Related Concept Videos
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
64
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...
64
02:31Habitat Fragmentation
17.7K
Habitat fragmentation describes the division of a more extensive, continuous habitat into smaller, discontinuous areas. Human activities such as land conversion, as well as slower geological processes leading to changes in the physical environment, are the two leading causes of habitat fragmentation. The fragmentation process typically follows the same steps: perforation, dissection, fragmentation, shrinkage, and attrition.
17.7K
00:59Range
11.7K
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
11.7K
00:54Distribution and Dispersion
21.9K
To understand intra-specific interactions in populations, scientists measure the spatial arrangement of species individuals. This geographic arrangement is known as the species distribution or dispersion. Highly territorial species exhibit a uniform distribution pattern, in which individuals are spaced at relatively equal distances from one another. Species that are highly tied to particular resources, such as food or shelter, tend to concentrate around those resources, and thus exhibit a...
21.9K
02:29Hybrid Zones
17.1K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
17.1K
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
You might also read
Related Articles
Articles linked to this work by shared authors, journal, and citation graph.
Sort by
Same author
HDAC10 promotes Th17 differentiation and IL-17A-driven neutrophilic airway inflammation in severe asthma.
Cell reports·2026
Same author
Accelerating geometry optimization <i>via</i> Grassmann-DIIS extrapolation.
Physical chemistry chemical physics : PCCP·2026
Same author
Ribosomal protein L8 promotes melanoma progression by regulating the cell cycle and metastasis.
Acta biochimica et biophysica Sinica·2026
Related Experiment Video
Updated: Jul 22, 2025
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
3.4K
Accurate prediction of global-density-dependent range-separation parameters based on machine learning.
Corentin Villot1, Tong Huang1, Ka Un Lao1
1Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284, USA.
The Journal of Chemical Physics
|July 24, 2023
Summary
A new XGBoost model accurately predicts the range-separation parameter for long-range corrected functionals (LRC-ωPBE), bypassing complex calculations. This machine learning approach enables rapid, efficient predictions for various chemical systems.
More Related Videos
12:26Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
Published on: October 11, 2016
13.4K
08:47Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
Published on: February 9, 2024
1.5K
Area of Science:
- Computational Chemistry
- Machine Learning in Quantum Mechanics
- Density Functional Theory
Background:
- Accurate prediction of electronic properties requires precise density functional approximations.
- Long-Range Corrected (LRC) functionals, like LRC-ωPBE, improve accuracy but often need system-specific parameter tuning.
- Determining the optimal range-separation parameter (ω) is crucial for LRC functional performance.
Purpose of the Study:
- To develop an accurate and efficient machine learning model for predicting the global-density-dependent range-separation parameter (ωGDD) for LRC-ωPBE.
- To enable rapid, computationally inexpensive predictions of ωGDD using only atomic coordinates.
- To demonstrate the utility of the developed model in predicting molecular properties and interactions.
Main Methods:
- Development of an XGBoost machine learning model (ωGDDML) utilizing fingerprints of local atomic environments and distance histograms.
- Training and validation of the model on a large dataset of 11,466 diverse chemical systems.
- Application of the LRC-ωPBE(ωGDDML) functional to predict polarizabilities and noncovalent interactions.
Main Results:
- The ωGDDML model achieved high accuracy on a test set of 7046 complexes, with a mean absolute error of 0.001117 a0-1.
- Excellent transferability was observed, with only 0.07% of systems showing errors greater than 0.01 a0-1.
- LRC-ωPBE(ωGDDML) demonstrated superior performance in predicting polarizabilities and bypassed the need for traditional ab initio system-specific tuning for noncovalent interactions.
Conclusions:
- The developed ωGDDML model provides an accurate, efficient, and transferable method for determining the range-separation parameter for LRC-ωPBE.
- This data-driven approach significantly reduces computational cost by eliminating the need for electronic structure calculations for parameter prediction.
- The fusion of physically inspired LRC-ωPBE with the data-driven ωGDDML model offers synergistic benefits for quantum mechanical calculations.