Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Properties of Transition Metals02:58

Properties of Transition Metals

26.5K
Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
26.5K
Colors and Magnetism03:02

Colors and Magnetism

12.0K
Color in Coordination Complexes
When atoms or molecules absorb light at the proper frequency, their electrons are excited to higher-energy orbitals. For many main group atoms and molecules, the absorbed photons are in the ultraviolet range of the electromagnetic spectrum, which cannot be detected by the human eye. For coordination compounds, the energy difference between the d orbitals often allows photons in the visible range to be absorbed and emitted, which is seen as colors by the human...
12.0K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

27.0K
Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
27.0K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.6K
VSEPR Theory for Determination of Electron Pair Geometries
34.6K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

71
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
71
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
88

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The BOS-Lig Data Set: Accurate Ligand Charges from a Consensus Approach for 66,810 Experimentally Synthesized Ligands.

Journal of chemical information and modeling·2026
Same author

Side-Chain-Based Cross-Linking of Amorphous Iono-Electronic Conductive Polymers for Thermo-Chemical Stability in Electrochemical Devices.

ACS applied materials & interfaces·2026
Same author

High-Throughput Discovery of Conformation-Switching Mechanophores with Enhanced Reactivity and Stability.

Inorganic chemistry·2026
Same author

Mechanophore cross-linking enhances ballistic energy dissipation of polymers.

Nature·2026
Same author

QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures.

Journal of chemical information and modeling·2026
Same author

Mammalian-like steroidogenesis in plants gives rise to endocrine-mimetic cardenolides.

Science advances·2026

Related Experiment Video

Updated: Aug 8, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning

Yael Cytter1, Aditya Nandy1,2, Chenru Duan1,2

  • 1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Physical Chemistry Chemical Physics : PCCP
|March 6, 2023
PubMed
Summary

Density functional approximations (DFAs) used in virtual screening have inaccuracies due to energy curvature. Machine learning models predict curvature, identifying better DFAs for targeted optical gaps in transition metal complexes.

More Related Videos

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
14:44

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

Published on: December 16, 2013

9.7K
Line Shape Analysis of Dynamic NMR Spectra for Characterizing Coordination Sphere Rearrangements at a Chiral Rhenium Polyhydride Complex
10:52

Line Shape Analysis of Dynamic NMR Spectra for Characterizing Coordination Sphere Rearrangements at a Chiral Rhenium Polyhydride Complex

Published on: July 27, 2022

2.8K

Related Experiment Videos

Last Updated: Aug 8, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR
14:44

Structure and Coordination Determination of Peptide-metal Complexes Using 1D and 2D 1H NMR

Published on: December 16, 2013

9.7K
Line Shape Analysis of Dynamic NMR Spectra for Characterizing Coordination Sphere Rearrangements at a Chiral Rhenium Polyhydride Complex
10:52

Line Shape Analysis of Dynamic NMR Spectra for Characterizing Coordination Sphere Rearrangements at a Chiral Rhenium Polyhydride Complex

Published on: July 27, 2022

2.8K

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Quantum Chemistry

Background:

  • Virtual high-throughput screening (VHTS) and machine learning (ML) methods using density functional theory (DFT) are limited by inaccuracies in density functional approximations (DFAs).
  • These inaccuracies often stem from the lack of derivative discontinuity, causing energy curvature upon electron addition or removal.

Purpose of the Study:

  • To analyze and predict the curvature of energy with respect to electron number for various DFAs.
  • To identify DFAs that minimize curvature for transition metal complexes, enabling more accurate VHTS and ML applications.

Main Methods:

  • Computed and analyzed average curvature for 23 DFAs across different rungs of "Jacob's ladder" using a dataset of ~1000 transition metal complexes.
  • Trained artificial neural networks (ANNs) to predict curvature and frontier orbital energies for each DFA.
  • Interpreted DFA curvature differences using ML model analysis, focusing on the role of Hartree-Fock exchange and spin.

Main Results:

  • Observed expected dependence of curvature on Hartree-Fock exchange, but limited correlation between different DFA rungs.
  • Found spin significantly impacts curvature in range-separated and double hybrid functionals compared to semi-local ones.
  • Identified DFAs yielding near-zero curvature with low uncertainty for transition metal complexes over 187.2k hypothetical compounds using ANNs.

Conclusions:

  • Curvature analysis and ML prediction offer a pathway to select optimal DFAs for VHTS and ML in computational materials science.
  • The findings provide a method to accelerate the screening of transition metal complexes with desired optical gaps by minimizing DFA-induced inaccuracies.