On representing chemical environments

Article Properties
Refrences
Title Journal Journal Categories Citations Publication Date
10.1093/oso/9780199765652.001.0001 2012
10.1017/CBO9780511721724 2004
10.1142/0270 1987
Gaussian Processes for Machine Learning 2007
Information Theory, Inference, and Learning Algorithms 2003
Citations
Title Journal Journal Categories Citations Publication Date
Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow Journal of Chemical Information and Modeling
  • Medicine: Therapeutics. Pharmacology
  • Science: Chemistry: General. Including alchemy
  • Science: Science (General): Cybernetics: Information theory
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Chemistry
 1 2024
Completeness of atomic structure representations

 APL Machine Learning
  • Science: Physics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 2024
Development of a machine learning finite-range nonlocal density functional

 The Journal of Chemical Physics
  • Science: Chemistry: Physical and theoretical chemistry
  • Science: Physics: Atomic physics. Constitution and properties of matter
  • Science: Chemistry: Physical and theoretical chemistry
  • Science: Chemistry
 2024
Interpretably learning the critical temperature of superconductors: Electron concentration and feature dimensionality reduction

 APL Materials
  • Technology: Chemical technology: Biotechnology
  • Science: Physics
  • Technology: Chemical technology
  • Science: Chemistry
  • Science: Physics
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
  • Technology: Chemical technology
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
 2024
The effect of machine learning predicted anharmonic frequencies on thermodynamic properties of fluid hydrogen fluoride

 The Journal of Chemical Physics
  • Science: Chemistry: Physical and theoretical chemistry
  • Science: Physics: Atomic physics. Constitution and properties of matter
  • Science: Chemistry: Physical and theoretical chemistry
  • Science: Chemistry
 2024
Citations Analysis
Category Category Repetition
Science: Chemistry192
Science: Chemistry: Physical and theoretical chemistry178
Science: Physics: Atomic physics. Constitution and properties of matter168
Technology: Chemical technology19
Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials15
Science: Physics11
Science: Chemistry: General. Including alchemy6
Science: Mathematics: Instruments and machines: Electronic computers. Computer science6
Medicine: Therapeutics. Pharmacology4
Science: Science (General): Cybernetics: Information theory4
Technology: Chemical technology: Biotechnology3
Medicine: Medicine (General): Computer applications to medicine. Medical informatics2
Science: Biology (General)2
Social Sciences: Industries. Land use. Labor: Special industries and trades: Energy industries. Energy policy. Fuel trade1
Science: Physics: Geophysics. Cosmic physics1
Science: Geology1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software1
Technology1
The category Science: Chemistry 192 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Structure-based sampling and self-correcting machine learning for accurate calculations of potential energy surfaces and vibrational levels and was published in 2017. The most recent citation comes from a 2024 study titled Interpretably learning the critical temperature of superconductors: Electron concentration and feature dimensionality reduction. This article reached its peak citation in 2023, with 53 citations. It has been cited in 23 different journals, 30% of which are open access. Among related journals, the The Journal of Chemical Physics cited this research the most, with 158 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year