Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Maxwell-Boltzmann Distribution: Problem Solving

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by

You might also read

Related Articles

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

Sort by

Same author

Designing a Robust MEA-Based Post-Combustion Carbon Capture Process with Capture Rate Guarantees.

Industrial & engineering chemistry research·2026

Same journal

PSDTA: An Approach to Drug-Target Binding Affinity Prediction by Integrating Physicochemical and Structural Information to Reduce Feature Redundancy.

Journal of chemical information and modeling·2026

Same journal

M-JEPA: Predictive Self-Supervised Learning for Molecular Graphs with Scaffold-Shift Evaluation on Tox21.

Journal of chemical information and modeling·2026

Same journal

Advancing Biochemical Molecule Registration, Representation and Search for New Drug Modalities.

Journal of chemical information and modeling·2026

Same journal

A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug-Hormone Receptor Interactions with Structure-Aware Validation.

Journal of chemical information and modeling·2026

Same journal

Intricate Role of Cholesterol in Membrane Fusion.

Journal of chemical information and modeling·2026

Same journal

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

Journal of chemical information and modeling·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 6, 2025

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

MatOpt: A Python Package for Nanomaterials Design Using Discrete Optimization.

Christopher L Hanselman¹, Xiangyu Yin¹, David C Miller²

¹Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

Journal of Chemical Information and Modeling

|January 13, 2022

Summary

This summary is machine-generated.

A new Python package, MatOpt, simplifies the design of nanostructured materials. It uses mathematical optimization to efficiently explore atomic arrangements, accelerating materials development for scientists.

More Related Videos

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

Published on: February 10, 2023

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Published on: October 31, 2013

Related Experiment Videos

Last Updated: Oct 6, 2025

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

Published on: February 10, 2023

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Fine-tuning the Size and Minimizing the Noise of Solid-state Nanopores

Published on: October 31, 2013

Area of Science:

Materials Science
Computational Materials Science
Optimization

Background:

Advances in synthesis and computational methods accelerate materials development.
Mathematical optimization models can navigate complex atomic arrangement design spaces.
Previous work demonstrated optimization for nanostructured materials design.

Purpose of the Study:

To identify common features of materials optimization problems.
To develop a tool that models these problems using mixed-integer linear optimization.
To facilitate the on-demand design of nanostructured materials.

Main Methods:

Highlighting commonalities in materials optimization problems.
Formalizing design space representation and optimization model formulation.
Developing the MatOpt Python package for mixed-integer linear optimization.

Main Results:

Identification of common features in materials optimization.
Creation of MatOpt, a Python package for nanostructured materials design.
Demonstration of efficient modeling via mixed-integer linear optimization.

Conclusions:

MatOpt bridges the gap between materials science and optimization expertise.
The package lowers barriers to applying numerical optimization in materials development.
Facilitates rigorous, on-demand design of novel nanostructured materials.