首页|Investigators from Shenyang University of Technology Release New Data on Machine Learning (Insights Into the Diffusion Coefficient and Adsorption Energy of Nh3 In Mgcl2 From Molecular Simulation, Experiments, and Machine Learning)
Investigators from Shenyang University of Technology Release New Data on Machine Learning (Insights Into the Diffusion Coefficient and Adsorption Energy of Nh3 In Mgcl2 From Molecular Simulation, Experiments, and Machine Learning)
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NETL
NSTL
Elsevier
Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Liaoning, People’s Republic of China, by NewsRx correspondents, research stated, “Molecular models were developed to evaluate the adsorption behavior between NH3 and MgCl2. These models were utilized to assess the diffusion coefficient and adsorption energy at different temperatures and pressures through the application of molecular dynamics (MD) simulations.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Liaoning Application demonstration of new generation light. Our news editors obtained a quote from the research from the Shenyang University of Technology, “Subsequently, a comprehensive dataset comprising the diffusion coefficient and adsorption energy was established. The analysis of the relative diffusion and adsorption mechanisms involved calculating the radius distribution function, coordination numbers, and energy values within the primary solvent layer. The molecular simulation results revealed that the highest values for the diffusion coefficient and adsorption energy of NH3 in MgCl2 were observed at a temperature of 348 K and a pressure of 0.2 MPa. Moreover, the experimental findings exhibited good agreement with the computational simulation conclusions. The preparation of Magnesium hydroxide (MH) under the aforementioned temperature and pressure conditions resulted in a concentrated particle size distribution, effective dispersion, and a complete hexagonal sheet morphology. Furthermore, machine learning predictions were performed using significant features (i.e., molarity(M), temperature(T), pressure(P), volume(Ⅴ), density(D), and total energy(E)).”
LiaoningPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningShenyang University of Technology
NETL
NSTL
Elsevier
