Understanding Machine-learned Density Functionals. - Li Li 060; John C. Snyder; Isabelle M. Pelaschier; Jessica Huang; Uma-Naresh Niranjan; Paul Duncan; Matthias Rupp; Klaus-Robert Müller; Kieron Burke (CoRR, 2014)

Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals. - Kevin Vu; John C. Snyder; Li Li 060; Matthias Rupp; Brandon F. Chen; Tarek Khelif; Klaus-Robert Müller; Kieron Burke (CoRR, 2015)

By-passing the Kohn-Sham equations with machine learning. - Felix Brockherde; Li Li 060; Kieron Burke; Klaus-Robert Müller (CoRR, 2016)

Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds. - Nathaniel Thomas; Tess E. Smidt; Steven Kearnes; Lusann Yang; Li Li 060; Kai Kohlhoff; Patrick Riley 001 (CoRR, 2018)

Optimization of Molecules via Deep Reinforcement Learning. - Zhenpeng Zhou; Steven Kearnes; Li Li 060; Richard N. Zare; Patrick Riley 001 (CoRR, 2018)

Neural-Guided Symbolic Regression with Semantic Prior. - Li Li 060; Minjie Fan; Rishabh Singh; Patrick Riley 001 (CoRR, 2019)

Decoding Molecular Graph Embeddings with Reinforcement Learning. - Steven Kearnes; Li Li 060; Patrick Riley 001 (CoRR, 2019)

Energy-based View of Retrosynthesis. - Ruoxi Sun 002; Hanjun Dai; Li Li 060; Steven Kearnes; Bo Dai 001 (CoRR, 2020)

Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics. - Li Li 060; Stephan Hoyer; Ryan Pederson; Ruoxi Sun 002; Ekin D. Cubuk; Patrick Riley 001; Kieron Burke (CoRR, 2020)

Scaling Symbolic Methods using Gradients for Neural Model Explanation. - Subham Sekhar Sahoo; Subhashini Venugopalan; Li Li 060; Rishabh Singh; Patrick Riley 001 (ICLR, 2021)

Towards understanding retrosynthesis by energy-based models. - Ruoxi Sun 002; Hanjun Dai; Li Li 060; Steven Kearnes; Bo Dai 001 (NeurIPS, 2021)

Generalizability of density functionals learned from differentiable programming on weakly correlated spin-polarized systems. - Bhupalee Kalita; Ryan Pederson; Li Li 060; Kieron Burke (CoRR, 2021)