Every reference with a DOI in the deposited reference list resolved to a known
work in Crossref or DataCite at the dated check, and none carried a retraction,
withdrawal, or removal notice.
The 113 checked references that resolve
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resolves10.1021/jacs.0c09105The Role of Machine Learning in the Understanding and Design of Materials
resolves10.1002/jcc.25787Identifying promising metal–organic frameworks for heterogeneous catalysis via high‐throughput periodic density functional theory
resolves10.1016/j.matt.2021.02.015Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery
resolves10.1063/1.2403848Many-electron self-interaction error in approximate density functionals
resolves10.1021/acs.jctc.9b00322Large-Scale Benchmark of Exchange–Correlation Functionals for the Determination of Electronic Band Gaps of Solids
resolves10.1103/PhysRevB.50.14947All-electron local-density and generalized-gradient calculations of the structural properties of semiconductors
resolves10.1063/1.3076922Calculation of semiconductor band gaps with the M06-L density functional
resolves10.26434/chemrxiv-2021-gwm9m-v2Performance comparison of r2SCAN and SCAN metaGGA density functionals for solid materials via an automated, high-throughput computational workflow
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resolves10.1021/acs.jpcc.7b01066HLE17: An Improved Local Exchange–Correlation Functional for Computing Semiconductor Band Gaps and Molecular Excitation Energies
resolves10.1063/1.2404663Influence of the exchange screening parameter on the performance of screened hybrid functionals
resolves10.1063/1.4812323Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
resolves10.1039/B812838CScreened hybrid density functionals for solid-state chemistry and physics
resolves10.1021/acs.jctc.9b00842Impact of Approximate DFT Density Delocalization Error on Potential Energy Surfaces in Transition Metal Chemistry
resolves10.1063/1.4926836Towards quantifying the role of exact exchange in predictions of transition metal complex properties
resolves10.1039/D0CS01074JReplacing hybrid density functional theory: motivation and recent advances
resolves10.1021/jp952944uElectron Densities of Several Small Molecules As Calculated from Density Functional Theory
resolves10.1063/1.479620The accuracy of current density functionals for the calculation of electric field gradients: A comparison with <i>ab initio</i> methods for HCl and CuCl
resolves10.1021/jp064467tPd<i><sub>n</sub></i>CO (<i>n</i> = 1,2): Accurate Ab Initio Bond Energies, Geometries, and Dipole Moments and the Applicability of Density Functional Theory for Fuel Cell Modeling
resolves10.1021/acs.jctc.7b01061Where Does the Density Localize in the Solid State? Divergent Behavior for Hybrids and DFT+U
resolves10.1021/acs.jpca.0c06939Evaluation of Local Hybrid Functionals for Electric Properties: Dipole Moments and Static and Dynamic Polarizabilities
resolves10.1021/ct500790pModeling the Partial Atomic Charges in Inorganometallic Molecules and Solids and Charge Redistribution in Lithium-Ion Cathodes
resolves10.1039/C6RA04656HIntroducing DDEC6 atomic population analysis: part 1. Charge partitioning theory and methodology
resolves10.1039/C6RA05507AIntroducing DDEC6 atomic population analysis: part 2. Computed results for a wide range of periodic and nonperiodic materials
resolves10.1039/C7RA07400JIntroducing DDEC6 atomic population analysis: part 3. Comprehensive method to compute bond orders
resolves10.1021/ct100125xChemically Meaningful Atomic Charges That Reproduce the Electrostatic Potential in Periodic and Nonperiodic Materials
resolves10.1039/C9RA07755CLocal structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity
resolves10.1021/acs.jctc.6b00937Where Does the Density Localize? Convergent Behavior for Global Hybrids, Range Separation, and DFT+U
resolves10.1021/jp0482666Atomic Charges Are Measurable Quantum Expectation Values: A Rebuttal of Criticisms of QTAIM Charges
