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Machine learning meets with metal organic frameworks for gas storage and separation
C Altintas, OF Altundal, S Keskin… - Journal of Chemical …, 2021 - ACS Publications
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to
focus on high-throughput computational screening (HTCS) methods to quickly assess the …
focus on high-throughput computational screening (HTCS) methods to quickly assess the …
Making the collective knowledge of chemistry open and machine actionable
KM Jablonka, L Patiny, B Smit - Nature Chemistry, 2022 - nature.com
Large amounts of data are generated in chemistry labs—nearly all instruments record data
in a digital form, yet a considerable proportion is also captured non-digitally and reported in …
in a digital form, yet a considerable proportion is also captured non-digitally and reported in …
The role of machine learning in the understanding and design of materials
SM Moosavi, KM Jablonka, B Smit - Journal of the American …, 2020 - ACS Publications
Develo** algorithmic approaches for the rational design and discovery of materials can
enable us to systematically find novel materials, which can have huge technological and …
enable us to systematically find novel materials, which can have huge technological and …
Diversifying databases of metal organic frameworks for high-throughput computational screening
S Majumdar, SM Moosavi, KM Jablonka… - … applied materials & …, 2021 - ACS Publications
By combining metal nodes and organic linkers, an infinite number of metal organic
frameworks (MOFs) can be designed in silico. Therefore, when making new databases of …
frameworks (MOFs) can be designed in silico. Therefore, when making new databases of …
Machine learning accelerates the investigation of targeted MOFs: performance prediction, rational design and intelligent synthesis
Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …
used in various emerging fields due to their large specific surface area, high porosity and …
An ecosystem for digital reticular chemistry
KM Jablonka, AS Rosen, AS Krishnapriyan, B Smit - 2023 - ACS Publications
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 …
How reproducible is the synthesis of Zr–porphyrin metal–organic frameworks? An interlaboratory study
HLB Boström, S Emmerling, F Heck… - Advanced …, 2024 - Wiley Online Library
Metal–organic frameworks (MOFs) are a rapidly growing class of materials that offer great
promise in various applications. However, the synthesis remains challenging: for example, a …
promise in various applications. However, the synthesis remains challenging: for example, a …
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to
reveal predictive structure–property relationships. For many properties of interest in …
reveal predictive structure–property relationships. For many properties of interest in …
Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective
Metal–organic frameworks (MOFs) have attracted tremendous interest because of their
tunable structures, functionalities, and physiochemical properties. The nearly infinite …
tunable structures, functionalities, and physiochemical properties. The nearly infinite …
Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF
NP Domingues, SM Moosavi, L Talirz… - Communications …, 2022 - nature.com
The synthesis of metal-organic frameworks (MOFs) is often complex and the desired
structure is not always obtained. In this work, we report a methodology that uses a joint …
structure is not always obtained. In this work, we report a methodology that uses a joint …