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RESEARCH PAPER · Journal of Chemical Theory and Computation · 2019

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

Chenru Duan, Jon Paul Janet, Fang Liu, Aditya Nandy, Heather J. Kulik

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Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning ModelsOriginalCompressing physical properties of atomic species for improving predictive chemistry29A quantitative uncertainty metric controls error in neural network-driven chemical discovery240Data Mining the C−C Cross‐Coupling Genome21Enumeration of de novo inorganic complexes for chemical discovery and machine learning42Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation90Making machine learning a useful tool in the accelerated discovery of transition metal complexes47In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling75Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress317Dataset’s chemical diversity limits the generalizability of machine learning predictions127Machine Learning for Catalysis Informatics: Recent Applications and Prospects613Quantum Chemistry Meets Machine Learning11Large-scale comparison of 3d and 4d transition metal complexes illuminates the reduced effect of exchange on second-row spin-state energetics41Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex178Quantum chemistry assisted by machine learning33Automated in Silico Design of Homogeneous Catalysts186Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules39Quantum Chemistry in the Age of Machine Learning417Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization198Machine learning the computational cost of quantum chemistry41Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions80Machine Learning in Chemistry77Big-Data Science in Porous Materials: Materials Genomics and Machine Learning599Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics47Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost34Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening58Exchange Spin Coupling from Gaussian Process Regression27Influence of Electronic Structure Modeling and Junction Structure on First-Principles Chiral Induced Spin Selectivity59tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes138The Role of Machine Learning in the Understanding and Design of Materials433Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks22Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts51Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles40Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design55Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design2What's Left for a Computational Chemist To Do in the Age of Machine Learning?10Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery55Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354A machine learning approach to predict L-edge x-ray absorption spectra of light transition metal ion compounds3Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States26Time Frequency Representations and Deep Convolutional Neural Networks: A Recipe for Molecular Properties Prediction3Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks198Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost24Modeling the roles of rigidity and dopants in single-atom methane-to-methanol catalysts26A robust solver for wavefunction-based density functional theory calculations*2Representations and strategies for transferable machine learning improve model performance in chemical discovery19Roadmap on Machine learning in electronic structure180MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks135Machine Learning for the Discovery, Design, and Engineering of Materials66New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts74Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks0Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis18Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands18Machine learning: An overview0Active Learning Exploration of Transition-Metal Complexes to Discover Method-Insensitive and Synthetically Accessible Chromophores37A transferable recommender approach for selecting the best density functional approximations in chemical discovery31Deep learning metal complex properties with natural quantum graphs59High-throughput computational workflow for ligand discovery in catalysis with the CSD9Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models4Machine learning methods in photochemistry and photophysics10Computational Discovery of Stable Metal–Organic Frameworks for Methane-to-Methanol Catalysis34Deep Learning-Based Modelling of Complex Photonic Crystal Slow Light Waveguides12Stability Trends in disubstituted Cobaltocenium Based on the Analysis of the Machine Learning Models1Artificial intelligence in catalysis2Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning27Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances78Large-scale comparison of Fe and Ru polyolefin C–H activation catalysts3Reducing training data needs with minimal multilevel machine learning (M3L)18Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning1TS‐tools: Rapid and automated localization of transition states based on a textual reaction SMILES input17AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning11Ligand Many-Body Expansion as a General Approach for Accelerating Transition Metal Complex Discovery6Many-body expansion based machine learning models for octahedral transition metal complexes2Bayesian Analysis Reveals the Key to Extracting Pair Potentials from Neutron Scattering Data6Computational Exploration of Codoped Fe and Ru Single-Atom Catalysts for the Oxygen Reduction Reaction14Machine learning for sustainable development in electronics1Machine learning approach to predict L -edge x-ray absorption spectra of light transition metal ion compounds5Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models23A Multitask Learning Model for Predicting Various Types of Metal–Organic Framework Stability7Representative Random Sampling of Chemical Space1The role of atomic-level understanding in optimizing lithium titanate oxide based anodes for lithium-ion batteries8Accelerating quasiparticle physics: a review on the synergy of machine learning and density functional theory for excitons, polaritons, and plasmons0A generic model for mixture adsorption property prediction (MAPP): Toward efficient discovery of metal-organic frameworks for CO2 capture1High-Throughput Discovery of Conformation-Switching Mechanophores with Enhanced Reactivity and Stability0ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification across the Periodic Table0

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  17. Synergistic Approach of Ultrafast Spectroscopy and Molecular Simulations in the Characterization of Intramolecular Charge Transfer in Push-Pull Molecules

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  19. Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

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  20. Machine learning the computational cost of quantum chemistry

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  21. Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

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  24. Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics

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  42. Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks

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