3 citations · 4 across the 14 of their papers we have counts for
17 papers
Drude, Where's My Charge? End-to-End Learning of Polarizable Drude Force Fields for Polymer Electrolytes
Artur Lyssenko, KyuJung Jun, Rafael Gomez-Bombarelli
Molecular dynamics simulations of solid polymer electrolytes require force fields that are fast enough to converge ion transport over hundreds of nanoseconds, yet accurately descri…
LLM sequential decision making under uncertainty in biochemical domains
Mattias Akke, Soojung Yang, Jurgis Ruža +2
Large language models (LLMs) are increasingly used to drive scientific discovery. Understanding how LLMs make decisions from new data and memory of the literature is vital before t…
Vibrational, structural, and chemical fingerprints of ion diffusion in crystalline solids
Gavin Winter, Juno Nam, Rafael Gómez-Bombarelli
Predicting mobile-ion self-diffusivity from molecular dynamics (MD) simulations is essential for identifying promising solid-state electrolytes, but directly simulating ion d…
Universal Thermodynamic Interatomic Potentials for Crystalline Materials
Juno Nam, Bowen Deng, Xiaochen Du +3
Free energies govern solid-state phase stability, yet computational materials discovery still relies largely on ground-state energies because free energy calculations require ensem…
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…
Scaling Autoregressive Models for Lattice Thermodynamics
Xiaochen Du, Juno Nam, Sulin Liu +1
Predicting how materials behave under realistic conditions requires understanding the statistical distribution of atomic configurations on crystal lattices, a problem central to al…
Discovery of Polymer Electrolytes with Bayesian Optimization and High-Throughput Molecular Dynamics simulations
Antonia S. Kuhn, Jurğis Ruža, KyuJung Jun +2
Polymer electrolytes are critical for safe, high-energy-density solid-state batteries, yet discovering candidates that balance high ionic conductivity with high transference number…
Universal Framework for Decomposing Ionic Transport into Interpretable Mechanisms
KyuJung Jun, Pablo A. Leon, Jurğis Ruža +2
Understanding mechanisms of ion transport in bulk materials is central to designing next-generation ion conductors for energy storage devices, yet studies employing all-atom molecu…
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…
Discrete Adjoint Schrödinger Bridge Sampler
Wei Guo, Yuchen Zhu, Xiaochen Du +6
Learning discrete neural samplers is challenging due to the lack of gradients and combinatorial complexity. While stochastic optimal control (SOC) and Schrödinger bridge (SB) provi…
Universally Converging Representations of Matter Across Scientific Foundation Models
Sathya Edamadaka, Soojung Yang, Ju Li +1
Machine learning models of vastly different modalities and architectures are being trained to predict the behavior of molecules, materials, and proteins. However, it remains unclea…
Microstructural Insights into Fast Ion Transport in Solid Electrolytes via Multiscale Modeling
Yongliang Ou, Lena Scholz, Sanath Keshav +7
Improving solid electrolytes is critical for high-performance all-solid-state batteries, yet the microstructural features that enable fast ion transport remain poorly understood. H…
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…
Learning Collective Variables from BioEmu with Time-Lagged Generation
Seonghyun Park, Kiyoung Seong, Soojung Yang +2
Molecular dynamics is crucial for understanding molecular systems but its applicability is often limited by the vast timescales of rare events like protein folding. Enhanced sampli…