journal article·2026-09

Deep learning-based temperature field prediction model for diamond synthesis assemblies

Xiaotian Xing, Fuming Deng, Hengchen Xu, Chang Dong, Houzhen Chen, Yonghai Lai, Jiale Niu, Lei Zhang, Diaoyu Wang, Ziyi Liu, Qiang Chen, Jianghe Liao

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References (31)

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1. Growth of structurally perfect diamond single crystals at high pressures and temperatures. Review10.3103/S1063457618050039 ↗
2. Studies on the mechanism of large cavity to improve the growth rate of large diamond under HPHT conditions10.1016/j.jcrysgro.2019.03.016 ↗
3. Method to reduce the oil pressure during HPHT diamond synthesis: FEM simulations and experiments10.1039/D0CE00913J ↗
4. Finite element design for the HPHT synthesis of diamond10.1016/j.jcrysgro.2017.12.021 ↗
5. Temperature field simulation of HTHP diamond synthesis cavity stacked with sheet alloy catalyst and graphite10.1016/j.jcrysgro.2023.127337 ↗
6. Simulation of temperature and convection field of diamond large single crystal growth with different catalyst systems10.1016/j.jcrysgro.2025.128419 ↗
7. FEM simulations and experimental studies of the temperature field in a large diamond crystal growth cell
8. Structure and properties of high-strength HPHT sintered carbon-graphite blocks10.1016/j.ceramint.2025.06.197 ↗
9. Deep learning based key information extraction from business documents: systematic literature review10.1145/3749369 ↗
10. Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review10.1007/s10462-023-10690-2 ↗
11. Small data challenges for intelligent prognostics and health management: a review10.1007/s10462-024-10820-4 ↗
12. GraphSAGE with deep reinforcement learning for financial portfolio optimization10.1016/j.eswa.2023.122027 ↗
13. Explainable artificial intelligence (XAI) in finance: a systematic literature review10.1007/s10462-024-10854-8 ↗
14. Code smell detection based on supervised learning models: a survey10.1016/j.neucom.2023.127014 ↗
15. Advancing materials science through next-generation machine learning10.1016/j.cossms.2024.101157 ↗
16. Applications of computational mechanics methods combined with machine learning and neural networks: a systematic review (2015–2025)10.3390/app151910816 ↗
17. A deep learning-based crystal plasticity finite element model10.1016/j.scriptamat.2024.116315 ↗
18. Precise temperature prediction for small-sample fiber optic spectra based on deep learning10.1109/LPT.2024.3483210 ↗
19. Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning10.1016/j.neuroimage.2022.119705 ↗
20. Predicting the temperature field of thermal cloaks in homogeneous isotropic multilayer materials based on deep learning10.1016/j.ijheatmasstransfer.2023.124849 ↗
21. Cross-scale thermodynamic simulation of RDL using hybrid machine learning and finite element modeling
22. Integrating machine learning and finite element simulation for interpretable prediction of 3D-printed bone scaffold mechanics
23. Application of machine learning and deep learning in finite element analysis: a comprehensive review10.1007/s11831-024-10063-0 ↗
24. Fe melting transition: electrical resistivity, thermal conductivity, and heat flow at the inner core boundaries of mercury and ganymede10.3390/cryst9070359 ↗
25. The thermal conductivity of pyrolytic graphite
26. Solution of steady periodic heat conduction by the finite-element method10.1080/10407788908944702 ↗
27. Relationship between mechanical properties and processing performance of agglomerated diamond abrasive compared with single diamond abrasive10.1016/j.diamond.2019.107595 ↗
28. Effects of cavity size on the growth of hexahedral type-Ib gem-diamond single crystals
29. Surface roughness and lateral forces on the faces of type Ib and IIa diamond single crystals10.3103/S1063457625010058 ↗
30. Synthesis and characteristics of type Ib diamond doped with NiS as an additive10.1088/0256-307X/36/4/046101 ↗
31. A survey of loss functions in deep learning10.3390/math13152417 ↗