3 citations · 4 across the 14 of their papers we have counts for
6 papers · 1 filter
Harnessing AtomisticSkills for Agentic Atomistic Research
Bowen Deng, Bohan Li, Matthew Cox +20
Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabi…
Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building
Sauradeep Majumdar, Miguel Steiner, Johannes C. B. Dietschreit +4
Free energy profiles serve as a fundamental bridge between microscopic atomic fluctuations and macroscopic thermodynamic observables. Estimating the free energy profile along a rea…
A Priori Sampling of Transition States with Guided Diffusion
Hyukjun Lim, Soojung Yang, Lucas Pinède +3
Transition states, the first-order saddle points on the potential energy surfaces, govern the kinetics and mechanisms of chemical reactions and conformational changes. Locating the…
Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors
Simon Axelrod, Miroslav Kašpar, Kristýna Jelínková +8
Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set o…
FragmentFlow: Scalable Transition State Generation for Large Molecules
Ron Shprints, Peter Holderrieth, Juno Nam +2
Transition states (TSs) are central to understanding and quantitatively predicting chemical reactivity and reaction mechanisms. Although traditional TS generation methods are compu…
Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Juno Nam, Bálint Máté, Artur P. Toshev +6
Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for mole…