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Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial …
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Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
Quantifying the Long-Range Coupling of Electronic Properties in Proteins with Ab Initio Molecular Dynamics
A delicate interplay of covalent and noncovalent interactions gives proteins their unique ability to flexibly play numerous roles in …
PDF Cite DOI ChemRxiv
Quantifying the Long-Range Coupling of Electronic Properties in Proteins with Ab Initio Molecular Dynamics
Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs
Community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that …
Daniel G. A. Smith, Annabelle T. Lolinco, Zachary L. Glick, Jiyoung Lee, Asem Alenaizan, Taylor A. Barnes, Carlos H. Borca, Roberto Di Remigio, David L. Dotson, Sebastian Ehlert, Alexander G. Heide, Michael F. Herbst, Jan Hermann, Colton B. Hicks, Joshua T. Horton, Adrian G. Hurtado, Peter Kraus, Holger Kruse, Sebastian J. R. Lee, Jonathon P. Misiewicz, Levi N. Naden, Farhad Ramezanghorbani, Maximilian Scheurer, Jeffrey B. Schriber, Andrew C. Simmonett, Johannes Steinmetzer, Jeffrey R. Wagner, Logan Ward, Matthew Welborn, Doaa Altarawy, Jamshed Anwar, John D. Chodera, Andreas Dreuw, Heather J. Kulik, Fang Liu, Todd J. Martínez, Devin A. Matthews, Henry F. Schaefer, Jiří Šponer, Justin M. Turney, Lee-Ping Wang, Nuwan De Silva, Rollin A. King, John F. Stanton, Mark S. Gordon, Theresa L. Windus, C. David Sherrill, Lori A. Burns
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Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs
Spectroscopically Guided Simulations Reveal Distinct Strategies for Positioning Substrates to Achieve Selectivity in Nonheme Fe(II)/α-Ketoglutarate-Dependent Halogenases
Nonheme iron halogenases, such as SyrB2, WelO5, and BesD, halogenate unactivated carbon atoms of diverse substrates at ambient …
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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
Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from …
PDF Cite DOI arXiv MIT News
Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks
When are two hydrogen bonds better than one? Accurate first-principles models explain the balance of hydrogen bond donors and acceptors found in proteins
Hydrogen bonds (HBs) play an essential role in the structure and catalytic action of enzymes, but a complete understanding of HBs in …
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When are two hydrogen bonds better than one? Accurate first-principles models explain the balance of hydrogen bond donors and acceptors found in proteins
Ionization behavior of nanoporous polyamide membranes
Escalating global water scarcity necessitates high-performance desalination membranes, for which fundamental understanding of …
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Ionization behavior of nanoporous polyamide membranes
Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts
The design of selective and active C–H activation catalysts for direct methane-to-methanol conversion is challenging. Bioinspired …
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Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts
Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening
Despite its widespread use in chemical discovery, approximate density functional theory (DFT) is poorly suited to many targets, such as …
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Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening
Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost
Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference …
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Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost