4.8 Article

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Review Chemistry, Multidisciplinary

The Current Status of MOF and COF Applications

Ralph Freund et al.

Summary: The amalgamation of different disciplines at the heart of reticular chemistry has broadened the boundaries of chemistry. Reticular design enables precise prediction of crystalline framework structures, tunability of chemical composition, and fine-tuning of material properties in metal-organic frameworks and covalent organic frameworks. Leveraging the unique properties of reticular materials has led to significant advances in both fundamental and applied research.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2021)

Article Chemistry, Multidisciplinary

Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks

Zach Jensen et al.

Summary: Organic structure directing agents (OSDAs) play a crucial role in synthesizing micro- and mesoporous materials, especially zeolites, but their interaction mechanisms with zeolite frameworks are not well understood. A data-driven approach was used to establish OSDA-zeolite relationships through a comprehensive database of 5,663 synthesis routes. Structural features of OSDAs were analyzed using WHIM descriptors to relate them to different types of zeolites, and a generative neural network was adapted to suggest new OSDA candidates for specific zeolite structures and gel chemistry.

ACS CENTRAL SCIENCE (2021)

Article Chemistry, Multidisciplinary

Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworks

Kevin Maik Jablonka et al.

Summary: Understanding the oxidation state of metal centres in compounds and materials is crucial for understanding their chemical bonding and properties. By using a machine-learning model trained on chemist-assigned chemical names in the Cambridge Structural Database, oxidation states can be automatically assigned to metal ions in metal-organic frameworks, with high accuracy and reliability. This approach demonstrates the ability to detect incorrect assignments in the database, showcasing how collective knowledge can be harnessed through machine learning.

NATURE CHEMISTRY (2021)

Review Chemistry, Physical

Machine-learned potentials for next-generation matter simulations

Pascal Friederich et al.

Summary: This paper discusses how machine-learned potentials break the limitations of system-size or accuracy, how active-learning will aid their development, how they are applied, and how they may become a more widely used approach.

NATURE MATERIALS (2021)

Review Chemistry, Multidisciplinary

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

Kevin Maik Jablonka et al.

CHEMICAL REVIEWS (2020)

Article Chemistry, Multidisciplinary

Standard Practices of Reticular Chemistry

Cornelius Gropp et al.

ACS CENTRAL SCIENCE (2020)

Article Multidisciplinary Sciences

Understanding the diversity of the metal-organic framework ecosystem

Seyed Mohamad Moosavi et al.

NATURE COMMUNICATIONS (2020)

Editorial Material Multidisciplinary Sciences

Retrospective on a decade of machine learning for chemical discovery

O. Anatole von Lilienfeld et al.

NATURE COMMUNICATIONS (2020)

Review Chemistry, Multidisciplinary

Digital Reticular Chemistry

Hao Lyu et al.

Review Physics, Applied

Data-driven materials research enabled by natural language processing and information extraction

Elsa A. Olivetti et al.

APPLIED PHYSICS REVIEWS (2020)

Article Multidisciplinary Sciences

Capturing chemical intuition in synthesis of metal-organic frameworks

Seyed Mohamad Moosavi et al.

NATURE COMMUNICATIONS (2019)

Review Chemistry, Multidisciplinary

Rising Up: Hierarchical Metal-Organic Frameworks in Experiments and Simulations

Yi Luo et al.

ADVANCED MATERIALS (2019)

Article Chemistry, Multidisciplinary

A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction

Zach Jensen et al.

ACS CENTRAL SCIENCE (2019)

Article Multidisciplinary Sciences

Unsupervised word embeddings capture latent knowledge from materials science literature

Vahe Tshitoyan et al.

NATURE (2019)

Article Multidisciplinary Sciences

Holistic prediction of enantioselectivity in asymmetric catalysis

Jolene P. Reid et al.

NATURE (2019)

Article Chemistry, Multidisciplinary

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

Philippe Schwaller et al.

ACS CENTRAL SCIENCE (2019)

Article Thermodynamics

Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal-Organic Framework Database: CoRE MOF 2019

Yongchul G. Chung et al.

JOURNAL OF CHEMICAL AND ENGINEERING DATA (2019)

Review Chemistry, Multidisciplinary

How to explore chemical space using algorithms and automation

Piotr S. Gromski et al.

NATURE REVIEWS CHEMISTRY (2019)

Article Chemistry, Physical

SchNet - A deep learning architecture for molecules and materials

K. T. Schuett et al.

JOURNAL OF CHEMICAL PHYSICS (2018)

Review Nanoscience & Nanotechnology

Accelerating the discovery of materials for clean energy in the era of smart automation

Daniel P. Tabor et al.

NATURE REVIEWS MATERIALS (2018)

Review Multidisciplinary Sciences

Machine learning for molecular and materials science

Keith T. Butler et al.

NATURE (2018)

Review Chemistry, Physical

Machine learning in materials informatics: recent applications and prospects

Rampi Ramprasad et al.

NPJ COMPUTATIONAL MATERIALS (2017)

Article Multidisciplinary Sciences

Machine-learning-assisted materials discovery using failed experiments

Paul Raccuglia et al.

NATURE (2016)

Article Chemistry, Multidisciplinary

The Cambridge Structural Database

Colin R. Groom et al.

ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE CRYSTAL ENGINEERING AND MATERIALS (2016)

Review Multidisciplinary Sciences

The Chemistry and Applications of Metal-Organic Frameworks

Hiroyasu Furukawa et al.

SCIENCE (2013)

Article Chemistry, Multidisciplinary

ChemicalTagger: A tool for semantic text-mining in chemistry

Lezan Hawizy et al.

JOURNAL OF CHEMINFORMATICS (2011)

Review Chemistry, Multidisciplinary

Functional porous coordination polymers

S Kitagawa et al.

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION (2004)