scholarly journals A Data-Driven Perspective on the Colours of Metal-Organic Frameworks

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Seyed Mohamad Moosavi ◽  
Mehrdad Asgari ◽  
Christopher Ireland ◽  
Luc Patiny ◽  
...  
Keyword(s):  
Metal Organic Frameworks ◽  
Data Driven ◽  
Chemical Data ◽  
The Core ◽  
Continuous Scale ◽  
Data Driven Approach ◽  
Metal Organic ◽  
Ancient Times ◽  
Properties Of Materials ◽  
Electronic Lab Notebook

<div> <div> <div> <p>Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale, using chemically meaningful reasoning. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general—that is more suitable for a data-driven approach to chemistry. All data is captured in an electronic lab notebook and subsequently exported to a repository. </p> </div> </div> </div>

scholarly journals A Data-Driven Perspective on the Colours of Metal-Organic Frameworks

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Seyed Mohamad Moosavi ◽  
Mehrdad Asgari ◽  
Christopher Ireland ◽  
Luc Patiny ◽  
...  
Keyword(s):  
Metal Organic Frameworks ◽  
Data Driven ◽  
Chemical Data ◽  
The Core ◽  
Continuous Scale ◽  
Data Driven Approach ◽  
Metal Organic ◽  
Ancient Times ◽  
Properties Of Materials ◽  
Electronic Lab Notebook

<div> <div> <div> <p>Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale, using chemically meaningful reasoning. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general—that is more suitable for a data-driven approach to chemistry. All data is captured in an electronic lab notebook and subsequently exported to a repository. </p> </div> </div> </div>

scholarly journals A data-driven perspective on the colours of metal-organic frameworks

2021 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Berend Smit ◽  
Seyed Mohamad Moosavi ◽  
Mehrdad Asgari ◽  
Christopher Ireland ◽  
...  
Keyword(s):  
Metal Organic Frameworks ◽  
Optoelectronic Properties ◽  
Data Driven ◽  
The Core ◽  
Metal Organic ◽  
Ancient Times ◽  
Properties Of Materials

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used...

scholarly journals Crystal growth of the core and rotated epitaxial shell of a heterometallic metal-organic framework revealed with atomic force microscopy

2021 ◽  
Author(s):  
Fajar Inggit Pambudi ◽  
Michael William Anderson ◽  
Martin Attfield
Keyword(s):  
Atomic Force Microscopy ◽  
Crystal Growth ◽  
Metal Organic Framework ◽  
Metal Organic Frameworks ◽  
Force Microscopy ◽  
The Core ◽  
Atomic Force ◽  
Metal Organic ◽  
Surface Crystal ◽  
Organic Framework

Atomic force microscopy has been used to determine the surface crystal growth of two isostructural metal-organic frameworks, [Zn2(ndc)2(dabco)] (ndc = 1,4-naphthalene dicarboxylate, dabco = 4-diazabicyclo[2.2.2]octane) (1) and [Cu2(ndc)2(dabco)] (2) from...

Tuning and understanding the electronic effect of Co-Mo-O sties in bifunctional electrocatalysts for ultralong-lasting rechargable zinc-air battery

2021 ◽  
Author(s):  
Yi Guan ◽  
Nan Li ◽  
Jiao He ◽  
Yongliang Li ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Electronic Effect ◽  
Metal Organic Frameworks ◽  
Core Shell ◽  
Zeolitic Imidazolate Frameworks ◽  
Assembly Strategy ◽  
The Core ◽  
Bifunctional Electrocatalysts ◽  
Metal Organic ◽  
Air Battery

Herein, we report a post-assembly strategy by growing the bimetallic Co/Zn zeolitic imidazolate frameworks (BIMZIF) on the surface of the customized Mo metal-organic frameworks (MOFs) (Mo-MOF) to prepare the core-shell...

