Inverse design of porous materials using artificial neural networks

TopoFF: MOF structure prediction using specifically optimized blueprints

(2018) Julian Keupp et al.   FARADAY DISCUSSIONS

Predicting the Band Gaps of Inorganic Solids by Machine Learning

(2018) Ya Zhuo et al.   Journal of Physical Chemistry Letters

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

TopoFF: MOF structure prediction using specifically optimized blueprints

(2018) Julian Keupp et al.   FARADAY DISCUSSIONS

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

(2018) Rafael Gómez-Bombarelli et al.   ACS Central Science

Inverse molecular design using machine learning: Generative models for matter engineering

(2018) Benjamin Sanchez-Lengeling et al.   SCIENCE

An evolutionary algorithm for the discovery of porous organic cages

(2018) Enrico Berardo et al.   Chemical Science

Prediction of Carbon Dioxide Adsorption via Deep Learning

(2018) Zihao Zhang et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Modeling adsorption properties of structurally deformed metal–organic frameworks using structure–property map

(2017) WooSeok Jeong et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Excavating hidden adsorption sites in metal-organic frameworks using rational defect engineering

(2017) Sanggyu Chong et al.   Nature Communications

Multi-scale computation methods: Their applications in lithium-ion battery research and development

(2016) Siqi Shi et al.   Chinese Physics B

A review on contemporary Metal–Organic Framework materials

(2016) Kranthi Kumar Gangu et al.   INORGANICA CHIMICA ACTA

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

(2016) Y. G. Chung et al.   Science Advances

In silico prediction of MOFs with high deliverable capacity or internal surface area

(2015) Yi Bao et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

Methane storage in metal–organic frameworks

(2014) Yabing He et al.   CHEMICAL SOCIETY REVIEWS

In Silico Discovery of High Deliverable Capacity Metal–Organic Frameworks

(2014) Yi Bao et al.   Journal of Physical Chemistry C

In Silico Design of Three-Dimensional Porous Covalent Organic Frameworks via Known Synthesis Routes and Commercially Available Species

(2014) Richard L. Martin et al.   Journal of Physical Chemistry C

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture

(2014) Michael Fernandez et al.   Journal of Physical Chemistry Letters

The Chemistry and Applications of Metal-Organic Frameworks

(2013) H. Furukawa et al.   SCIENCE

Methane storage in advanced porous materials

(2012) Trevor A. Makal et al.   CHEMICAL SOCIETY REVIEWS

High-Throughput Characterization of Porous Materials Using Graphics Processing Units

(2012) Jihan Kim et al.   Journal of Chemical Theory and Computation

In silico screening of carbon-capture materials

(2012) Li-Chiang Lin et al.   NATURE MATERIALS

Large-scale screening of hypothetical metal–organic frameworks

(2011) Christopher E. Wilmer et al.   Nature Chemistry

A database of new zeolite-like materials

(2011) Ramdas Pophale et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS