ArticleScience advances2017
Machine learning unifies the modeling of materials and molecules.
Article in Science advances, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers.
What it found
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Who cites it
132 citing papers in PubMed.
- Soft Computing Approaches toward Density-Based Analysis of Many-Electron Systems.ACS physical chemistry Au · 2026Review
- When is nanoconfined water different from interfacial water?Faraday discussions · 2026Article
- Utility-Scale Quantum Computational Chemistry.The journal of physical chemistry letters · 2026Review
- Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.Journal of the American Chemical Society · 2026Article
- Quantifying Gibbs measures of disordered crystals up to the solid-liquid phase transition.PNAS nexus · 2026Article
- Narrowing down the cause of the hard-sphere nucleation discrepancy: The free energy of precritical nuclei is consistent with predictions.Science advances · 2026Article
- Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Enhanced Representation-Based Sampling for the Efficient Generation of Data Sets for Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Quantum vibrational spectroscopy with classical trajectories.Chemical science · 2026Review
- The CP2K Program Package Made Simple.The journal of physical chemistry. B · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water.Journal of chemical theory and computation · 2025Article
- Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations.Journal of chemical theory and computation · 2025Article
- Hierarchical Quantum Embedding by Machine Learning for Large Molecular Assemblies.Journal of chemical theory and computation · 2025Article
- Machine Learning Force Fields in Electrochemistry: From Fundamentals to Applications.ACS nano · 2025Review
- Probing Rate-Dependent Liquid Shear Viscosity Using Combined Machine Learning and Nonequilibrium Molecular Dynamics.Journal of chemical theory and computation · 2025Article
- Automating the Analysis of Substrate Reactivity through Environment Interaction Mapping.Journal of chemical information and modeling · 2025Article
- Neural Network Potential with Multiresolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution.Journal of the American Chemical Society · 2025Article
- Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.Chemical science · 2025Article
- ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials.Digital discovery · 2025Article
72 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provide new insight into the potential energy surface of materials and molecules.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.