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Understanding the diversity of the metal-organic framework ecosystem
Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 …
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Understanding the diversity of the metal-organic framework ecosystem
Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics
High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe …
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Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics
Uncovering Alternate Pathways to Nafion Membrane Degradation in Fuel Cells with First-Principles Modeling
Polymer electrolyte membrane fuel cells (PEMFCs) represent promising energy storage solutions, but challenges remain to maximize their …
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Uncovering Alternate Pathways to Nafion Membrane Degradation in Fuel Cells with First-Principles Modeling
Both Configuration and QM Region Size Matter: Zinc Stability in QM/MM Models of DNA Methyltransferase
Quantum-mechanical/molecular-mechanical (QM/MM) methods are essential to the study of metalloproteins, but the relative importance of …
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Both Configuration and QM Region Size Matter: Zinc Stability in QM/MM Models of DNA Methyltransferase
Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
The accelerated discovery of materials for real world applications requires the achievement of multiple design objectives. The …
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Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization
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