ReviewNature reviews. Chemistry2022
Extending machine learning beyond interatomic potentials for predicting molecular properties.
Review in Nature reviews. Chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 145 citations in OpenAlex.
- Resolving Chemically InequivalentJACS Au · 2026Article
- Methods for the establishment of enzymatic mechanisms - from QM to ML.Chemical science · 2026Review
- Cross-material catalyst discovery via deep learning.Nature materials · 2026Article
- Predicting Density and Elucidating the Thermodynamic Drivers of Viscosity in Carboxy-Functionalized Imidazolium Ionic Liquids.Molecules (Basel, Switzerland) · 2026Article
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces.ACS applied materials & interfaces · 2026Review
- Multi-scale molecular dynamics of perovskite quantum dots: from atomic fluctuations to predictive stability design.Journal of molecular modeling · 2026Review
- Amine-Appended Hyper-Crosslinked Polymers for Direct Air Capture of COACS sustainable chemistry & engineering · 2026Article
- Explainable artificial intelligence for materials discovery: application to catalysts for the HER and ORR.Chemical science · 2026Article
- Accurate and Generalizable Protein-Ligand Binding Affinity Prediction With Geometric Deep Learning.IEEE open journal of engineering in medicine and biology · 2026Article
- A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials.Scientific data · 2025Article
- Perspective on Theoretical Modeling of Soft Molecular Machines and Devices: A Fusion of Data-Driven Approaches with Traditional Computational Chemistry Algorithms.ChemistryOpen · 2025Article
- Comparative analysis of search approaches to discover donor molecules for organic solar cells.Digital discovery · 2025Article
- Using Deep Graph Neural Networks Improves Physics-Based Hydration Free Energy Predictions Even for Molecules Outside of the Training Set Distribution.The journal of physical chemistry. B · 2025Article
- Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry.Journal of chemical information and modeling · 2025Article
- Computational Methods for Modeling Lipid-Mediated Active Pharmaceutical Ingredient Delivery.Molecular pharmaceutics · 2025Review
- Interpolating numerically exact many-body wave functions for accelerated molecular dynamics.Nature communications · 2025Article
- Modelling ligand exchange in metal complexes with machine learning potentials.Faraday discussions · 2025Article
- Machine learning and data-driven methods in computational surface and interface science.npj computational materials · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference dataset that can be used to establish a relationship between a molecular structure and its chemical properties. This Review highlights developments in the use of ML to evaluate chemical properties such as partial atomic charges, dipole moments, spin and electron densities, and chemical bonding, as well as to obtain a reduced quantum-mechanical description. We overview several modern neural network architectures, their predictive capabilities, generality and transferability, and illustrate their applicability to various chemical properties. We emphasize that learned molecular representations resemble quantum-mechanical analogues, demonstrating the ability of the models to capture the underlying physics. We also discuss how ML models can describe non-local quantum effects. Finally, we conclude by compiling a list of available ML toolboxes, summarizing the unresolved challenges and presenting an outlook for future development. The observed trends demonstrate that this field is evolving towards physics-based models augmented by ML, which is accompanied by the development of new methods and the rapid growth of user-friendly ML frameworks for chemistry.
Identifiers
What OpenQuestion holds
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.