Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
出版年份 2021 全文链接
标题
Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
作者
关键词
-
出版物
Scientific Reports
Volume 11, Issue 1, Pages -
出版商
Springer Science and Business Media LLC
发表日期
2021-04-26
DOI
10.1038/s41598-021-88027-8
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model
- (2020) Ryther Anderson et al. Journal of Chemical Theory and Computation
- A Universal Machine Learning Algorithm for Large-Scale Screening of Materials
- (2020) George S. Fanourgakis et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials
- (2020) Aditi S. Krishnapriyan et al. Journal of Physical Chemistry C
- Understanding the diversity of the metal-organic framework ecosystem
- (2020) Seyed Mohamad Moosavi et al. Nature Communications
- Revealing hidden medium-range order in amorphous materials using topological data analysis
- (2020) Søren S. Sørensen et al. Science Advances
- Catalysis by Metal Organic Frameworks: Perspective and Suggestions for Future Research
- (2019) Dong Yang et al. ACS Catalysis
- Unsupervised word embeddings capture latent knowledge from materials science literature
- (2019) Vahe Tshitoyan et al. NATURE
- Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRE MOF 2019
- (2019) Yongchul G. Chung et al. JOURNAL OF CHEMICAL AND ENGINEERING DATA
- Machine learning and in silico discovery of metal-organic frameworks: Methanol as a working fluid in adsorption-driven heat pumps and chillers
- (2019) Zenan Shi et al. CHEMICAL ENGINEERING SCIENCE
- Matminer: An open source toolkit for materials data mining
- (2018) Logan Ward et al. COMPUTATIONAL MATERIALS SCIENCE
- Role of Pore Chemistry and Topology in the CO2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning
- (2018) Ryther Anderson et al. CHEMISTRY OF MATERIALS
- Effect of pore size and shape on the thermal conductivity of metal-organic frameworks
- (2017) Hasan Babaei et al. Chemical Science
- Quantifying similarity of pore-geometry in nanoporous materials
- (2017) Yongjin Lee et al. Nature Communications
- Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)
- (2017) Maryam Pardakhti et al. ACS Combinatorial Science
- Author Correction: Chemically intuited, large-scale screening of MOFs by machine learning techniques
- (2017) Giorgos Borboudakis et al. npj Computational Materials
- A generalized method for constructing hypothetical nanoporous materials of any net topology from graph theory
- (2016) Peter G. Boyd et al. CRYSTENGCOMM
- Geometrical Properties Can Predict CO2 and N2 Adsorption Performance of Metal–Organic Frameworks (MOFs) at Low Pressure
- (2016) Michael Fernandez et al. ACS Combinatorial Science
- Quantum-Chemical Characterization of the Properties and Reactivities of Metal–Organic Frameworks
- (2015) Samuel O. Odoh et al. CHEMICAL REVIEWS
- What Are the Best Materials To Separate a Xenon/Krypton Mixture?
- (2015) Cory M. Simon et al. CHEMISTRY OF MATERIALS
- Responsive Metal–Organic Frameworks and Framework Materials: Under Pressure, Taking the Heat, in the Spotlight, with Friends
- (2015) François-Xavier Coudert CHEMISTRY OF MATERIALS
- Methane storage in metal–organic frameworks
- (2014) Yabing He et al. CHEMICAL SOCIETY REVIEWS
- Topological Analysis of Metal–Organic Frameworks with Polytopic Linkers and/or Multiple Building Units and the Minimal Transitivity Principle
- (2013) Mian Li et al. CHEMICAL REVIEWS
- Similarity-Driven Discovery of Zeolite Materials for Adsorption-Based Separations
- (2012) Richard L. Martin et al. CHEMPHYSCHEM
- Carbon Dioxide Capture in Metal–Organic Frameworks
- (2011) Kenji Sumida et al. CHEMICAL REVIEWS
- Metal–Organic Frameworks for Separations
- (2011) Jian-Rong Li et al. CHEMICAL REVIEWS
- Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials
- (2011) Thomas F. Willems et al. MICROPOROUS AND MESOPOROUS MATERIALS
- Large-scale screening of hypothetical metal–organic frameworks
- (2011) Christopher E. Wilmer et al. Nature Chemistry
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