ArticleScientific reports2017
Geometry Optimization with Machine Trained Topological Atoms.
Article in Scientific reports, 2017. 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.
- DFT-based evaluation of covalent organic frameworks for adsorption, optoelectronic, clean energy storage, and gas sensor applications.Journal of molecular modeling · 2025Article
- Unlocking the chemistry facilitated by enzymes that process nucleic acids using quantum mechanical and combined quantum mechanics-molecular mechanics techniques.Pure and applied chemistry. Chimie pure et appliquee · 2025Review
- Incorporating Noncovalent Interactions in Transfer Learning Gaussian Process Regression Models for Molecular Simulations.Journal of chemical theory and computation · 2024Article
- Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors.Nature communications · 2024Article
- Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials.Chemical science · 2023Article
- Choosing the right molecular machine learning potential.Chemical science · 2021Article
- Interacting Quantum Atoms-A Review.Molecules (Basel, Switzerland) · 2020Review
- A FFLUX Water Model: Flexible, Polarizable and with a Multipolar Description of Electrostatics.Journal of computational chemistry · 2020Article
- Atomic Partitioning of the MPn (n = 2, 3, 4) Dynamic Electron Correlation Energy by the Interacting Quantum Atoms Method: A Fast and Accurate Electrostatic Potential Integral Approach.Journal of computational chemistry · 2019Article
- Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns.Frontiers in chemistry · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The geometry optimization of a water molecule with a novel type of energy function called FFLUX is presented, which bypasses the traditional bonded potentials. Instead, topologically-partitioned atomic energies are trained by the machine learning method kriging to predict their IQA atomic energies for a previously unseen molecular geometry. Proof-of-concept that FFLUX's architecture is suitable for geometry optimization is rigorously demonstrated. It is found that accurate kriging models can optimize 2000 distorted geometries to within 0.28 kJ mol
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.