175 citations · 1.1k across the 47 of their papers we have counts for
5 papers · 1 filter
Training neural networks using Metropolis Monte Carlo and an adaptive variant
Stephen Whitelam, Viktor Selin, Ian Benlolo +2
We examine the zero-temperature Metropolis Monte Carlo algorithm as a tool for training a neural network by minimizing a loss function. We find that, as expected on theoretical gro…
Machine Learning Diffusion Monte Carlo Energies
Kevin Ryczko, Jaron T. Krogel, Isaac Tamblyn
We present two machine learning methodologies that are capable of predicting diffusion Monte Carlo (DMC) energies with small datasets (~60 DMC calculations in total). The first use…
Generative Enriched Sequential Learning (ESL) Approach for Molecular Design via Augmented Domain Knowledge
Mohammad Sajjad Ghaemi, Karl Grantham, Isaac Tamblyn +2
Deploying generative machine learning techniques to generate novel chemical structures based on molecular fingerprint representation has been well established in molecular design.…
Cellular automata can classify data by inducing trajectory phase coexistence
Stephen Whitelam, Isaac Tamblyn
We show that cellular automata can classify data by inducing a form of dynamical phase coexistence. We use Monte Carlo methods to search for general two-dimensional deterministic a…
Learning stochastic dynamics and predicting emergent behavior using transformers
Corneel Casert, Isaac Tamblyn, Stephen Whitelam
We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of th…