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RESEARCH PAPER · Journal of Chemical Theory and Computation · 2020

Effective Molecular Descriptors for Chemical Accuracy at DFT Cost: Fragmentation, Error-Cancellation, and Machine Learning

Eric M. Collins, Krishnan Raghavachari

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Effective Molecular Descriptors for Chemical Accuracy at DFT Cost: Fragmentation, Error-Cancellation, and Machine LearningOriginalPairing double hybrid functionals with a tailored basis set for an accurate thermochemistry of hydrocarbons9Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions42Scaffold‐Directed Face Selectivity Machine‐Learned from Vectors of Non‐covalent Interactions15A Fragmentation-Based Graph Embedding Framework for QM/ML20Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning78Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning6Designing novel spiro compounds as favorable hole transport materials for quantum dot sensitized photovoltaics24Deep dive into machine learning density functional theory for materials science and chemistry111Toward Chemical Accuracy in Predicting Enthalpies of Formation with General-Purpose Data-Driven Methods53Leveraging genetic algorithms to maximise the predictive capabilities of the SOAP descriptor18High-Accuracy Heats of Formation for Alkane Oxidation: From Small to Large via the Automated CBH-ANL Method33Beyond independent error assumptions in large GNN atomistic models11First-principles, machine learning and symbolic regression modelling for organic molecule adsorption on two-dimensional CaO surface12Approaching Coupled Cluster Accuracy with Density Functional Theory Using the Generalized Connectivity-Based Hierarchy12Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning4Δ-Learning applied to coarse-grained homogeneous liquids1Enhancing the Search Performance of Bayesian Optimization by Creating Different Descriptor Datasets Using Density Functional Theory6Catalyst Energy Prediction with CatBERTa: Unveiling Feature Exploration Strategies through Large Language Models59Leveraging DFT and Molecular Fragmentation for Chemically Accurate pKa Prediction Using Machine Learning10Stepping-Stone CBH: Benchmark and Application of a Multilayered Isodesmic-Based Correction Scheme3The removal of elemental aluminium and its ions using some heteroatom-decorated graphitic carbonitride nanosheets: a DFT study2The possibility of using the Zn (II) Butadiyne-linked porphyrin nanoring for detection and adsorption of AsH3, NO2, H2O, SO2, CS2, CO, and CO2 gases5A theoretical study on adsorption and sensing of SO2, CS2, CO2, CH2O, H2O, C2H2, and CF3H air pollutant gases by B3S monolayer7A theoretical approach on adsorption and detection of Ni and Co ions by applying the B3S monolayer1Investigation on the possibility of using the C2N semiconductor segment for adsorption and detection of some chlorofluorocarbons; a DFT survey2Evaluation of Different DFT Functionals and Basis Sets for Predicting Proton Affinity and Gas‐Phase Basicity of Small Organic Molecule MALDI Matrices1CBH-BDC Enhanced Δ-ML for Predicting the Accurate Standard Enthalpy of Formation3SMILES Token Additivity Model with Interpretability and Generalizability for Fuel Property Predictions2ZnO-Based Photocatalysts: Synergistic Effects of Material Modifications and Machine Learning Optimization28Branched [4]Phenylene molecular unit for adsorption, removal, and selective sensing of toxic and volatile gases: Insights from DFT and TD-DFT analyses2Fragme∩t : An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation3Interpretable Machine-Learning-Assisted Design of Metal Oxide Catalysts by Decoding Key Descriptors for CO 2 -Assisted Propane Dehydrogenation5Predicting S 1 TDDFT Energies from ZINDO Calculations Using Message-Passing ΔML with Electronically Informed Descriptors5Discriminative adsorption and sensing of air pollutants for public health by graphdiyne: irreversible adsorption and selectivity toward NO2 revealed by DFT5Machine Learning for Accelerated Metal Oxide Thermochemical Predictions1Selective gas sensing and electronic response of Al-decorated phographene (Al-d-PHOG) nanosheets: Insights from DFT and electron density analyses0Reactive differentiation and electronic selectivity of gas molecules on BC10N: A first-principles investigation for advanced sensing platforms0Selective detection of atmospheric pollutant gases on a B2C3N nanosheet: A comprehensive DFT, electronic structure, and topological analysis of adsorption mechanisms0Oxidation-state-dependent adsorption of iron and copper on a B3S quantum-dot-like model: A DFT study0Synergistic Effects of Al-Doping on Chlorofluorocarbon Adsorption and Sensing Characteristics of B2N2 Nanosheets0

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