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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...

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Predicción de catalizadores de mayor selectividad mediante flujo de trabajo controlado por computadora y aprendizaje

Andrew F Zahrt¹, Jeremy J Henle¹, Brennan T Rose¹

¹Roger Adams Laboratory, Department of Chemistry, University of Illinois, Urbana, IL 61801, USA.

Science (New York, N.Y.)

|January 19, 2019

Resumen

Este estudio introduce un enfoque computacional para la selección de catalizadores quirales, acelerando las reacciones asimétricas. Los modelos de aprendizaje automático predicen con precisión la selectividad del catalizador, mejorando la eficiencia en la síntesis química.

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Área de la Ciencia:

Catálisis asimétrica
Química computacional
Aprendizaje automático en química

Sus antecedentes:

El diseño tradicional de catalizadores se basa en métodos empíricos y en el reconocimiento cualitativo de patrones.
El aprendizaje automático y la quimioinformática ofrecen el potencial para acelerar el descubrimiento de catalizadores mediante el análisis de grandes conjuntos de datos.
El desarrollo de modelos predictivos para la selectividad del catalizador quiral es crucial para avanzar en la síntesis asimétrica.

Objetivo del estudio:

Desarrollar un flujo de trabajo guiado por computación para la selección de catalizadores quirales.
Utilizar la quimioinformática para descriptores moleculares robustos y conjuntos de entrenamiento universales.
Entrenar modelos de aprendizaje automático para una predicción precisa de la selectividad del catalizador.

Principales métodos:

Empleó la quimioinformática para generar descriptores moleculares agnóstico del andamio.
Construido un conjunto de entrenamiento universal basado en propiedades estéricas y electrónicas.
Algoritmos aplicados de aprendizaje automático, incluidas las máquinas vectoriales de soporte y las redes neuronales de transmisión profunda.

Principales resultados:

Logró modelos predictivos muy precisos para la selectividad del catalizador en un amplio rango.
Aplicación demostrada con éxito en la adición de tiol catalizado por ácido fosfórico quiral a las N-aciliminas.
Validación de la eficacia del flujo de trabajo computacional para guiar la selección del catalizador.

Conclusiones:

El flujo de trabajo computacional desarrollado acelera significativamente la selección de catalizadores quirales.
Los modelos de aprendizaje automático proporcionan predicciones precisas, superando las limitaciones de los métodos empíricos.
Este enfoque mejora la eficiencia y el alcance en el desarrollo de reacciones asimétricas.