scholarly journals Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

2021 ◽  
Author(s):  
Arni Sturluson ◽  
Ali Raza ◽  
Grant D. McConachie ◽  
Daniel W. Siderius ◽  
Xiaoli Z. Fern ◽  
...  
Keyword(s):  
Recommendation System ◽  
Nanoporous Materials ◽  
Adsorption Properties

scholarly journals A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

2021 ◽  
Author(s):  
Arni Sturluson ◽  
Ali Raza ◽  
Grant D. McConachie ◽  
Daniel Siderius ◽  
Xiaoli Fern ◽  
...  
Keyword(s):  
Recommendation System ◽  
Adsorption Property ◽  
Property Values ◽  
Recommendation Systems ◽  
Nanoporous Materials ◽  
Adsorption Properties ◽  
Low Rank ◽  
Covalent Organic Frameworks ◽  
Partially Observed ◽  
Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs.<br><br>The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties. <br>

scholarly journals A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

2021 ◽  
Author(s):  
Arni Sturluson ◽  
Ali Raza ◽  
Grant D. McConachie ◽  
Daniel Siderius ◽  
Xiaoli Fern ◽  
...  
Keyword(s):  
Recommendation System ◽  
Adsorption Property ◽  
Property Values ◽  
Recommendation Systems ◽  
Nanoporous Materials ◽  
Adsorption Properties ◽  
Low Rank ◽  
Covalent Organic Frameworks ◽  
Partially Observed ◽  
Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs.<br><br>The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties. <br>

A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

2021 ◽  
Author(s):  
Arni Sturluson ◽  
Ali Raza ◽  
Grant D. McConachie ◽  
Daniel Siderius ◽  
Xiaoli Fern ◽  
...  
Keyword(s):  
Recommendation System ◽  
Adsorption Property ◽  
Property Values ◽  
Recommendation Systems ◽  
Nanoporous Materials ◽  
Adsorption Properties ◽  
Low Rank ◽  
Covalent Organic Frameworks ◽  
Partially Observed ◽  
Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs.<br><br>The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties. <br>

scholarly journals Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

2021 ◽  
Author(s):  
Arni Sturluson ◽  
Ali Raza ◽  
Grant D. McConachie ◽  
Daniel Siderius ◽  
Xiaoli Fern ◽  
...  
Keyword(s):  
Recommendation System ◽  
Adsorption Property ◽  
Property Values ◽  
Recommendation Systems ◽  
Nanoporous Materials ◽  
Adsorption Properties ◽  
Low Rank ◽  
Covalent Organic Frameworks ◽  
Partially Observed ◽  
Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs.<br /><br />The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, represented as points, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties. <br />

scholarly journals A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

2021 ◽  
Author(s):  
Arni Sturluson ◽  
Ali Raza ◽  
Grant D. McConachie ◽  
Daniel Siderius ◽  
Xiaoli Fern ◽  
...  
Keyword(s):  
Recommendation System ◽  
Adsorption Property ◽  
Property Values ◽  
Recommendation Systems ◽  
Nanoporous Materials ◽  
Adsorption Properties ◽  
Low Rank ◽  
Covalent Organic Frameworks ◽  
Partially Observed ◽  
Different Gases

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores, and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of the relevant adsorption properties in the candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice the NPM-property matrix is only partially observed (incomplete); (i) many properties of any given NPM have not been measured and (ii) any given property has not been measured for all NPMs.<br><br>The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete item-customer ratings matrix to recommend items to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low rank model of an incomplete COF--adsorption-property matrix. A low rank model, trained on the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a "map" of COFs, represented as points, wherein COFs with similar (dissimilar) adsorption properties congregate (separate). We find the performance of the COF recommendation system varies for different adsorption tasks and diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to many different materials and properties. <br>

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

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