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Roadmap on Machine Learning in Electronic Structure
In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly …
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Roadmap on Machine Learning in Electronic Structure
Influence of the Greater Protein Environment on the Electrostatic Potential in Metalloenzyme Active Sites: The Case of Formate Dehydrogenase
The Mo/W-containing metalloenzyme formate dehydrogenase (FDH) is an efficient and selective natural catalyst that reversibly converts …
PDF Cite DOI ChemRxiv
Influence of the Greater Protein Environment on the Electrostatic Potential in Metalloenzyme Active Sites: The Case of Formate Dehydrogenase
What's Left for a Computational Chemist To Do in the Age of Machine Learning?
Machine learning (ML) has become a central focus of the computational chemistry community. I will first discuss my personal history in …
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What's Left for a Computational Chemist To Do in the Age of Machine Learning?
Are Vanadium Intermediates Suitable Mimics in Non-Heme Iron Enzymes? An Electronic Structure Analysis
Vanadyl intermediates are frequently used as mimics for the fleeting Fe(IV)═O intermediate in non-heme iron enzymes that catalyze C–H …
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Are Vanadium Intermediates Suitable Mimics in Non-Heme Iron Enzymes? An Electronic Structure Analysis
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property …
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Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Computational Modeling of Conformer Stability in Benenodin-1, a Thermally Actuated Lasso Peptide Switch
Benenodin-1 is a thermally actuated lasso peptide rotaxane switch with two primary translational isomers that differ in the relative …
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Computational Modeling of Conformer Stability in Benenodin-1, a Thermally Actuated Lasso Peptide Switch
Computational Scaling Relationships Predict Experimental Activity and Rate Limiting Behavior in Homogenous Water Oxidation
While computational screening with first-principles density functional theory (DFT) is essential for evaluating candidate catalysts, …
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Computational Scaling Relationships Predict Experimental Activity and Rate Limiting Behavior in Homogenous Water Oxidation
Detection of multi-reference character imbalances enables a transfer learning approach for chemical discovery with coupled cluster accuracy at DFT cost
Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving …
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Detection of multi-reference character imbalances enables a transfer learning approach for chemical discovery with coupled cluster accuracy at DFT cost
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