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RESEARCH PAPER · Journal of Chemical Theory and Computation · 2018

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes

Gregor N. C. Simm, Markus Reiher

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Error-Controlled Exploration of Chemical Reaction Networks with Gaussian ProcessesOriginalDiagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method.8Comprehensive Analysis of the Neglect of Diatomic Differential Overlap Approximation33Exploration of Reaction Pathways and Chemical Transformation Networks229A Trajectory-Based Method to Explore Reaction Mechanisms43Mechanism Deduction from Noisy Chemical Reaction Networks52Boosting Quantum Machine Learning Models with a Multilevel Combination Technique: Pople Diagrams Revisited126A quantitative uncertainty metric controls error in neural network-driven chemical discovery240Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry57Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry113On Benchmarking of Automated Methods for Performing Exhaustive Reaction Path Search58autoCAS : A Program for Fully Automated Multiconfigurational Calculations104Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation90Predicting Adsorption Energies Using Multifidelity Data23Generalized potential games1Unexpected Rearrangement of Dilithiated Isoindoline-1,3-diols into 3-Aminoindan-1-ones viaN-Lithioaminoarylcarbenes: A Combined Synthetic and Computational Study11Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonome Entdeckung in den chemischen Wissenschaften, Teil II: Ausblick4Autonomous Discovery in the Chemical Sciences Part I: Progress317Autonomous Discovery in the Chemical Sciences Part II: Outlook245Gaussian Process-Based Refinement of Dispersion Corrections59Enhancing Automated Reaction Discovery with Boxed Molecular Dynamics in Energy Space5Automated exploration of the low-energy chemical space with fast quantum chemical methods2363Uncertainty quantification in materials modeling18Automated in Silico Design of Homogeneous Catalysts186Self-Parametrizing System-Focused Atomistic Models34Machine learning for interatomic potential models363Systematic microsolvation approach with a cluster‐continuum scheme and conformational sampling83Leveraging Thermochemistry Data to Build Accurate Microkinetic Models15The Exploration of Chemical Reaction Networks202Globally optimal catalytic fields for a Diels–Alder reaction28Machine learning the computational cost of quantum chemistry41Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis243Bayesian Optimization for Calibrating and Selecting Hybrid-Density Functional Models208Machine Learning in Chemistry77An adaptive design approach for defects distribution modeling in materials from first-principle calculations20Exchange Spin Coupling from Gaussian Process Regression27Ab initio machine learning in chemical compound space4Uncertainty quantification in classical molecular dynamics107Machine-learned potentials for next-generation matter simulations560An optimized deep convolutional neural network for yield prediction of Buchwald-Hartwig amination5Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning354Pairwise Difference Regression: A Machine Learning Meta-algorithm for Improved Prediction and Uncertainty Quantification in Chemical Search64Ab Initio Machine Learning in Chemical Compound Space195AutoMeKin2021 : An open‐source program for automated reaction discovery112Automated exploration of DNA-based structure self-assembly networks2Molecule‐Specific Uncertainty Quantification in Quantum Chemical Studies29Machine learning activation energies of chemical reactions83The transferability limits of static benchmarks11Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis65New Tools for Taming Complex Reaction Networks: The Unimolecular Decomposition of Indole Revisited31Heuristics and Uncertainty Quantification in Rational and Inverse Compound and Catalyst Design3Data-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 Catalysis18Chemoton 2.0: Autonomous Exploration of Chemical Reaction Networks86Improving the predictive power of microkinetic models via machine learning15Cheap Turns Superior: A Linear Regression-Based Correction Method to Reaction Energy from the DFT4Twin neural network regression22Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning13Exploring catalytic reaction networks with machine learning159Universal QM / MM approaches for general nanoscale applications57High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation27Automated Preparation of Nanoscopic Structures: Graph-Based Sequence Analysis, Mismatch Detection, and pH-Consistent Protonation with Uncertainty Estimates0AABBA: Atom–Atom Bond–Bond Bond–Atom Graph Kernel for Machine Learning on Molecules and Materials0Automated preparation of nanoscopic structures: Graph‐based sequence analysis, mismatch detection, and pH‐consistent protonation with uncertainty estimates3Machine Learned Material Simulation0Interoperable workflows by exchanging grid-based data between quantum-chemical program packages7A human-machine interface for automatic exploration of chemical reaction networks26Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning1Uncertainty-Aware First-Principles Exploration of Chemical Reaction Networks12SCINE—Software for chemical interaction networks20Nanoscale chemical reaction exploration with a quantum magnifying glass10AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning11Learning Inducing Points and Uncertainty on Molecular Data by Scalable Variational Gaussian Processes1Automated Microsolvation for Minimum Energy Path Construction in Solution10regAL: Python package for active learning of regression problems2Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations5An Accurate and Efficient Reaction Path Search with Iteratively Trained Neural Network Potential: Answering the Passerini Mechanism Controversy7Exploring the unknown: automated methods for finding novel and unexpected reaction pathways1Uncertainty Quantification for In Silico Chemistry13Catalyst Design for Sustainable Reactions: A Primer toward Knowledge-Driven Innovation0

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