H. J. Kulik

Latest

• Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character Across Known Transition Metal Complex Ligands
• Influence of the Greater Protein Environment on the Electrostatic Potential in Metalloenzyme Active Sites: The Case of Formate Dehydrogenase
• Machine learning models predict calculation outcomes with the transferability necessary for computational catalysis
• Mechanistic Studies of a Skatole-Forming Glycyl Radical Enzyme Suggest Reaction Initiation via Hydrogen Atom Transfer
• Mechanochemically Assisted Release of Hydrogen Fluoride and its Application in Triggered Polymer Degradation
• Plug-and-Play Heterogeneous Catalysis with Metal–Organic Cage-Crosslinked Polymers
• Using Computational Chemistry to Reveal Nature's Blueprints for Single-Site Catalysis of C–H Activation
• Fluids and Electrolytes under Confinement in Single-Digit Nanopores
• 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
• 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
• Eliminating Delocalization Error to Improve Heterogeneous Catalysis Predictions with Molecular DFT+U
• 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
• Machine Learning for the Discovery, Design, and Engineering of Materials
• Machine Learning Reveals Key Ion Selectivity Mechanisms in Polymeric Membranes
• 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
• Probing the mechanism of isonitrile formation by a non-heme iron(II)-dependent oxidase/decarboxylase
• Representations and Strategies for Transferable Machine Learning Improve Model Performance in Chemical Discovery
• Computational Discovery of Transition-Metal Complexes: From High-throughput Screening to Machine Learning
• Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning
• Machine learning to tame divergent density functional approximations a new path to consensus materials design principles
• Mapping the Origins of Surface- and Chemistry-Dependent Doping Trends in III-V Quantum Dots with Density Functional Theory
• Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
• Spectroscopically Guided Simulations Reveal Distinct Strategies for Positioning Substrates to Achieve Selectivity in Nonheme Fe(II)/α-Ketoglutarate-Dependent Halogenases
• Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks