17 citations · 24 across the 5 of their papers we have counts for
5 papers
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
Blake R. Duschatko, Xiang Fu, Cameron Owen +4
We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temper…
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang +11
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' a…
Uncertainty Driven Active Learning of Coarse Grained Free Energy Models
Blake R. Duschatko, Jonathan Vandermause, Nicola Molinari +1
Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are ap…
Correlating 3D atomic defects and electronic properties of 2D materials with picometer precision
Xuezeng Tian, Dennis S. Kim, Shize Yang +10
The exceptional electronic, optical and chemical properties of two-dimensional materials strongly depend on the 3D atomic structure and crystal defects. Using Re-doped MoS2 as a mo…
Tracking the quantized information transfer at the edge of a chiral Floquet phase
Blake R. Duschatko, Philipp T. Dumitrescu, Andrew C. Potter
Two-dimensional arrays of periodically driven qubits can host inherently dynamical topological phases with anomalous chiral edge dynamics. These chiral Floquet phases are formally…