–hydride nanosheets." Nature
Catalysis 2.1: 46.
[5] Ghuman, K. K. et al. (2016), "Photoexcited surface frustrated Lewis pairs for heterogeneous photocatalytic CO2
reduction." Journal of the American Chemical Society 138.4: 1206-1214.
[6] Li, Lu, et al. (2017), "Phosphorene as a Polysulfide Immobilizer and Catalyst in High‐Performance Lithium–Sulfur
Batteries." Advanced materials 29.2: 1602734.
[7] Grixti, S., Mukherjee, S., & Singh, C. V. (2018), “Two‐dimensional boron as an impressive lithium‐sulphur battery
cathode material”, Energy Storage Materials, 13, 80-87.
[8] Li, Lu, et al. (2015), "A foldable lithium–sulfur battery." ACS nano 9.11: 11342-11350.
[9] Cao, C et al. (2018), "Nonlinear fracture toughness measurement and crack propagation resistance of functionalized
graphene multilayers." Science advances 4.4: eaao7202.
[10] Cao, C., Daly, M., Chen, B., Howe, J. Y., Singh, C. V., Filleter, T., & Sun, Y. (2015). Strengthening in graph