Contour clustering: A field-data-driven approach for documenting and analysing prototypical f0 contours

2021 ◽  
pp. 1-30
Author(s):  
Constantijn Kaland
Keyword(s):  
Cluster Analysis ◽  
User Interfaces ◽  
Data Driven ◽  
Language Documentation ◽  
Cluster Number ◽  
Automatic Data ◽  
Speaker Variability ◽  
The Core ◽  
Data Driven Approach ◽  
Confirmatory Analysis

ABSTRACT This paper reports an automatic data-driven analysis for describing prototypical intonation patterns, particularly suitable for initial stages of prosodic research and language description. The approach has several advantages over traditional ways to investigate intonation, such as the applicability to spontaneous speech, language- and domain-independency, and the potential of revealing meaningful functions of intonation. These features make the approach particularly useful for language documentation, where the description of prosody is often lacking. The core of this approach is a cluster analysis on a time-series of f0 measurements and consists of two scripts (Praat and R, available from https://constantijnkaland.github.io/contourclustering/). Graphical user interfaces can be used to perform the analyses on collected data ranging from spontaneous to highly controlled speech. There is limited need for manual annotation prior to analysis and speaker variability can be accounted for. After cluster analysis, Praat textgrids can be generated with the cluster number annotated for each individual contour. Although further confirmatory analysis is still required, the outcomes provide useful and unbiased directions for any investigation of prototypical f0 contours based on their acoustic form.

scholarly journals Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

Nature ◽  
2019 ◽  
Vol 576 (7786) ◽  
pp. 253-256 ◽  
Author(s):  
Peter G. Boyd ◽  
Arunraj Chidambaram ◽  
Enrique García-Díez ◽  
Christopher P. Ireland ◽  
Thomas D. Daff ◽  
...  
Keyword(s):  
Co2 Capture ◽  
Flue Gas ◽  
Metal Organic Frameworks ◽  
Data Driven ◽  
Metal Organic

scholarly journals Using Collective Knowledge to Assign Oxidation States

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Daniele Ongari ◽  
Seyed Mohamad Moosavi ◽  
Berend Smit
Keyword(s):  
Machine Learning ◽  
Oxidation State ◽  
Learning Algorithm ◽  
Oxidation States ◽  
Data Driven ◽  
Chemical Formula ◽  
Collective Knowledge ◽  
Metal Centre ◽  
Data Driven Approach ◽  
Metal Organic

<div><div><div><p>Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (> 98 %) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.</p></div></div></div>

scholarly journals Using Collective Knowledge to Assign Oxidation States

2020 ◽  
Author(s):  
Kevin Maik Jablonka ◽  
Daniele Ongari ◽  
Seyed Mohamad Moosavi ◽  
Berend Smit
Keyword(s):  
Machine Learning ◽  
Oxidation State ◽  
Learning Algorithm ◽  
Oxidation States ◽  
Data Driven ◽  
Chemical Formula ◽  
Collective Knowledge ◽  
Metal Centre ◽  
Data Driven Approach ◽  
Metal Organic

<div><div><div><p>Knowledge of the oxidation state of a metal centre in a material is essential to understand its properties. Chemists have developed several theories to predict the oxidation state on the basis of the chemical formula. These methods are quite successful for simple compounds but often fail to describe the oxidation states of more complex systems, such as metal-organic frameworks. In this work, we present a data-driven approach to automatically assign oxidation states, using a machine learning algorithm trained on the assignments by chemists encoded in the chemical names in the Cambridge Crystallographic Database. Our approach only considers the immediate local chemical environment around a metal centre and, in this way, is robust to most of the experimental uncertainties in these structures (like incorrect protonation or unbound solvents). We find such excellent accuracy (> 98 %) in our predictions that we can use our method to identify a large number of incorrect assignments in the database. The predictions of our model follow chemical intuition, without explicitly having taught the model those heuristics. This work nicely illustrates how powerful the collective knowledge of chemists actually is. Machine learning can harvest this knowledge and convert it into a useful tool for chemists.</p></div></div></div>

Metal-Organic Frameworks

2021 ◽  
Author(s):  
Lars Öhrström ◽  
Francoise M. Amombo Noa
Keyword(s):  
Metal Organic Frameworks ◽  
Metal Organic
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