323 citations · 861 across the 26 of their papers we have counts for
11 papers · 1 filter
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…