58 citations · 96 across the 12 of their papers we have counts for
6 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…
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…
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…