Research Topic
high-throughput
7 publications exploring this topic
2025
AIMNet2-rxn: A Machine Learned Potential for Generalized Reaction Modeling on a Millions-of-Pathways Scale
(2025)
AIMNet2-rxn is a machine-learned interatomic potential trained on 4.7 10^6 range-separated DFT calculations that accelerates reaction modeling by about six orders of magnitude while retaining approximately 1–2 kcal/mol accuracy along reaction coordinates. By leveraging three‑dimensional chemical information and a batched nudged elastic band (BNEB) method, the model searches millions of reaction pathways and enables high‑throughput mechanistic analysis for complex transformations such as glucose pyrolysis.
Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
(2025)
Finding efficient substrate-catalyst combinations for palladium-catalyzed cross-coupling reactions remains a critical challenge in synthetic chemistry, with broad implications for pharmaceutical and materials manufacturing.
High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning
Chemical Science, 16, 20553–20563 (2025)
Ring Vault contains 201 546 cyclic molecules across 11 elements.
2024
In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
(2024)
The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods.
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential
Nature Chemistry, 16, 727–734 (2024)
Abstract Atomistic simulation has a broad range of applications from drug design to materials discovery.
2023
In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations
(2023)
The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge is focused on identifying small molecule inhibitors of protein targets using computational methods.
The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions
(2023)
Accurate prediction of reaction yield is the holy grail for computer-assisted synthesis, but current models have failed to generalize to large literature datasets.