resolves10.1039/C7RA11829EIntroducing DDEC6 atomic population analysis: part 4. Efficient parallel computation of net atomic charges, atomic spin moments, bond orders, and more
resolves10.1021/ct200866dCharge Model 5: An Extension of Hirshfeld Population Analysis for the Accurate Description of Molecular Interactions in Gaseous and Condensed Phases
resolves10.1039/D0RA06392DSeven confluence principles: a case study of standardized statistical analysis for 26 methods that assign net atomic charges in molecules
resolves10.1021/acs.jpcc.0c04903Message Passing Neural Networks for Partial Charge Assignment to Metal–Organic Frameworks
resolves10.1021/acs.jctc.0c01229Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks
resolves10.1021/acs.chemmater.0c02468Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials
resolves10.1002/adts.201900131Materials Databases: The Need for Open, Interoperable Databases with Standardized Data and Rich Metadata
resolves10.1002/cpe.3698User applications driven by the community contribution framework MPContribs in the Materials Project
resolves10.1021/acs.cgd.9b01050Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis
resolves10.1103/PhysRevB.54.11169Efficient iterative schemes for<i>ab initio</i>total-energy calculations using a plane-wave basis set
resolves10.1063/1.3382344A consistent and accurate<i>ab initio</i>parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
resolves10.1002/jcc.21759Effect of the damping function in dispersion corrected density functional theory
resolves10.1063/1.4722993Analysis of the Heyd-Scuseria-Ernzerhof density functional parameter space
resolves10.1021/am507016rElectronic Structure Modulation of Metal–Organic Frameworks for Hybrid Devices
resolves10.1063/1.4922693Perspective: Treating electron over-delocalization with the DFT+U method
resolves10.1063/1.4947240First-principles Hubbard <i>U</i> approach for small molecule binding in metal-organic frameworks
resolves10.1063/5.0010166Comparing GGA, GGA+<i>U</i>, and meta-GGA functionals for redox-dependent binding at open metal sites in metal–organic frameworks
resolves10.1103/PhysRevB.73.195107Oxidation energies of transition metal oxides within the<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>GGA</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant="normal">U</mml:mi></mml:mrow></mml:math>framework
resolves10.1103/PhysRevB.84.045115Formation enthalpies by mixing GGA and GGA<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>U</mml:mi></mml:mrow></mml:math>calculations
resolves10.1021/acs.jctc.0c00320Relationships between Orbital Energies, Optical and Fundamental Gaps, and Exciton Shifts in Approximate Density Functional Theory and Quasiparticle Theory
resolves10.1039/c3cp52547cThe Kohn–Sham gap, the fundamental gap and the optical gap: the physical meaning of occupied and virtual Kohn–Sham orbital energies
resolves10.1063/1.5019779SchNet – A deep learning architecture for molecules and materials
The 8 references without a DOI — listed, not checked
no DOI — not checkedAtkins P., Overton T., Rourke J., Weller M., Armstrong F., M. H. Shriver & Atkins’ Inorganic Chemistry. (Oxford University Press, 2009).
no DOI — not checkedMPContribs. https://mpcontribs.org.
no DOI — not checkedMPContribs-Client. https://pypi.org/project/mpcontribs-client.
no DOI — not checkedManz, T. A. & Gabaldon Limas, N. Chargemol program for performing DDEC analysis. http://ddec.sourceforge.net/.
no DOI — not checkedKingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
no DOI — not checkedLoshchilov, I. & Hutter, F. Decoupled weight decay regularization. Preprint at https://arxiv.org/abs/1711.05101 (2017).
no DOI — not checkedPaszke, A. et al. PyTorch: An imperative style, high-performance deep learning library. in Advances in Neural Information Processing Systems 8024–8035 (2019).
no DOI — not checkedFey, M. & Lenssen, J. E. Fast graph representation learning with PyTorch Geometric. Preprint at https://arxiv.org/abs/1903.02428 (2019).
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