Dr. Lindsey develops new artificial intelligence-driven simulation methods to accelerate the design, discovery, and synthesis of next-generation materials. She leads the development of ChIMES, a physics-informed machine-learned interatomic model and generation framework enabling quantum-accurate simulation on large, experimentally relevant spatiotemporal scales. This framework includes the Active Learning Driver, an advanced autonomous model generation framework that orchestrates complex fitting problems on high performance computing platforms. She is particularly interested in further developing these tools to enable more efficient, scalable, and trustworthy predictions in materials-related research.
Atomic resolution simulations of bulk and nanocarbon material synthesis enabled by ChIMES. These simulations were critical in developing a new, ultrafast nanocarbon synthesis technique by providing critical missing insights on the underlying mechanism (Armstrong, Lindsey, et al., Nat. Commun. 2020; Lindsey et al, Nat. Commun. 2022). We are using this simulation capability to discover next-generation nanocarbon materials and provide experimentally realizable synthetic routes to them.