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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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.
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Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
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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 squares (OLS)...
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Related Experiment Video

Updated: Jun 21, 2026

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

Machine learning potentials for modeling alloys across compositions.

Killian Sheriff1, Daniel Z Xiao1, Yifan Cao1

  • 1Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.

Science Advances
|June 19, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning potentials (MLPs) now better predict metallic alloy behavior by optimizing chemical sampling. This approach accurately captures diverse chemical arrangements for improved materials modeling and property prediction.

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Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

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Last Updated: Jun 21, 2026

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
08:32

Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting

Published on: May 14, 2016

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Area of Science:

  • Materials Science
  • Computational Materials Science
  • Chemical Physics

Background:

  • Materials properties are fundamentally linked to chemical composition and arrangement.
  • Predicting materials behavior across the full compositional spectrum, from ordered compounds to disordered solid solutions, is a significant challenge.
  • Existing machine learning potentials (MLPs) struggle with accurately capturing diverse chemical arrangements, limiting predictive power in materials modeling.

Purpose of the Study:

  • To develop advanced machine learning potentials (MLPs) capable of accurately modeling metallic alloys across their entire compositional and structural landscape.
  • To enhance the predictive accuracy of MLPs by optimizing the sampling of chemical motifs using information theory.
  • To enable high-fidelity materials modeling by effectively capturing complex chemical arrangements.

Main Methods:

  • Integration of information theory with machine learning techniques to optimize the sampling of chemical motifs.
  • Design and application of novel MLPs tailored for metallic alloys.
  • Systematic prediction of materials properties, including stacking-fault energies, short-range order, heat capacities, and phase diagrams.

Main Results:

  • Demonstrated effectiveness of the developed MLPs in predicting the compositional dependence of key materials properties for binary (AuPt, CuAu), ternary (CrCoNi), and high-entropy alloys (TiTaVW).
  • Accurate prediction of stacking-fault energies, short-range order, heat capacities, and phase diagrams across diverse alloy systems.
  • Validation against extensive experimental data confirmed the robustness and high physical fidelity of the approach.

Conclusions:

  • The combined information theory and machine learning approach significantly improves the ability of MLPs to model materials properties.
  • This method provides a robust framework for designing MLPs that accurately capture the behavior of metallic alloys across their full compositional range.
  • The developed approach enables materials modeling with unprecedented physical fidelity, advancing the field of computational materials science.