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RESEARCH PAPER · Angewandte Chemie International Edition · 2017

Quantum Machine Learning in Chemical Compound Space

O. Anatole von Lilienfeld

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Quantum Machine Learning in Chemical Compound SpaceOriginalAccelerating CALYPSO structure prediction by data-driven learning of a potential energy surface112Alchemical normal modes unify chemical space0Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements140Machine learning for predicting product distributions in catalytic regioselective reactions41Machine learning meets volcano plots: computational discovery of cross-coupling catalysts209Quantum Machine Learning in Chemistry and Materials19Representing molecular and materials data for unsupervised machine learning27Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database13Combining theory and experiment in lithium–sulfur batteries: Current progress and future perspectives446Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry98NOMAD: The FAIR concept for big data-driven materials science453Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)32Diversification of Device Platforms by Molecular Layers: Hybrid Sensing Platforms, Monolayer Doping, and Modeling11Quantum machine learning for electronic structure calculations145Combining electronic and structural features in machine learning models to predict organic solar cells properties177Alchemical Normal Modes Unify Chemical Space32Read between the Molecules: Computational Insights into Organic Semiconductors105Searching for Hidden Descriptors in the Metal–Ligand Bond through Statistical Analysis of Density Functional Theory (DFT) Results29Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited126Machine learning enables long time scale molecular photodynamics simulations214Making Machine Learning Work in Chemistry: Methodological Innovation, Software Development, and Application Studies3Recent Advancements in Computing Reliable Binding Free Energies in Drug Discovery Projects3Fast and Accurate Uncertainty Estimation in Chemical Machine Learning160Operators in quantum machine learning: Response properties in chemical space116Computational Ligand Descriptors for Catalyst Design406Machine learning modeling of Wigner intracule functionals for two electrons in one-dimension3Molecular shape as a (useful) bias in chemistry1Unsupervised machine learning in atomistic simulations, between predictions and understanding172Machine Learning the Voltage of Electrode Materials in Metal-Ion Batteries199Report of the Basic Energy Sciences Roundtable on Chemical Upcycling of Polymers72Chemical diversity in molecular orbital energy predictions with kernel ridge regression78Chemical diversity in molecular orbital energy predictions with kernel ridge regression70Deep Learning in Chemistry561Increasing the Potential of the Auristatin Cancer-Drug Family by Shifting the Conformational Equilibrium14A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space8A Physics-Infused Deep Learning Model for the Prediction of Refractive Indices and Its Use for the Large-Scale Screening of Organic Compound Space10Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry6Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry2Embedding domain knowledge for machine learning of complex material systems71Advancing Drug Discovery via Artificial Intelligence649Physical machine learning outperforms "human learning" in Quantum Chemistry3Making machine learning a useful tool in the accelerated discovery of transition metal complexes47Bio-inspired weighed quantum particle swarm optimization and smooth support vector machine ensembles for identification of abnormalities in medical data121Controlled exploration of chemical space by machine learning of coarse-grained representations21Machine learning models for hydrogen bond donor and acceptor strengths using large and diverse training data generated by first-principles interaction free energies32Atoms in Molecules from Alchemical Perturbation Density Functional Theory26Thinking Globally, Acting Locally: On the Issue of Training Set Imbalance and the Case for Local Machine Learning Models in Chemistry12Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part B. The March of Simulation, for Better or Worse27Simulation vs. Understanding: A Tension, in Quantum Chemistry and Beyond. Part B. The March of Simulation, for Better or Worse5Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions476Machine Learning in Nanoscience: Big Data at Small Scales229Role of Chemical Dynamics Simulations in Mass Spectrometry Studies of Collision-Induced Dissociation and Collisions of Biological Ions with Organic Surfaces40Machine Learning at the Atomic Scale6Operator Quantum Machine Learning: Navigating the Chemical Space of Response Properties11Quantum Chemistry Meets Machine Learning11Characterizing the Metal–Ligand Bond Strength via Vibrational Spectroscopy: The Metal–Ligand Electronic Parameter (MLEP)18Machine Learning: Quantum vs Classical211Molecular dynamics based descriptors for predicting supramolecular gelation46Quantum chemistry assisted by machine learning33Quantum Machine Learning in Chemistry and Materials18Quantum Machine Learning with Response Operators in Chemical Compound Space5Recent advances of computational chemistry in organic solar cell research99On-the-fly Prediction of Protein Hydration Densities and Free Energies using Deep Learning1Catalytic materials and chemistry development using a synergistic combination of machine learning and ab initio methods52FCHL revisited: Faster and more accurate quantum machine learning313The Roles of the NLRP3 Inflammasome in Neurodegenerative and Metabolic Diseases and in Relevant Advanced Therapeutic Interventions84Quantum Chemistry in the Age of Machine Learning417Noncovalent Quantum Machine Learning Corrections to Density Functionals32Predictions of High-Order Electric Properties of Molecules: Can We Benefit from Machine Learning?15Machine learning the computational cost of quantum chemistry41Eigen component analysis: A quantum theory incorporated machine learning technique to find linearly maximum separable components0Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids59Neural networks and kernel ridge regression for excited states dynamics of CH2NH 2+ : From single-state to multi-state representations and multi-property machine learning models62Recent developments in the general atomic and molecular electronic structure system1443Machine learning models of the energy curvature vs particle number for optimal tuning of long-range corrected functionals13Hierarchical machine learning of potential energy