Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Abstract

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross-validation over more than seven thousand small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.

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• Combining Hammett $\sigma$ constants for $\Delta$-machine learning and catalyst discovery
• Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies
• A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra
• Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations
• Hybrid localized graph kernel for machine learning energy-related properties of molecules and solids
• Rapid Exploration of Optimization Strategies on Advanced Architectures using TestSNAP and LAMMPS
• Physical machine learning outperforms "human learning" in Quantum Chemistry
• Surrogate-Based Black-Box Optimization Method for Costly Molecular Properties
• Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties
• Solution of inverse problem for Gross-Pitaevskii equation with artificial neural networks
• Efficient Long-Range Convolutions for Point Clouds
• Graph Information Bottleneck for Subgraph Recognition
• Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective
• Quantum Walk Inspired Neural Networks for Graph-Structured Data
• Accurate Prediction of Free Solvation Energy of Organic Molecules via Graph Attention Network and Message Passing Neural Network from Pairwise Atomistic Interactions
• Discovery of Novel Silicon Allotropes with Optimized Band Gaps to Enhance Solar Cell Efficiency through Evolutionary Algorithms and Machine Learning
• First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models
• Self-supervised Representations and Node Embedding Graph Neural Networks for Accurate and Multi-scale Analysis of Materials
• Reconstructing Kernel-based Machine Learning Force Fields with Super-linear Convergence
• Machine-Learned Potentials for Solvation Modeling
• Extending the definition of atomic basis sets to atoms with fractional nuclear charge
• Modeling Edge Features with Deep Bayesian Graph Networks
• GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
• Assessment of machine learning methods for state-to-state approaches
• Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation
• Prediction of Carbon Nanostructure Mechanical Properties and Role of Defects Using Machine Learning
• Predicting Kovats Retention Indices Using Graph Neural Networks
• The Homunculus Brain and Categorical Logic
• Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space
• Designing compact training sets for data-driven molecular property prediction
• Efficiency of neural-network state representations of one-dimensional quantum spin systems
• A universal neural network for learning phases and criticalities
• Prediction of Atomization Energies of Au13+ Clusters through the Machine Learning Approach
• Detecting Label Noise via Leave-One-Out Cross-Validation
• Using Slisemap to interpret physical data
• VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds
• Antisymmetry rules of response properties in certain chemical spaces
• Efficient Generalized Spherical CNNs
• Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
• Representing spherical tensors with scalar-based machine-learning models
• Incorporating electronic information into Machine Learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations
• The PubChemQC Project: a large chemical database from the first principle calculations
• Discriminative Learning of Similarity and Group Equivariant Representations
• Atomistic Global Optimization X: A Python package for optimization of atomistic structures
• Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
• Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY Chemical Space: High-Throughput Computation, Quantum Machine Learning, and Inverse Design
• Variational Integrator Graph Networks for Learning Energy Conserving Dynamical Systems
• Bosonic Random Walk Networks for Graph Learning
• Self-consistency error correction for accurate machine learning potentials from variational Monte Carlo
• Learning the gravitational force law and other analytic functions
• Encoder-Decoder Neural Networks in Interpretation of X-ray Spectra
• Neural network distillation of orbital dependent density functional theory
• Bond type restricted radial distribution functions for accurate machine learning prediction of atomization energies
• Graph Convolutional Neural Networks for (QM)ML/MM Molecular Dynamics Simulations
• Machine learning of electronic structure and atomistic properties from the external potential
• Extension of the Jordan-Wigner mapping to nonorthogonal spin orbitals for quantum computing application to valence bond approaches
• Reducing the Long Tail Losses in Scientific Emulations with Active Learning
• AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars
• Machine learning reveals orbital interaction in crystalline materials
• Bond Energies from a Diatomics-in-Molecules Neural Network
• Simplifying inverse material design problems for fixed lattices with alchemical chirality
• Pairwise interactions for Potential energy surfaces and Atomic forces with Deep Neural network
• Analysis of Atomistic Representations Using Weighted Skip-Connections
• Inverse Design of Conjugated Polymers from Computed Electronic Structure Properties: Model Chemistries of Polythiophenes
• Measuring the Similarity between Materials with an Emphasis on the Materials Distinctiveness
• Efficient Data Selection Methods for the Development of Machine Learned Potentials
• Using Clinical Drug Representations for Improving Mortality and Length of Stay Predictions
• Charge Transfer in Classical Molecular Dynamics Simulations of Met-enkephalin: Improving Traditional Force Field with Data Driven Models
• Active learning of potential-energy surfaces of weakly-bound complexes with regression-tree ensembles
• Fast Haar Transforms for Graph Neural Networks
• Chemical Bond-Based Representation of Materials
• Combining DFT with ML to study size specific interactions between metal clusters and adsorbates
• Trees and Islands -- Machine learning approach to nuclear physics
• Stacked Generalization Approach to Improve Prediction of Molecular Atomization Energies
• Elucidating atmospheric brown carbon -- Supplanting chemical intuition with exhaustive enumeration and machine learning
• Integer linear programming for unsupervised training set selection in molecular machine learning