323 citations · 861 across the 26 of their papers we have counts for
13 papers · 1 filter
Many-body Expansion Based Machine Learning Models for Octahedral Transition Metal Complexes
Ralf Meyer, Daniel Benjamin Kasman Chu, Heather J. Kulik
Graph-based machine learning models for materials properties show great potential to accelerate virtual high-throughput screening of large chemical spaces. However, in their simple…
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…
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…
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…
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…
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…
A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery
Chenru Duan, Aditya Nandy, Ralf Meyer +2
Approximate density functional theory (DFT) has become indispensable owing to its cost-accuracy trade-off in comparison to more computationally demanding but accurate correlated wa…
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
Chenru Duan, Aditya Nandy, Husain Adamji +2
Virtual high throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is oft…
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy
Chenru Duan, Daniel B. K. Chu, Aditya Nandy +1
Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high throughpu…
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Aditya Nandy, Chenru Duan, Heather J. Kulik
Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships. For many properties of interest in m…
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery
Daniel R. Harper, Aditya Nandy, Naveen Arunachalam +3
Strategies for machine-learning(ML)-accelerated discovery that are general across materials composition spaces are essential, but demonstrations of ML have been primarily limited t…
Molecular DFT+U: A Transferable, Low-Cost Approach to Eliminate Delocalization Error
Akash Bajaj, Heather J. Kulik
While density functional theory (DFT) is widely applied for its combination of cost and accuracy, corrections (e.g., DFT+U) that improve it are often needed to tackle correlated tr…
Systematic Quantum Mechanical Region Determination in QM/MM Simulation
Maria Karelina, Heather J. Kulik
Hybrid quantum mechanical-molecular mechanical (QM/MM) simulations are widely used in enzyme simulation. Over ten convergence studies of QM/MM methods have revealed over the past s…