2 citations · 2 across the 3 of their papers we have counts for
10 papers
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…
High-Throughput Transition-State Searches in Zeolite Nanopores
Pau Ferri-Vicedo, Alexander J. Hoffman, Avni Singhal +1
Zeolites are important for industrial catalytic processes involving organic molecules. Understanding molecular reaction mechanisms within the confined nanoporous environment can gu…
Accelerating and enhancing thermodynamic simulations of electrochemical interfaces
Xiaochen Du, Mengren Liu, Jiayu Peng +6
Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsor…
Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matching
Akshay Subramanian, Shuhui Qu, Cheol Woo Park +3
Amorphous molecular solids offer a promising alternative to inorganic semiconductors, owing to their mechanical flexibility and solution processability. The packing structure of th…
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,…