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Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.Quantum chemistry structures and properties of 134 kilo molecules.Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space.Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations.Collective many-body van der Waals interactions in molecular systems.Ab initio molecular dynamics calculations of ion hydration free energies.Properties and reactivity of nucleic acids relevant to epigenomics, transcriptomics, and therapeutics.Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties.Toward transferable interatomic van der Waals interactions without electrons: the role of multipole electrostatics and many-body dispersion.Many Molecular Properties from One Kernel in Chemical Space.Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.Electronic spectra from TDDFT and machine learning in chemical space.Two- and three-body interatomic dispersion energy contributions to binding in molecules and solids.Variational optimization of effective atom centered potentials for molecular properties.Modeling of molecular atomization energies using machine learning.Quantum machine learning in chemical compound space.Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity.Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.Toward Quantitative Structure-Property Relationships for Charge Transfer Rates of Polycyclic Aromatic Hydrocarbons.Blind test of density-functional-based methods on intermolecular interaction energies.Path Integral Computation of Quantum Free Energy Differences Due to Alchemical Transformations Involving Mass and Potential.Predicting noncovalent interactions between aromatic biomolecules with London-dispersion-corrected DFT.Coarse-grained interaction potentials for polyaromatic hydrocarbons.Alchemical Predictions for Computational Catalysis: Potential and Limitations.Exploring dissociative water adsorption on isoelectronically BN doped graphene using alchemical derivatives.Enol tautomers of Watson-Crick base pair models are metastable because of nuclear quantum effects.Guiding ab initio calculations by alchemical derivatives.Alchemical derivatives of reaction energetics.Accurate ab initio energy gradients in chemical compound space.Alchemical Variations of Intermolecular Energies According to Molecular Grand-Canonical Ensemble Density Functional Theory.Molecular grand-canonical ensemble density functional theory and exploration of chemical space.Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning.Machine learning of molecular electronic properties in chemical compound spaceMachine learning meets volcano plots: computational discovery of cross-coupling catalystsAlchemical Normal Modes Unify Chemical SpaceFCHL revisited: Faster and more accurate quantum machine learningTorsional Potentials of Glyoxal, Oxalyl Halides, and Their Thiocarbonyl Derivatives: Challenges for Popular Density Functional ApproximationsBoosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams RevisitedAtoms in Molecules from Alchemical Perturbation Density Functional Theory
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O Anatole von Lilienfeld
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O Anatole von Lilienfeld
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O Anatole von Lilienfeld
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