Modeling of the formation of magnetic materials by binary arithmetic: the formation mechanism of magnetic materials is discussed in my previous article , which can be simulated by another 3D modeling. In a new model, each molecule is evenly divided into 8 spacial grids. If the electron clouds or orbits in a spacial grid is clockwise orientation, its value is set to be 0 as Yin pole; If the electron clouds or orbits in a spacial grid is anti-clockwise orientation, its value is set to be 1 as Yang pole; Modeling of symmetric magnetism: if there is collision between two molecules, and the adjacent spacial grids between these two molecules is symmetric magnetism, which means that one is clockwise orientation as 0 value and the other is anti-clockwise orientation as 1 value, it strengthens the formation of magnetic materials. If the adjacent spacial grids between these two molecules is asymmetric magnetism, which means that two spacial grids show the same orientation orbits, they generate repulsive force against each other; the motion orbit of the central points of a molecule is set as random probability in each spacial grid, driven by thermal energy effects. Obviously, if there is more symmetric spacial array as [0,1] or [1,0] value by binary arithmetic, this materials tends to be ferromagnetism; if there is more asymmetric spacial array, including [0,0] and [1,1] by binary arithmetic, this materials tends to be anti-ferromagnetism.The probability of both symmetric spacial array and asymmetric spacial array in each grid can be simulated and estimated by this theoretical 3D modeling.
Modeling of electron cloud: in a new sub-model, each atom is evenly divided into 64 spacial grids. Then it is to model the spinning of electron cloud as: if the electron clouds or orbits in a spacial grid is clockwise orientation, its value is set to be - 1 as Yin pole; if the electron clouds or orbits in a spacial grid is anti-clockwise orientation, its value is set to be +1 as Yang pole. It is further to model the density of electron cloud, which is measured by magnetic flux: if the density of electron cloud in a spacial grid is higher, then the probability of electron occurrence × electron quantity in this spacial grid is higher, so that the magnetic flux within unit temporal scale in this spacial grid is stronger. The overall magnetic flux of an atom is the vector sum of magnetic flux in each spacial grid. For the highly symmetric electron clouds of an atom, the overall magnetic flux tends to be zero, revealing high stability in chemistry activity. In comparison, the asymmetric magnetic flux in spatial grids represents the free electron orbits. Then the more spatial grids of asymmetric magnetic flux, or the more asymmetric overall magnetic flux of an atom, the broader spherical surface area of active chemistry bonds in atom spinning motion, the more active in chemistry reaction. My another article has demonstrated that the quantity of electric charges must be different between clockwise spinning electrons and anticlockwise spinning electrons in electron clouds . Therefore, constant j and k are given to the clockwise spinning electrons and anticlockwise spinning electrons in electron clouds respectively, when the quantity of electric charges is modeled to represent the difference in electric charges between two types. For the electrons with higher electric charges, the total electron energy is higher, the chemistry bond energy required to activate the reaction is lower, so the chemistry reaction is more active correspondingly. When the parameter of chemistry bond energy is added in this 3D modeling, the chemistry reaction rate is calculated as:
Chemistry Reaction Rate = Pa × Matrix A × Pb × (Matrix B)^T × Fa × Fb × 1/Ea × 1/Eb
Where Ea , Eb represents the chemistry bond energy for active chemistry bond A and B respectively. According to the definition of chemistry bond energy in quantum chemistry, the Ea , Eb is the function of the overall magnetic flux of an atom, which will be further discussed and quantified in the coming article .
In this paper, the Chinese Eight Diagrams matrix operation essentially reflects that the magnetic line in the fourth dimension coordinate between two symmetric three-dimensional spaces dominates the motion law of microscopic particles. Its theory has been fully discussed in previous papers . In this paper, the 3D modeling sets the occurrence probability of the particle motion trajectory in each spatial grid cell as the random one, but the probability distribution of randomness is still subject to statistical laws (such as normal distribution rate), which is fundamentally different from the disorder of probability. Just because the distribution probability of microscopic particles on the spatial motion trajectory is subject to the statistical laws, it can be predicted and modeled by the simulation. If it is the disordered motion trajectory, it cannot be estimated by the modeling. Thus, it also reveals that the magnetic line between two symmetric three-dimensional spaces is an intelligent magnetic line dominated by biotic forces.
