activity
20192025
most citedPutting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

58 citations · 115 across the 17 of their papers we have counts for

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Showing physics.chem-phShow all

10 papers · 1 filter

physics.chem-ph2025

HORM: A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials

Taoyong Cui, Yunhong Han, Haojun Jia +2

Transition state (TS) characterization is central to computational reaction modeling, yet conventional approaches depend on expensive density functional theory (DFT) calculations,…

physics.chem-ph2024★ 4 cited

React-OT: Optimal Transport for Generating Transition State in Chemical Reactions

Chenru Duan, Guan-Horng Liu, Yuanqi Du +6

Transition states (TSs) are transient structures that are key in understanding reaction mechanisms and designing catalysts but challenging to be captured in experiments. Alternativ…

physics.chem-ph2023★ 4 cited

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

Chenru Duan, Yuanqi Du, Haojun Jia +1

Transition state (TS) search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D TS structures, however, requires nu…

physics.chem-ph2022

Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties

Gianmarco Terrones, Chenru Duan, Aditya Nandy +1

Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these compl…

physics.chem-ph2022★ 1 cited

Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores

Chenru Duan, Aditya Nandy, Gianmarco Terrones +2

Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is…

physics.chem-ph2022★ 24 cited

Ligand additivity relationships enable efficient exploration of transition metal chemical space

Naveen Arunachalam, Stefan Gugler, Michael G. Taylor +7

To accelerate exploration of chemical space, it is necessary to identify the compounds that will provide the most additional information or value. A large-scale analysis of mononuc…