Here are some of our current projects


Concrete, simply due to the sheer scale of production, is responsible for a staggering 8% of total anthropogenic carbon emissions. Its environmental impact mainly strives from the manufacture of its binder, ordinary Portland cement (OPC), and, being a one-use material, from the accumulation of demolition waste in landfills. Accordingly, current strategies to mitigate greenhouse gas emissions from the concrete industry focus on replacing OPC for alternative low-carbon materials with cementitious value and on increasing the durability of cement to increase the lifetime of infrastructure. Attaining  these goals, however, remains challenging due in large to our incomplete understanding of how fundamental phenomena such as aqueous reactivity of glasses, expansive physico-chemical processes in nanoporous networks, or the long-term progression of inelastic deformations (i.e., creep), depend on the complex disordered, heterogeneous, and porous structure of cementitious systems.

Our aim in this project is to formulate physics-based design guidelines rooted on a fundamental understanding of the composition-structure-property relations in cementitious materials. To this end, we use simulation techniques that cover a broad range of time and length scales including density functional theory (DFT), molecular dynamics (MD) simulations, and kinetic Monte Carlo tools, complemented by data-driven machine learning (ML) methods.


In this project we collaborate with our brilliant colleague Prannoy Suraneni, an experimental expert on cementitious materials.


High-entropy alloys are a relatively new class of materials with remarkable mechanical and chemical properties that make them ideal candidates as designer catalysts or as refractory alloys. Being made of a disordered mixture of four or more metals in similar proportions, HEAs display a huge variety of distinct local environments. For catalysis applications, for example, these environments constitute a huge variety of potential active sites at the surface, thus opening the door to the tantalizing possibility of accelerating multiple reactions simultaneously using the same catalyst material. The astronomical size of the design space, however, renders conventional approaches to materials selection, optimization, and design grossly inadequate when applied to HEAs.

Our aim in this project is to combine atomistic simulations of simple metrics calculated for large representative samples of HEA configurations and use the generated data to train ML models with the ability to find the underlying structure of the data, which embodies the structure-property relation. The ultimate goals is to develop an efficient and transferable computational framework that could advance the rational selection of optimal HEAs for a variety of applications.

In this project we collaborate with two of our colleagues doing experiments. James Englehardt, an environmental engineer passionate about net-zero water treatment, and James Coakley, an expert metallurgist fascinated by microstructure evolution during alloy processing.