Big-data science in porous materials: materials genomics and machine learning
By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
Material evolution with nanotechnology, nanoarchitectonics, and materials informatics: what will be the next paradigm shift in nanoporous materials?
Materials science and chemistry have played a central and significant role in advancing
society. With the shift toward sustainable living, it is anticipated that the development of …
society. With the shift toward sustainable living, it is anticipated that the development of …
Applications of machine learning in metal-organic frameworks
Abstract Machine learning (ML) is the field of computer science where computing systems
are trained to perform an analysis of provided data to reveal previously unseen trends and …
are trained to perform an analysis of provided data to reveal previously unseen trends and …
Two-dimensional energy histograms as features for machine learning to predict adsorption in diverse nanoporous materials
A major obstacle for machine learning (ML) in chemical science is the lack of physically
informed feature representations that provide both accurate prediction and easy …
informed feature representations that provide both accurate prediction and easy …
High throughput methods in the synthesis, characterization, and optimization of porous materials
Porous materials are widely employed in a large range of applications, in particular, for
storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre …
storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre …
[HTML][HTML] An ecosystem for digital reticular chemistry
The vastness of the materials design space makes it impractical to explore using traditional
brute-force methods, particularly in reticular chemistry. However, machine learning has …
brute-force methods, particularly in reticular chemistry. However, machine learning has …
High-Performing Deep Learning Regression Models for Predicting Low-Pressure CO2 Adsorption Properties of Metal–Organic Frameworks
J Burner, L Schwiedrzik, M Krykunov… - The Journal of …, 2020 - ACS Publications
Metal–organic frameworks (MOFs) have garnered interest as potential solid sorbent
materials for postcombustion CO2 capture. With a seemingly infinite design space, high …
materials for postcombustion CO2 capture. With a seemingly infinite design space, high …
[HTML][HTML] Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
Abstract Machine learning has emerged as a powerful approach in materials discovery. Its
major challenge is selecting features that create interpretable representations of materials …
major challenge is selecting features that create interpretable representations of materials …
Machine learning and descriptor selection for the computational discovery of metal-organic frameworks
K Mukherjee, YJ Colón - Molecular Simulation, 2021 - Taylor & Francis
ABSTRACT Metal-organic frameworks (MOFs), crystalline materials with high internal
surface area and pore volume, have demonstrated great potential for many applications. In …
surface area and pore volume, have demonstrated great potential for many applications. In …
Mofdiff: Coarse-grained diffusion for metal-organic framework design
Metal-organic frameworks (MOFs) are of immense interest in applications such as gas
storage and carbon capture due to their exceptional porosity and tunable chemistry. Their …
storage and carbon capture due to their exceptional porosity and tunable chemistry. Their …