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Design method of composite sample store and its system

A design method and sample technology, applied in the field of high-efficiency experiments-high-throughput experiments, can solve problems such as lack of experience and knowledge without consideration

Inactive Publication Date: 2007-04-18
YASHENTECH CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this approach takes little into account the known empirical knowledge about the constituents of the sample
Even taking such empirical knowledge into account, there is a lack of adequate methods for designing sample pools that are sufficiently randomized in the sample space

Method used

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  • Design method of composite sample store and its system
  • Design method of composite sample store and its system
  • Design method of composite sample store and its system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0112] This example shows how to select qualified samples from pseudo-samples composed of two components (cerium and iron) produced by Monte Carlo simulation. Cerium variable V Ce Takes a value between 0 and 1, the iron variable V Fe It also takes a value between 0 and 1. The Monte Carlo simulation uses a uniformly distributed randomly generated V between 0 and 1 Ce and V Fe value to be carried out, the simulation in which V Ce A randomly generated value of V Fe The randomly generated values ​​of are independent of each other. And in this simulation, V Ce and V Fe There are no mandatory limitations of any relationship or constraint. As a result of the Monte Carlo simulation, pseudo-sample populations are generated. The totality of points (including hollow, gray and dark colors) as shown in Fig. 1 constitute a set of pseudo samples.

[0113] We can use empirical knowledge to reduce the number of qualified samples by introducing constraints. The first constraint is de...

Embodiment 2

[0222] This example shows how to select qualified samples from pseudo-samples composed of four components (cerium, iron, tungsten and nickel) generated by Monte Carlo simulation. Variable V for cerium, iron, tungsten and nickel Ce , V Fe , V W , V NiBoth take values ​​between 0 and 1. In the Monte Carlo simulation, we use uniformly distributed randomly generated values ​​between 0 and 1 for each variable. The randomly generated values ​​in this simulation are independent of each other and are not limited by any constraints. The result of the Monte Carlo simulation is a sample point (pseudo sample) in the four-dimensional space, and the projection of the four-dimensional sample point in the three-dimensional space is shown in FIG. 2 .

[0223] Here a first constraint is proposed for selecting samples corresponding to physically real, which is defined as V Ce +V Fe +V W +V Ni =1. When the first constraint is considered in the selection process, the selected set of pseud...

Embodiment 3

[0228] This example describes a computer program that allows a user to enter information through a graphical user interface and perform calculations and simulations, including Monte Carlo simulations, to design qualified sample libraries.

[0229] As shown in Figure 5, the graphical user interface allows the user to select the desired components for designing samples. For example, a sample composed of components A, B and C, component A can be any one from the element group consisting of vanadium (V), niobium (Nb) and molybdenum (Mo), the variable of component A (V a ) ranges from 0 to 1 (please refer to the range of 0.00 to 1.00 shown in FIG. 5 ), and the variable range is divided into 10 parts (10 segments as shown in FIG. 5 ). As a result, component A is assigned a variable (V a ) (please refer to Figure 6), similarly, components B and C are also given corresponding variables (V b and V c ) (please refer to Figure 6).

[0230] As a way to incorporate empirical knowledge...

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Abstract

The invention offered a method to design combined library of samples, which facilitated people to reduce the frequency of sample experiments and increase effective amount of information in fixed frequency of sample experiments by applying acquired knowledge and given assumption extremely. The steps of this method were as follows: (1) Many components to compose samples were supplied. (2) Variables were offered for each component, which were sampled at intervals. (3) At least one constraint condition was set for at least one variable. (4) Pseudo-samples were produced. (5) Pseudo-samples were checked to confirm whether they were qualified samples. (6) Steps (4) and (5) were repeated until at least one qualified sample was found. The method of this invention was efficient and accurate, which could avoid system deviation in sample design.

Description

technical field [0001] The invention relates to an efficient test method-high-throughput test method, and more specifically, relates to the field of design of a combined sample library therein. Background technique [0002] For many properties of materials, such as thermal conductivity, luminescence, catalytic activity, etc., the discovery methods and systems of combinatorial materials can be used to identify new materials or optimize existing materials. Current methods for combinatorial studies synthesize large numbers of samples 'by brute force' through lattice searches in the sample space, and then screen these samples for desired properties. However, this approach takes little account of known empirical knowledge about the constituents of the sample. Even taking such empirical knowledge into account, there is a lack of adequate methods for designing sample pools that are sufficiently randomized in the sample space. [0003] Therefore, it is necessary to develop a new c...

Claims

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Application Information

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IPC IPC(8): C40B30/00G16C20/62
CPCC40B50/02G16B35/00G16C20/60G16C20/62
Inventor 华新雷冯希臣
Owner YASHENTECH CORP
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