以上两个图片分别为阴、阳二阵，阴阵为矩阵 A=[A1, A2, ....A8], 阳阵为矩阵 B=[B1, B2,...B8], 其中 A1 与 B1 为 0, 1 二位进制算法的随机型数字 (如图所示) 排列组合，并且分别为矩阵 A 和 B 的第一列。与A1和B1相似，矩阵A和B中其它列中的数值亦为0, 1 二位进制算法的随机型数字排列组合。阴阳二阵的相互作用关系表达为矩阵 A 乘以矩阵 B 的倒置。每隔一段时间 T，矩阵就像时钟一样旋转一格，旋转一圈是一共八格。如图例所示，阴阵顺时针方向旋转一格，矩阵A 则变化为[A8,A1,A2,...A7], 阴阳二阵的相互作用关系表达为变化后的矩阵A乘以矩阵B的倒置。之后再次旋 转一格，矩阵变化则以此类推。
神奇算术：每旋转一圈，A 和 B 中的 0, 1 二位进制算法的数字排列组合就会重新随机型选取一次，代表阴、阳二阵中的事物随着时间发生变动，但是最终的行列式运算结果总体上一定符合统计学规律，并非无序性。如果喜欢应用数学，可以通过模拟计算并且探讨。
Please note: This is the revised materials in book “Proceedings for Degree of Postgraduate Diploma in Environmental Science (3rd Edition).” published in 2016. Firstly revised on 31/ 12/2020; Secondly revised on 20/ 11/2021; Thirdly revised on 21/ 11/2021; Fourthly revised on 30/01/2022; Fifthly revised on 17/02/2022. This journal article is previously published as: Liu Huan. (2021). Ancient Chinese Eight Diagrams and Application on Chemistry Reaction Rate. Journal of Environment and Health Science (ISSN 2314- 1628), 2021(02)., which is converted into Journal of Quantum Physics and Materials Chemistry (ISSN2958-4027) . Both Journals belong to the same publisher, Liu Huan. The previous journal article is closed to the public, but the previous reference is still valid. Latest revised on 04/02/2023; 05/02/2023; 06/02/2023;08/02/2023a; 08/02/2023b; 08/02/2023c; 10/02/2023; 12/02/2023; 22/05/2023; 26/05/2023; 07/06/2023; 12/06/2023; 03/08/2023 a;b; 04/08/2023; 05/08/2023; 08/11/2023 a;b.
[ 1]. Liu Huan. (2021). The anti-matter of symmetric three-dimensional spaces along the fourth dimension axis. Journal of Environment and Health Science (ISSN 2314- 1628), 2021(2). https://doi.org/10.58473/JAES0002
. Liu Huan. (2022). Essay: Electromagnetics and Materials. Journal of Environment and Health Science (ISSN 2314- 1628), 2022(11). https://doi.org/10.58473/JQPMC0004
. Liu Huan (2021). Modeling of annual net primary production of a forest in the Taramakau Valley, Westland, New Zealand. Journal of Environment and Health Science (ISSN 2314- 1628). 2021(2). https://doi.org/10.58473/JEHS0007
. Liu Huan (2021). The particle dualism of electromagnetic waves. Journal of Environment and Health Science (ISSN 2314- 1628), 2021(2). https://doi.org/10.58473/JQPMC0002
. Liu Huan. (2021). The Principal of Thermodynamics: The Inner Energy, Energy Loss and Materials Perishing. Journal of Environment and Health Science (ISSN 2314- 1628), 2021(02). https://doi.org/10.58473/JQPMC0008
. Liu Huan (2023). Essay: original review of high-dimensional spaces and astronomy theories in modern physics. Journal of Astronomy and Earth Sciences (ISSN2958-4043). 2023 (07). https://doi.org/10.58473/JAES0010
. Liu Huan. (2023). Original review of quantum chemistry and 3D modeling of artificial intelligence. Journal of Quantum Physics and Materials Chemistry (ISSN2958-4027). 2023(11). https://doi.org/10.58473/JQPMC0013