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Polymers02:34

Polymers

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The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...
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Polymers02:34

Polymers

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Shape and Texture of Coarse Aggregate01:25

Shape and Texture of Coarse Aggregate

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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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Optimal Foraging00:48

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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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Dimensional Analysis03:40

Dimensional Analysis

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Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
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Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
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Updated: Jan 22, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Optimización bayesiana para la parametrización de modelos de grano grueso de alta dimensionalidad: un estudio de caso

Carlos A Martins1, Daniela A Damasceno2, Keat Yung Hue3,4

  • 1University of São Paulo, Department of Materials Physics and Mechanics, Institute of Physics, Rua do Matão 1371, São Paulo 05508-090, Brazil.

Journal of chemical theory and computation
|January 21, 2026
PubMed
Resumen
Este resumen es generado por máquina.

La optimización bayesiana (BO) optimiza eficazmente modelos de grano grueso (CG) complejos con muchos parámetros. Este enfoque, que utiliza el estimador Parzen estructurado en árbol (TPE), acelera el desarrollo de modelos CG para simulaciones de materiales.

Palabras clave:
optimización bayesianamodelos de grano gruesoparametrización de polímerossimulaciones de materialesestimador Parzen estructurado en árbol

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Área de la Ciencia:

  • Ciencia computacional de materiales
  • Física de polímeros
  • Mecánica estadística

Sus antecedentes:

  • Los campos de fuerza de grano grueso (CG) ofrecen eficiencia computacional para simulaciones de materiales.
  • La parametrización tradicional de modelos CG utiliza métodos secuenciales de arriba hacia abajo y de abajo hacia arriba, lo que limita la optimización conjunta de parámetros.
  • La optimización bayesiana (BO) se ha limitado a problemas de baja dimensionalidad, lo que dificulta su aplicación a modelos CG complejos.

Objetivo del estudio:

  • Extender la optimización bayesiana (BO) para la parametrización de modelos de grano grueso (CG) de alta dimensionalidad.
  • Desafiar la suposición de que la BO no es adecuada para modelos CG complejos con numerosos parámetros.
  • Desarrollar un modelo CG eficiente para Pebax-1657 optimizando múltiples propiedades físicas simultáneamente.

Principales métodos:

  • Se utilizó el modelo de estimador Parzen estructurado en árbol (TPE), una variante de la optimización bayesiana (BO).
  • Se aplicó BO-TPE a un modelo CG de 41 parámetros de Pebax-1657.
  • Se optimizaron simultáneamente propiedades estructurales (densidad, radio de giro) y termodinámicas (temperatura de transición vítrea).

Principales resultados:

  • Se parametrizó con éxito un modelo CG de alta dimensionalidad (41 parámetros) de Pebax-1657 utilizando BO-TPE.
  • El modelo CG optimizado reprodujo con precisión las propiedades físicas clave de la representación atomística.
  • BO-TPE demostró una convergencia más rápida y mejoras consistentes sobre los métodos de parametrización tradicionales.

Conclusiones:

  • La optimización bayesiana (BO), específicamente BO-TPE, es una estrategia viable y eficaz para optimizar campos de fuerza CG de alta dimensionalidad.
  • Este enfoque BO extendido permite el desarrollo de modelos CG precisos y eficientes para polímeros complejos como Pebax-1657.
  • El marco ofrece un avance significativo sobre los métodos convencionales para la parametrización de modelos CG en simulaciones de materiales.