323 citations · 861 across the 26 of their papers we have counts for
28 papers
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…
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models
Aditya Nandy, Shuwen Yue, Changhwan Oh +4
High-throughput screening of large hypothetical databases of metal-organic frameworks (MOFs) can uncover new materials, but their stability in real-world applications is often unkn…
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…
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Chenru Duan, Fang Liu, Aditya Nandy +1
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. N…
Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands
Chenru Duan, Adriana J. Ladera, Julian C. -L. Liu +3
Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multi-reference (MR) character that compli…
Ligand Additivity and Divergent Trends in Two Types of Delocalization Errors from Approximate Density Functional Theory
Yael Cytter, Aditya Nandy, Akash Bajaj +1
Despite its widespread use, the predictive accuracy of density functional theory (DFT) is hampered by delocalization errors, especially for correlated systems such as transition-me…
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…
Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition metal chemistry
Akash Bajaj, Chenru Duan, Aditya Nandy +2
Low-cost, non-empirical corrections to semi-local density functional theory are essential for accurately modeling transition metal chemistry. Here, we demonstrate the judiciously-m…
Eliminating Delocalization Error to Improve Heterogeneous Catalysis Predictions with Molecular DFT+U
Akash Bajaj, Heather J. Kulik
Approximate semi-local density functional theory (DFT) is known to underestimate surface formation energies yet paradoxically overbind adsorbates on catalytic transition-metal oxid…
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…
MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks
A. Nandy, G. Terrones, N. Arunachalam +3
We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally chara…
Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning
Michael G. Taylor, Aditya Nandy, Connie C. Lu +1
The rational tailoring of transition metal complexes is necessary to address outstanding challenges in energy utilization and storage. Heterobimetallic transition metal complexes t…
Mapping the Electronic Structure Origins of Surface- and Chemistry-Dependent Doping Trends in III-V Quantum Dots
Michael G. Taylor, Heather J. Kulik
Modifying the optoelectronic properties of nanostructured materials through introduction of dopant atoms has attracted intense interest. Nevertheless, the approaches employed are o…
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks
Aditya Nandy, Chenru Duan, Heather J. Kulik
Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of underst…
Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles
Chenru Duan, Shuxin Chen, Michael G. Taylor +2
Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By nec…
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…
An Irreversible Synthetic Route to an Ultra-Strong Two-Dimensional Polymer
Yuwen Zeng, Pavlo Gordiichuk, Takeo Ichihara +14
Polymers that extend covalently in two dimensions have attracted recent attention as a means of combining the mechanical strength and in-plane energy conduction of conventional two…
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…