58 citations · 127 across the 15 of their papers we have counts for
17 papers
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks
Chenru Duan, Aditya Nandy, Sizhan Liu +5
Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals, yet their astronomical design space defies brute-force…
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…
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties
Gianmarco Terrones, Chenru Duan, Aditya Nandy +1
Photoactive iridium complexes are of broad interest due to their applications ranging from lighting to photocatalysis. However, the excited state property prediction of these compl…
Active Learning Exploration of Transition Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores
Chenru Duan, Aditya Nandy, Gianmarco Terrones +2
Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is…
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…
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…
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…
Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
Chenru Duan, Aditya Nandy, Husain Adamji +2
Virtual high throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is oft…
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy
Chenru Duan, Daniel B. K. Chu, Aditya Nandy +1
Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high throughpu…
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…
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…
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…
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…