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Articles by Aditya Nandy
Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery
MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules
1. Introduction Click to copy section linkSection link copied! In recent years, carbon emissions have been on the rise, with 35.8 Gt emitted globally in 2023. (1,2) These escalating levels of greenhouse gases in the atmosphere demand immediate attention as they contribute significantly to climate change. (3) To mitigate these effects, adsorption strategies for capturing and storing greenhouse gases are being studied.
Protein3D: Enabling analysis and extraction of metal‐containing sites from the Protein Data Bank with molSimplify
1 INTRODUCTION Metalloenzymes make up nearly 40% of all enzymes1, 2 and catalyze some of nature's most challenging reactions such as the reduction of nitrogen gas to ammonia,3 splitting of water,4 and the activation of CH bonds in rather inert species with high selectivity.5, 6 Computation plays a key role in assigning intermediates7, 8 in metalloenzyme catalytic cycles, probing the electronic structure of metallocofactors,9-11 and aiding in crystallographic refinement of metal centers in...
Discovering Molecular Coordination Environment Trends for Selective Ion Binding to Molecular Complexes Using Machine Learning
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Phys. Chem. B 2023, XXXX, XXX Terms & Conditions Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses).
Computational Discovery of Stable Metal-Organic Frameworks for Methane-to-Methanol Catalysis
The challenge of direct partial oxidation of methane to methanol has motivated the targeted search of metal–organic frameworks (MOFs) as a promising class of materials for this transformation because of their site-isolated metals with tunable ligand environments. Thousands of MOFs have been synthesized, yet relatively few have been screened for their promise in methane conversion.
Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes
We survey more than 240 000 crystallized mononuclear transition metal complexes (TMCs) to identify trends in preferred geometric structure and metal coordination. While we observe that an increased level of d filling correlates with a lower coordination number preference, we note exceptions, and we observe undersampling of 4d/5d transition metals and 3p-coordinating ligands.
Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models
Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models Yael Cytter, Aditya Nandy, Chenru Duan and Heather Kulik Abstract Virtual high-throughput screening (VHTS) and machine learning (ML) with density functional theory (DFT) suffer from inaccuracies from the underlying density functional approximation (DFA).
Low-cost machine learning prediction of excited state properties of iridium-centered phosphors
Low-cost machine learning prediction of excited state properties of iridium-centered phosphors† Gianmarco G. Terrones, a Chenru Duan, ab Aditya Nandy ab and Heather J. Kulik *ab Abstract Prediction of the excited state properties of photoactive iridium complexes challenges ab initio methods such as time-dependent density functional theory (TDDFT) both from the perspective of accuracy and of computational cost, complicating high-throughput virtual screening (HTVS).
Detection of multi-reference character imbalances enables a transfer learning approach for chemical discovery with coupled cluster accuracy at DFT cost
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MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks - Scientific Data
Abstract 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 characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data.
Modeling the roles of rigidity and dopants in single-atom methane-to-methanol catalysts
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Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks
1. Introduction Metal–organic frameworks (MOFs) have the potential to revolutionize catalysis (1) and functional materials (2) design due to their reticular nature and well-defined, isolated metal sites. (3,4) To meet numerous design criteria, an exhaustive search of MOFs must be carried out for each potential application, beyond the tens of MOFs that an experimentalist could synthesize.
Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Phys. Chem. Lett. 2021, 12, 40 RETURN TO ISSUEPREVPhysical Insights in...Physical Insights into Chemistry, Catalysis, and InterfacesNEXT Michael G. Taylor, Aditya Nandy Aditya Nandy Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States , Connie C. Lu, and Heather J.
Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the biases of training data derived from density functional theory (DFT) and leads to many attempted calculations that are doomed to fail.
Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics
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Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
1. Introduction Determination of the ground-state spins of open-shell transition-metal complexes is essential to understanding their catalytic(1−6) and materials(7−13) properties. Nevertheless, prediction of spin-state ordering is extremely sensitive to electronic structure method choice. Correlated wave function theory methods exhibit limitations in predicting properties of open-shell transition-metal complexes(14−17) and remain cost-prohibitive for large-scale, high-throughput screening.
Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation
The selective partial oxidation of alkanes (e.g., methane to methanol(1,2)) represents one of the foremost challenges in catalysis. Despite intense focus,(3−8) the design of highly selective and active synthetic catalysts has been limited by overoxidation as a result of the higher reactivity of products than reactants.
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
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Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Top of Page Abstract Introduction Computational Details Results and Discussion Conclusions and Outlook References 1.
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