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

58 citations · 91 across the 10 of their papers we have counts for

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cond-mat.mtrl-sci20221 cited

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…

cond-mat.mtrl-sci202258 cited

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…

cond-mat.mtrl-sci2022

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…

cond-mat.mtrl-sci20216 cited

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…

cond-mat.mtrl-sci2021

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…

cond-mat.mtrl-sci2021

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…