548 citations · 1.2k across the 8 of their papers we have counts for
8 papers
Evolving symbolic density functionals
He Ma, Arunachalam Narayanaswamy, Patrick Riley +1
Systematic development of accurate density functionals has been a decades-long challenge for scientists. Despite the emerging application of machine learning (ML) in approximating…
Scalable variational Monte Carlo with graph neural ansatz
Li Yang, Wenjun Hu, Li Li
Deep neural networks have been shown as a potentially powerful ansatz in variational Monte Carlo for solving quantum many-body problems. We propose two improvements in this directi…
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li +2
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly li…
Quantum Optimization with a Novel Gibbs Objective Function and Ansatz Architecture Search
Li Li, Minjie Fan, Marc Coram +2
The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansa…
Decoding Molecular Graph Embeddings with Reinforcement Learning
Steven Kearnes, Li Li, Patrick Riley
We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previous…
Neural-Guided Symbolic Regression with Asymptotic Constraints
Li Li, Minjie Fan, Rishabh Singh +1
Symbolic regression is a type of discrete optimization problem that involves searching expressions that fit given data points. In many cases, other mathematical constraints about t…