175 citations · 1.1k across the 47 of their papers we have counts for
49 papers
Benchmark control problems in nonequilibrium statistical mechanics
Stephen Whitelam, Corneel Casert, Megan Engel +1
We present a set of computer codes designed to test methods for optimizing time-dependent control protocols in fluctuating nonequilibrium systems. Each problem consists of a stocha…
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning
Colin Bellinger, Mark Crowley, Isaac Tamblyn
Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The…
Automatic graph representation algorithm for heterogeneous catalysis
Zachary Gariepy, ZhiWen Chen, Isaac Tamblyn +2
One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of ML-aided exploration, m…
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague +10
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by…
fintech-kMC: Agent based simulations of financial platforms for design and testing of machine learning systems
Isaac Tamblyn, Tengkai Yu, Ian Benlolo
We discuss our simulation tool, fintech-kMC, which is designed to generate synthetic data for machine learning model development and testing. fintech-kMC is an agent-based model dr…
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…
Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment
Chris Beeler, Xinkai Li, Colin Bellinger +3
Using a novel toy nautical navigation environment, we show that dynamic programming can be used when only incomplete information about a partially observed Markov decision process…
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning
Colin Bellinger, Andriy Drozdyuk, Mark Crowley +1
The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing. A major challenge, however, lies in fact that me…
Electronic Response Quantities of Solids and Deep Learning
Kevin Ryczko, Olivier Malenfant-Thuot, Michel Côté +1
We introduce a deep neural network (DNN) framework called the \textbf{r}eal-space \textbf{a}tomic \textbf{d}ecomposition \textbf{net}work (\textsc{radnet}), which is capable of mak…
Deep Learning and Crystal Plasticity: A Preconditioning Approach for Accurate Orientation Evolution Prediction
Peyman Saidi, Hadi Pirgazi, Mehdi Sanjari +5
Efficient and precise prediction of plasticity by data-driven models relies on appropriate data preparation and a well-designed model. Here we introduce an unsupervised machine lea…
Twin Neural Network Regression is a Semi-Supervised Regression Algorithm
Sebastian J. Wetzel, Roger G. Melko, Isaac Tamblyn
Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are pres…
Unsupervised Hyperspectral Stimulated Raman Microscopy Image Enhancement: Denoising and Segmentation via One-Shot Deep Learning
Pedram Abdolghader, Andrew Ridsdale, Tassos Grammatikopoulos +5
Hyperspectral stimulated Raman scattering (SRS) microscopy is a label-free technique for biomedical and mineralogical imaging which can suffer from low signal to noise ratios. Here…
Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning
Kevin Ryczko, Sebastian J. Wetzel, Roger G. Melko +1
We use voxel deep neural networks to predict energy densities and functional derivatives of electron kinetic energies for the Thomas-Fermi model and Kohn-Sham density functional th…
Neural evolution structure generation: High Entropy Alloys
Conrard Giresse Tetsassi Feugmo, Kevin Ryczko, Abu Anand +2
We propose a method of neural evolution structures (NESs) combining artificial neural networks (ANNs) and evolutionary algorithms (EAs) to generate High Entropy Alloys (HEAs) struc…
Golem: An algorithm for robust experiment and process optimization
Matteo Aldeghi, Florian Häse, Riley J. Hickman +2
Numerous challenges in science and engineering can be framed as optimization tasks, including the maximization of reaction yields, the optimization of molecular and materials prope…
Weakly-supervised multi-class object localization using only object counts as labels
Kyle Mills, Isaac Tamblyn
We demonstrate the use of an extensive deep neural network to localize instances of objects in images. The EDNN is naturally able to accurately perform multi-class counting using o…
Interpretable discovery of new semiconductors with machine learning
Hitarth Choubisa, Petar Todorović, Joao M. Pina +5
Machine learning models of materials accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated resul…
Deep learning and high harmonic generation
M. Lytova, M. Spanner, I. Tamblyn
Using machine learning, we explore the utility of various deep neural networks (NN) when applied to high harmonic generation (HHG) scenarios. First, we train the NNs to predict the…
Twin Neural Network Regression
Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko +1
We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The s…
Neuroevolutionary learning of particles and protocols for self-assembly
Stephen Whitelam, Isaac Tamblyn
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without i…