journal article·2026-01

Incremental learning with heterogeneous data for morphology prediction in metal-organic frameworks

Yuncheng Du, Cristina Sanchez Cereceda, Dongping Du

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Metal-Organic Frameworks: Synthesis and Applications · Machine Learning in Materials Science · Machine Learning and ELM

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (24)

Show source-provided references
1. MOFs for long-term gas storage: exploiting kinetic trapping in ZIF-8 for on-demand and stimuli-controlled gas release10.1039/D3QI01007D ↗
2. Metal–organic frameworks in heterogeneous catalysis: recent Progress, new trends, and future perspectives10.1021/acs.chemrev.9b00685 ↗
3. Applications of Metal–Organic Frameworks (MOFs) in Drug Delivery, Biosensing, and Therapy: A Comprehensive Review10.1021/acs.langmuir.4c02795 ↗
4. Morphology control through the synthesis of metal-organic frameworks10.1016/j.cis.2023.102864 ↗
5. Investigation of metal-organic frameworks (MOFs): synthesis, properties, and applications - an in-depth review10.1016/j.chphi.2025.100864 ↗
6. Capturing chemical intuition in synthesis of metal-organic frameworks
7. Recent advances in computational modeling of MOFs: from molecular simulations to machine learning10.1016/j.ccr.2023.215112 ↗
8. Metal-organic frameworks: advances in first-principles computational studies on catalysis, adsorption, and energy storage
9. From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks10.1021/jacsau.4c00618 ↗
10. Comprehensive overview of machine learning applications in MOFs: from modeling processes to latest applications and design classifications10.1039/D4TA06740A ↗
11. Unified roadmap for ZIF-8 nucleation and growth: machine learning analysis of synthetic variables and their impact on particle size and morphology10.1021/acs.chemmater.4c01069 ↗
12. Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks10.1021/jacs.1c07217 ↗
13. An Intelligent Prediction Model for the Synthesis Conditions of Metal–Organic Frameworks Utilizing Artificial Neural Networks Enhanced by Genetic Algorithm Optimization10.1021/acs.jcim.4c00997 ↗
14. An XGBoost algorithm based on molecular structure and molecular specificity parameters for predicting gas adsorption10.1021/acs.langmuir.3c00255 ↗
15. Tabular data: deep learning is not all you need10.1016/j.inffus.2021.11.011 ↗
16. Lundberg · 2017
17. Machine learning for molecular and materials science10.1038/s41586-018-0337-2 ↗
18. Influence of the 2-methylimidazole/zinc nitrate hexahydrate molar ratio on the synthesis of zeolitic imidazolate framework-8 crystals at room temperature10.1038/s41598-018-28015-7 ↗
19. The effect of synthesis temperature on the particle size of nano-ZIF-8
20. Effect of synthesis parameters on ZIF-8 morphology: insights from experimental and statistical integrated analysis
21. Winsorization for robust Bayesian neural networks10.3390/e23111546 ↗
22. SMOTE: synthetic minority over-sampling technique10.1613/jair.953 ↗
23. Synthesis of Zeolitic Imidazolate Framework-8 using glycerol carbonate10.1021/acssuschemeng.3c02876 ↗
24. Precursor concentration effects on crystallite size and enzyme immobilization efficiency of enzyme@ZIF-8 composite10.1016/j.jcrysgro.2022.126877 ↗