13 citations · 22 across the 16 of their papers we have counts for
16 papers
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…
Few-step Cofolding with All-Atom Flow Maps
Gianluca Scarpellini, Ron Shprints, Peter Holderrieth +7
All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems. Generating struc…
MetaDNS: Enhancing Exploration in Discrete Neural Samplers via Well-Tempered Metadynamics
Xiaochen Du, Juno Nam, Jaemoo Choi +7
Sampling from discrete distributions with multiple modes and energy barriers is fundamental to machine learning and computational physics. Recent discrete neural samplers like MDNS…
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…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
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…
PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment
Akshay Subramanian, Elton Pan, Juno Nam +6
Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because ca…
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…
Transferable Learning of Reaction Pathways from Geometric Priors
Juno Nam, Miguel Steiner, Max Misterka +3
Identifying minimum-energy paths (MEPs) is crucial for understanding chemical reaction mechanisms but remains computationally demanding. We introduce MEPIN, a scalable machine-lear…
Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search
Akshay Subramanian, James Damewood, Juno Nam +3
Organic optoelectronic materials are a promising avenue for next-generation electronic devices due to their solution processability, mechanical flexibility, and tunable electronic…
Think While You Generate: Discrete Diffusion with Planned Denoising
Sulin Liu, Juno Nam, Andrew Campbell +4
Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce Discrete Diffusi…
Flow Matching for Accelerated Simulation of Atomic Transport in Crystalline Materials
Juno Nam, Sulin Liu, Gavin Winter +3
Atomic transport underpins the performance of materials in technologies such as energy storage and electronics, yet its simulation remains computationally demanding. In particular,…
Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials
Juno Nam, Jiayu Peng, Rafael Gómez-Bombarelli
Machine learning interatomic potentials (MLIPs) have become a workhorse of modern atomistic simulations, and recently published universal MLIPs, pre-trained on large datasets, have…
Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation
Soojung Yang, Juno Nam, Johannes C. B. Dietschreit +1
In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a co…