Neural Network Potentials for Chemistry: Concepts, Applications and Prospects

Abstract

Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on larger scale.

References in corpus (22)

• ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
• Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
• Machine learning for molecular simulation
• Machine Learning Unifies the Modelling of Materials and Molecules
• Towards Explainable Artificial Intelligence
• ANI-1: A data set of 20M off-equilibrium DFT calculations for organic molecules
• Understanding molecular representations in machine learning: The role of uniqueness and target similarity
• Deep Potentials for Materials Science
• Unsupervised machine learning in atomistic simulations, between predictions and understanding
• Training neural nets to learn reactive potential energy surfaces using interactive quantum chemistry in virtual reality
• Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials
• Bayesian machine learning for quantum molecular dynamics
• The Ecological Impact of High-performance Computing in Astrophysics
• Structure, Organization and Heterogeneity of Water-Containing Deep Eutectic Solvents
• BuRNN: Buffer Region Neural Network Approach for Polarizable-Embedding Neural Network / Molecular Mechanics Simulations
• Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
• Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
• The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials
• Prediction uncertainty validation for computational chemists
• Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties
• A Machine Learning Approach for Rate Constants III: Application to the Cl(${}^2$P) + CH${}_4$ $\to$ CH${}_3$ + HCl Reaction
• Rate Constants for Fine-Structure Excitations in O-H Collisions with Error Bars Obtained by Machine Learning

Cited by in corpus (11)

• Lifelong Machine Learning Potentials
• ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
• PhysNet Meets CHARMM: A Framework for Routine Machine Learning / Molecular Mechanics Simulations
• Transfer-Learned Potential Energy Surfaces: Towards Microsecond-Scale Molecular Dynamics Simulations in the Gas Phase at CCSD(T) Quality
• CoRe Optimizer: An All-in-One Solution for Machine Learning
• Aqueous Solution Chemistry In Silico and the Role of Data Driven Approaches
• Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials
• Hierarchical quantum embedding by machine learning for large molecular assemblies
• Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
• Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations
• A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning