81 citations · 132 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
A new semiempirical formula for exotic cluster decays of nuclei
M. Balasubramaniam, S. Kumarasamy, N. Arunachalam +1
A new semiempirical formula, with only three parameters, is proposed for cluster decay half-lives. The parameters of the formula are obtained by making a least square fit to the av…