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RESEARCH PAPER · ChemRxiv · 2018

Outsmarting Quantum Chemistry Through Transfer Learning

Justin S Smith, Benjamin T. Nebgen, R.I. Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton M. Barros, Sergei Tretiak, Olexandr Isayev, Adrián E. Roitberg

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Outsmarting Quantum Chemistry Through Transfer LearningOriginalPreface1Preface1Preface1Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)32Towards exact molecular dynamics simulations with machine-learned force fields700Transforming Computational Drug Discovery with Machine Learning and AI107Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited126A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules113Machine learning for molecular dynamics with strongly correlated electrons39Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals1525Deep Learning in Chemistry561Machine learning methods for track classification in the AT-TPC32Accurate Thermochemistry with Small Data Sets: A Bond Additivity Correction and Transfer Learning Approach107Message-passing neural networks for high-throughput polymer screening115Recent advances and applications of machine learning in solid-state materials science2607Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations8Understanding ML Driven HPC: Applications and Infrastructure12Understanding ML driven HPC: Applications and Infrastructure2Interactions Between Two Many‐Atom Systems0Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening108Machine Learning for Molecular Simulation906Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces58An Accurate Free Energy Method for Solvation of Organic Compounds and Binding to Proteins0Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms35Isolated 2-hydroxypyrene and its dimer: a frequency- and time-resolved spectroscopic study4Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials32Analytical Model of Electron Density and Its Machine Learning Inference33A machine learning-based representation of the electron density in biomacromolecules0Analytical gradients for molecular-orbital-based machine learning13Transfer Learning to CCSD(T): Accurate Anharmonic Frequencies from Machine Learning Models61Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing29Performance of small basis set Hartree–Fock methods for modeling non-covalent interactions7Transfer Learned Potential Energy Surfaces: Accurate Anharmonic Vibrational Dynamics and Dissociation Energies for the Formic Acid Monomer and Dimer41Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential88Using Deep 1D Convolutional Grated Recurrent Unit Neural Network to Optimize Quantum Molecular Properties and Predict Intramolecular Coupling Constants of Molecules of Potential Health Medications and Other Generic Molecules8Accurate Binding Free Energy Method from End-State MD Simulations55Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning18Machine Learning Diffusion Monte Carlo Energies18Neural network in food analytics51The performance of ANI‐ML potentials for ligand‐n( H2O ) interaction energies and estimation of hydration free energies from end‐point MD simulations7Enhancing the Performance of Global Optimization of Platinum Cluster Structures by Transfer Learning in a Deep Neural Network19Building a DFT+U machine learning interatomic potential for uranium dioxide19Transfer learning graph representations of molecules for pKa, 13 C-NMR, and solubility10Benchmarking ANI potentials as a rescoring function and screening FDA drugs for SARS-CoV-2 Mpro7Efficient high-throughput method utilizing neural network potentials to calculate interaction energies, validated by clean transfer experiment of CVD graphene with polymer mixtures5Recurrent Neural Network/Machine Learning Predictions of Reactive Channels in H+ + C2H4 at E Lab = 30 eV : A Prototype of Ion Cancer Therapy Reactions4Benchmarking Universal Machine Learning Force Fields with Hydrogen-Bonding Cooperativity3

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  1. Preface

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  2. Preface

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  3. Preface

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  4. Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT)

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  5. Towards exact molecular dynamics simulations with machine-learned force fields

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  6. Transforming Computational Drug Discovery with Machine Learning and AI

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  7. Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited

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  8. A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules

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  9. Machine learning for molecular dynamics with strongly correlated electrons

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  10. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

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  13. Accurate Thermochemistry with Small Data Sets: A Bond Additivity Correction and Transfer Learning Approach

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  14. Message-passing neural networks for high-throughput polymer screening

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  16. Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations

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  17. Understanding ML Driven HPC: Applications and Infrastructure

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  18. Understanding ML driven HPC: Applications and Infrastructure

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  19. Interactions Between Two Many‐Atom Systems

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  20. Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening

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  21. Machine Learning for Molecular Simulation

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  22. Reactive dynamics and spectroscopy of hydrogen transfer from neural network-based reactive potential energy surfaces

    New Journal of Physics · 2020-03-20

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  23. An Accurate Free Energy Method for Solvation of Organic Compounds and Binding to Proteins

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  24. Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms

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  25. Isolated 2-hydroxypyrene and its dimer: a frequency- and time-resolved spectroscopic study

    New Journal of Chemistry · 2020-06-16

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  26. Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials

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  27. Analytical Model of Electron Density and Its Machine Learning Inference

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  29. Analytical gradients for molecular-orbital-based machine learning

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  33. Transfer Learned Potential Energy Surfaces: Accurate Anharmonic Vibrational Dynamics and Dissociation Energies for the Formic Acid Monomer and Dimer

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  34. Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential

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