429 citations · 478 across the 11 of their papers we have counts for
11 papers
Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs
Jakob Hartmann, James Harvey, Jhonathan Navott +5
Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions requ…
Measuring Progress in Reasoning Toward Mathematical Discovery with Automatic Verification
Erik Y. Wang, Sumeet R. Motwani, James V. Roggeveen +9
Can AI make progress on important, unsolved mathematical problems? Large language models are now capable of sophisticated mathematical and scientific reasoning, but whether they ca…
Equivariant Interatomic Potentials without Tensor Products
Thiago Reschützegger, Sarp Aykent, Gabriel Jacob Perin +5
Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical space…
LeMat-Bulk: aggregating, and de-duplicating quantum chemistry materials databases
Martin Siron, Inel Djafar, Ali Ramlaoui +10
The rapid expansion of materials science databases has driven machine learning-based discovery while also posing challenges in data integration, duplication, and interoperability.…
MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNets
Dominic Phillips, Flaviu Cipcigan
Generative Flow Networks (GFlowNets) are a class of generative models that sample objects in proportion to a specified reward function through a learned policy. They can be trained…
Symbolic Learning for Material Discovery
Daniel Cunnington, Flaviu Cipcigan, Rodrigo Neumann Barros Ferreira +1
Discovering new materials is essential to solve challenges in climate change, sustainability and healthcare. A typical task in materials discovery is to search for a material in a…
Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GFlowNets
Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira +2
Artificial intelligence holds promise to improve materials discovery. GFlowNets are an emerging deep learning algorithm with many applications in AI-assisted discovery. By using GF…
Machine Guided Discovery of Novel Carbon Capture Solvents
James L. McDonagh, Benjamin H. Wunsch, Stamatia Zavitsanou +5
The increasing importance of carbon capture technologies for deployment in remediating CO2 emissions, and thus the necessity to improve capture materials to allow scalability and e…
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
Payel Das, Tom Sercu, Kahini Wadhawan +12
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e.g., high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled La…
Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed
Flaviu S. Cipcigan, Vlad P. Sokhan, Jason Crain +1
One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heter…
Molecular-scale remnants of the liquid-gas transition in supercritical polar fluids
V. P. Sokhan, A. Jones, F. S. Cipcigan +2
An electronically coarse-grained model for water reveals a persistent vestige of the liquid-gas transition deep into the supercritical region. A crossover in the density dependence…