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Eliminating Delocalization Error to Improve Heterogeneous Catalysis Predictions with Molecular DFT+U
Approximate semilocal density functional theory (DFT) is known to underestimate surface formation energies yet paradoxically overbind …
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Eliminating Delocalization Error to Improve Heterogeneous Catalysis Predictions with Molecular DFT+U
Harder, better, faster, stronger: large-scale QM and QM/MM for predictive modeling in enzymes and proteins
Computational prediction of enzyme mechanism and protein function requires accurate physics-based models and suitable sampling. We …
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Harder, better, faster, stronger: large-scale QM and QM/MM for predictive modeling in enzymes and proteins
Irreversible synthesis of an ultrastrong two-dimensional polymeric material
Polymers that extend covalently in two dimensions have attracted recent attention as a means of combining the mechanical strength and …
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Irreversible synthesis of an ultrastrong two-dimensional polymeric material
Large-scale Screening Reveals Geometric Structure Matters More than Electronic Structure in Bioinspired Catalyst Design of Formate Dehydrogenase Mimics
The design of inorganic molecular complexes for the reversible conversion of formate into CO2 inspired by formate dehydrogenase (FDH) …
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Large-scale Screening Reveals Geometric Structure Matters More than Electronic Structure in Bioinspired Catalyst Design of Formate Dehydrogenase Mimics
Ligand Additivity and Divergent Trends in Two Types of Delocalization Errors from Approximate Density Functional Theory
The predictive accuracy of density functional theory (DFT) is hampered by delocalization errors, especially for correlated systems such …
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Ligand Additivity and Divergent Trends in Two Types of Delocalization Errors from Approximate Density Functional Theory
Machine Learning for the Discovery, Design, and Engineering of Materials
Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite …
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Machine Learning for the Discovery, Design, and Engineering of Materials
Machine Learning Reveals Key Ion Selectivity Mechanisms in Polymeric Membranes
Designing single-species selective membranes for high-precision separations requires a fundamental understanding of the molecular …
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Machine Learning Reveals Key Ion Selectivity Mechanisms in Polymeric Membranes
Modeling the roles of rigidity and dopants in single-atom methane-to-methanol catalysts
Doped graphitic single-atom catalysts (SACs) with isolated iron sites have similarities to natural enzymes and molecular biomimetics …
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Modeling the roles of rigidity and dopants in single-atom methane-to-methanol catalysts
MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks
We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework …
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MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks
Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry
Low-cost, non-empirical corrections to semi-local density functional theory are essential for accurately modeling transition-metal …
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Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry