ArticleScience advances2019
A unified picture of the covalent bond within quantum-accurate force fields: From organic molecules to metallic complexes' reactivity.
Article in Science advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Penta- and Hexacoordinated Copper(II) Complexes with Azido and 4-amino-3,5-di-2-pyridyl-4H-1,2,4-triazole Ligands with Field-Induced Slow Magnetic Relaxation.ChemistryOpen · 2025Article
- A machine-learning framework for accelerating spin-lattice relaxation simulations.npj computational materials · 2025Article
- Machine-Learning-Assisted Construction of Ternary Convex Hull Diagrams.Journal of chemical information and modeling · 2024Article
- Quantum-accurate machine learning potentials for metal-organic frameworks using temperature driven active learning.npj computational materials · 2024Article
- Stiffness and Atomic-Scale Friction in Superlubricant MoSThe journal of physical chemistry letters · 2023Article
- Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations.Nature reviews. Chemistry · 2022Review
- Toward exact predictions of spin-phonon relaxation times: An ab initio implementation of open quantum systems theory.Science advances · 2022Article
- "Dividing and Conquering" and "Caching" in Molecular Modeling.International journal of molecular sciences · 2021Review
- Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems.Journal of chemical theory and computation · 2020Review
- Toward the Prediction of Multi-Spin State Charges of a Heme Model by Random Forest Regression.Frontiers in chemistry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational studies of chemical processes taking place over extended size and time scales are inaccessible by electronic structure theories and can be tackled only by atomistic models such as force fields. These have evolved over the years to describe the most diverse systems. However, as we improve the performance of a force field for a particular physical/chemical situation, we are also moving away from a unified description. Here, we demonstrate that a unified picture of the covalent bond is achievable within the framework of machine learning-based force fields. Ridge regression, together with a representation of the atomic environment in terms of bispectrum components, can be used to map a general potential energy surface for molecular systems at chemical accuracy. This protocol sets the ground for the generation of an accurate and universal class of potentials for both organic and organometallic compounds with no specific assumptions on the chemistry involved.
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.