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Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
Determination of ground-state spins of open-shell transition-metal complexes is critical to understanding catalytic and materials …
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Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions
Enumeration of de novo inorganic complexes for chemical discovery and machine learning
Despite being attractive targets for functional materials, the discovery of transition metal complexes with high-throughput …
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Enumeration of de novo inorganic complexes for chemical discovery and machine learning
Impact of Approximate DFT Density Delocalization Error on Potential Energy Surfaces in Transition Metal Chemistry
For approximate density functional theory (DFT) to be useful in catalytic applications of transition metal complexes, modeling …
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Impact of Approximate DFT Density Delocalization Error on Potential Energy Surfaces in Transition Metal Chemistry
Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics
Density functional theory (DFT) is widely used in transition-metal chemistry, yet essential properties such as spin-state energetics in …
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Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics
Making machine learning a useful tool in the accelerated discovery of transition metal complexes
As machine learning (ML) has matured, it has opened a new frontier in theoretical and computational chemistry by offering the promise …
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Making machine learning a useful tool in the accelerated discovery of transition metal complexes
Critical Knowledge Gaps in Mass Transport through Single-Digit Nanopores: A Review and Perspective
Not all nanopores are created equal. By definition, nanopores have characteristic diameters or conduit widths between ∼1 and 100 nm. …
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Critical Knowledge Gaps in Mass Transport through Single-Digit Nanopores: A Review and Perspective
Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation
Metal–oxo moieties are important catalytic intermediates in the selective partial oxidation of hydrocarbons and in water splitting. …
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Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation
Reply to “Comment on ‘Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis’”
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Reply to “Comment on ‘Evaluating Unexpectedly Short Non-covalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis’”
Stable Surfaces That Bind Too Tightly: Can Range-Separated Hybrids or DFT+U Improve Paradoxical Descriptions of Surface Chemistry?
Approximate, semilocal density functional theory (DFT) suffers from delocalization error that can lead to a paradoxical model of …
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Stable Surfaces That Bind Too Tightly: Can Range-Separated Hybrids or DFT+U Improve Paradoxical Descriptions of Surface Chemistry?
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design …
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Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry