323 citations · 861 across the 26 of their papers we have counts for
14 papers · 1 filter
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…
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…
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…
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…
Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost
Akash Bajaj, Jon Paul Janet, Heather J. Kulik
The flat plane condition is the union of two exact constraints in electronic structure theory: i) energetic piecewise linearity with fractional electron removal or addition and ii)…
Resolving transition metal chemical space: feature selection for machine learning and structure-property relationships
Jon Paul Janet, Heather J. Kulik
Machine learning (ML) of quantum mechanical properties shows promise for accelerating chemical discovery. For transition metal chemistry where accurate calculations are computation…
Predicting Electronic Structure Properties of Transition Metal Complexes with Neural Networks
Jon Paul Janet, Heather J. Kulik
High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and mol…