surfaces110Exploring chemical compound space with quantum-based machine learning369Machine learning and excited-state molecular dynamics62QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules112Wasserstein metric for improved quantum machine learning with adjacency matrix representations17An adaptive design approach for defects distribution modeling in materials from first-principle calculations20Hydration free energies from kernel-based machine learning: Compound-database bias35Critical Benchmarking of the G4(MP2) Model, the Correlation Consistent Composite Approach and Popular Density Functional Approximations on a Probabilistically Pruned Benchmark Dataset of Formation Enthalpies1Effective Molecular Descriptors for Chemical Accuracy at DFT Cost: Fragmentation, Error-Cancellation, and Machine Learning40Thousands of reactants and transition states for competing E2 and S N 2 reactions50Accurate Many-Body Repulsive Potentials for Density-Functional Tight Binding from Deep Tensor Neural Networks95Symmetrical Graph Neural Network for Quantum Chemistry with Dual Real and Momenta Space33Towards chemical accuracy for alchemical free energy calculations with hybrid physics-based machine learning / molecular mechanics potentials96Machine Learning for Absorption Cross Sections77Simplifying inverse material design problems for fixed lattices with alchemical chirality0A Gaussian process regression adaptive density guided approach for potential energy surface construction32Computational Prediction of Novel Ice Phases: A Perspective29Dictionary of 140k GDB and ZINC derived AMONs3A DFT/machine‐learning hybrid method for the prediction of 3 J HCCH couplings7Machine learning corrected quantum dynamics calculations25Machine Learning Corrected Quantum Dynamics Calculations15The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal Complexes11Machine learning corrected alchemical perturbation density functional theory for catalysis applications19Quantum machine learning using atom-in-molecule-based fragments selected on the fly204Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery106On the role of gradients for machine learning of molecular energies and forces137Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet38End-to-End Differentiable Molecular Mechanics Force Field Construction7Incompleteness of Atomic Structure Representations197Effects of perturbation order and basis set on alchemical predictions17Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks97Machine learning in chemical reaction space183Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies33tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes138Metric Transforms and Low Rank Matrices via Representation Theory of the Real Hyperrectangle1PySurf: A Framework for Database Accelerated Direct Dynamics18Computational compound screening of biomolecules and soft materials by molecular simulations0Mean-field density matrix decompositions14Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts51Ab initio machine learning in chemical compound space4TorsionNet: A Deep Neural Network to Rapidly Predict Small Molecule Torsion Energy Profiles with the Accuracy of Quantum Mechanics0Eutectics: formation, properties, and applications351High-throughput virtual screening for organic electronics: a comparative study of alternative strategies68Troubleshooting unstable molecules in chemical space11Digital Transformation in Materials Science: A Paradigm Change in Material's Development72Tackling the Challenge of a Huge Materials Science Search Space with Quantum‐Inspired Annealing28Pure non-local machine-learned density functional theory for electron correlation74Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design55Critical benchmarking of popular composite thermochemistry models and density functional approximations on a probabilistically pruned benchmark dataset of formation enthalpies18In Silico Tandem Mass Spectrometer: an Analytical and Fundamental Tool9QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules142Advances in De Novo Drug Design: From Conventional to Machine Learning Methods353A hybrid classical-quantum approach for multi-class classification70Perspectives in Dye Chemistry: A Rational Approach toward Functional Materials by Understanding the Aggregate State271Rapid enthalpy prediction of transition states using molecular graph convolutional network10QPoweredTarget2DeNovoDrugPropMax : a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest1From quantum similarity measures to quantum analogy functors: tools for QShAR, quantitative shape-activity relations5Hybrid localized graph kernel for machine learning energy‐related properties of molecules and solids1Simplifying inverse materials design problems for fixed lattices with alchemical chirality19Graph neural networks in TensorFlow-Keras with RaggedTensor representation (kgcnn)24Perspective on integrating machine learning into computational chemistry and materials science203Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems840PyRMD: A New Fully Automated AI-Powered Ligand-Based Virtual Screening Tool75Impact of the Characteristics of Quantum Chemical Databases on Machine Learning Prediction of Tautomerization Energies20Machine learning based energy-free structure predictions of molecules, transition states, and solids98Quantum chemical descriptors in quantitative structure–activity relationship models and their applications103Active Learning Configuration Interaction for Excited States Calculations of Polycyclic Aromatic Hydrocarbons0Ab Initio Machine Learning in Chemical Compound Space195Teaching free energy calculations to learn from experimental data13Solving the Schrödinger equation using program synthesis3Unsupervised Machine Learning Neural Gas Algorithm for Accurate Evaluations of the Hessian Matrix in Molecular Dynamics16Trendbericht Theoretische Chemie 1/2: Lichtgetriebene Reaktionen unter der Theorielupe0Data-efficient iterative training of Gaussian approximation potentials: Application to surface structure determination of rutile IrO2 and RuO235Active Learning Configuration Interaction for Excited-State Calculations of Polycyclic Aromatic Hydrocarbons33Toward Precise n-Type Doping Control in MOVPE-Grown β-Ga2O3 Thin Films by Deep-Learning Approach17

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  2. Alchemical normal modes unify chemical space

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  5. Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

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  13. Diversification of Device Platforms by Molecular Layers: Hybrid Sensing Platforms, Monolayer Doping, and Modeling

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