ArticleChemical reviews2021
Gaussian Process Regression for Materials and Molecules.
Article in Chemical reviews, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 221 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
221 citing papers in PubMed.
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Thermal conductivity predictions with foundation atomistic models.Nature communications · 2026Article
- Data-Driven Serendipitous Discovery of a Self-Organized Nanostructure for Enhanced Pinning in Superconducting Films.ACS nano · 2026Article
- Modeling dual-range atomic interactions with physicochemical principles for molecular force fields.Bioinformatics (Oxford, England) · 2026Article
- URVAvis: A Graphical User Interface for the Visual Mechanistic Analysis of Chemical Reactions Based on the Unified Reaction Valley Approach.The journal of physical chemistry. A · 2026Article
- Membrane remodeling by the collective action of caveolin-1.Nature communications · 2026Article
- A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches.ACS physical chemistry Au · 2026Article
- Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions.ACS omega · 2026Article
- Nuclear Gradients from Auxiliary-Field Quantum Monte Carlo and Their Applications in ML-Driven Geometry Optimization and Transition State Search.Journal of chemical theory and computation · 2026Article
- Multi-Scale Structural Regulation of Boron-Doped Diamond via Doping, Modification, and Annealing for Water Pollutant Sensing.Nanomaterials (Basel, Switzerland) · 2026Review
- Noise-Aware Machine Learning Accelerates Development of High-Latent-Heat Cu-Al-Ni Shape Memory Alloys for Thermal Management.Materials (Basel, Switzerland) · 2026Article
- Validation ofACS central science · 2026Article
- PPO-GPR: A Custom Proximal Policy Optimization Tool for Active Reinforcement Learning.ACS engineering Au · 2026Article
- Synthetic Expansion of Blood Dielectric Spectra at Microwave Frequencies Using Data-Driven Methods.Sensors (Basel, Switzerland) · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- Efficient preparation of size-controlled sodium alginate microspheresRSC advances · 2026Article
- Learning Electronic Polarization in Molecular Systems: Vibrational Spectroscopy of Ethanol-Water Mixtures.Journal of chemical information and modeling · 2026Article
- Nonlinear dimensionality reduction and Bayesian optimization for accelerating design of materials.Scientific reports · 2026Article
- Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
161 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.
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.