3 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
ALF: An Active Learning Framework for Scientific Discovery
Shikha Surana, Alex Hawkins-Hooker, Olivia Gallup +3
Machine learning for scientific discovery is almost systematically data bound. Producing relevant high quality data, under budget constraints, is amongst the most promising ways to…
Search at the Cost of Sampling: Nearly-Instant Latent Space Bayesian Optimization
Donney Fan, Colin Doumont, Aleksandra Kalisz +4
Generative models are increasingly central to many de novo discovery pipelines, in which designs are generated at scale and filtered through virtual screens to determine a set of c…
How to Spend Your Oracle Budget: Practical Guidance for Protein Structure Prediction Models
Aleksandra Kalisz, Jack Simons, Krisztina Sinkovics +4
Foundation models for protein structure prediction remain unreliable on certain targets. External oracles can flag and correct these failures, but biological oracles are expensive,…
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Eszter Varga-Umbrich, Zachary Weller-Davies, Paul Duckworth +3
Active learning for machine-learning interatomic potentials (MLIPs) must address several challenges to be practical: scaling to large candidate pools, leveraging energy-force super…
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Eszter Varga-Umbrich, Shikha Surana, Paul Duckworth +3
Training machine learning interatomic potentials (MLIPs) for reactive chemistry is often bottlenecked by the high cost of quantum chemical labels and the scarcity of transition sta…