Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang, Andrea Cepellotti, Jinxuan Ding, Kyle Bystrom, Blake R. Duschatko, Anders Johansson, Lixin Sun, Simon Batzner, Albert Musaelian, Jarad A. Mason, Boris Kozinsky, and Nicola Molinari. “Transferability and accuracy of ionic liquid simulations with equivariant machine learning interatomic potentials,” J. Phys. Chem. Lett. 15 (30), 7539-7547 (2024).

Blake R. Duschatko, Jonathan Vandermause, Nicola Molinari, and Boris Kozinsky. “Uncertainty driven active learning of coarse grained free energy models,” npj Computational Materials 10 (1), 9 (2024).

Xuezeng Tian, Dennis S. Kim, Shize Yang, Christopher J. Ciccarino, Yongji Gong, Yongsoo Yang, Yao Yang, Blake R. Duschatko, Pulickel M. Ajayan, Juan-Carlos Idrobo, Prineha Narang, and Jianwei Miao. “Correlating 3D crystal defects and electronic properties of 2D materials at the single-atom level,” Nature Materials 19 (8), 867-873 (2020)

Blake R. Duschatko, Philipp T. Dumitrescu, and Andrew C. Potter. “Tracking the quantized information transfer at the edge of a chiral Floquet phase,” Physical Review B 98, 054309 (2018).

Blake R. Duschatko, Xiang Fu, Cameron Owen, Yu Xie, Albert Musaelian, Tommi Jaakkola, and Boris Kozinsky. “Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining,” arXiv preprint arXiv:2405.19386

“Machine Learned Coarse Grained Force Fields for Dimensionality Reduction in Computational Materials Design.” A dissertation presented by Blake R. Duschatko to The Department of John A. Paulson School of Engineering and Applied Sciences at Harvard University.

“Quantifying Information Transfer in Chiral Floquet Phases.” An honors thesis presented by Blake R. Duschatko to the Physics Department at The University of Texas at Austin.