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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.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.

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas ...

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.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.

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.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.

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas adsorption/diffusion in MOFs, the adsorbate-MOF electrostatic interaction is typically modeled by placing partial point charges on the atoms of the MOF. For the virtual screening of large libraries of MOFs, it is critical to develop computationally inexpensive methods to assign atomic partial charges to MOFs that accurately reproduce the electrostatic potential in their pores. Herein, we design and train a message passing neural network (MPNN) to predict the atomic partial charges on MOFs under a charge neutral constraint. A set of ca. 2,250 MOFs labeled with high-fidelity partial charges, derived from periodic electronic structure calculations, serves as training examples. In an end-to-end manner, from charge-labeled crystal graphs representing MOFs, our MPNN machine-learns features of the local bonding environments of the atoms and learns to predict partial atomic charges from these features. Our trained MPNN assigns high-fidelity partial point charges to MOFs with orders of magnitude lower computational cost than electronic structure calculations. To enhance the accuracy of virtual screenings of large libraries of MOFs for their adsorption-based applications, we make our trained MPNN model and MPNN-charge-assigned computation-ready, experimental MOF structures publicly available.

Purpose The purpose of this paper is to provide insights about corporate finance decision-making of Icelandic private firms that have experienced a dramatic financial crisis in 2008–2010. It observes the capital budgeting methods and cost of capital techniques for private firms after a systemic financial crisis. Moreover, the paper identifies the main determinants of capital structure and capita rationing during this period. Design/methodology/approach This paper surveys corporate finance practices of 80 out of the 250 largest bank-centred private firms after the financial crisis. Findings Highly leveraged private firms that have experienced a dramatic financial crisis in 2008–2010 use payback and net present value techniques almost at a similar rate when assessing new investments. The sample firms largely rely on the cost of debt to determine the cost of invested capital. However, capital asset pricing model is the most popular method among the few sample firms that estimate the cost of equity. The need to maintain financial flexibility and cost associated with financial distress are the most influential factors regarding capital structures, whereas investment practices avoid capital rationing associated with the financial crisis. Research limitations/implications The limitations of the study are that it is country specific and absence of data over the period before the financial crisis that may have been applied to present more insight into this topic. Practical implications Sample firms fail to incorporate appropriate cost of capital methods and as the result they are likely to apply incorrect “hurdle rate” which could undervalue or overvalue new investments. This paper indicates that capital budgeting decisions by managers of the bank-centred Icelandic private firms who tend to be major shareholders do not reflect the tendency to expropriate outside and minority investors. Private firms that have emerged from the meltdown of the financial system highlighting the importance of “special” lending relationship in assisting bank-centred firms to avoid the severity of financial constraints. Originality/value This study employs a failure of the banking system to provide new knowledge about corporate finance practices of private firms after the financial crisis that have curtailed the access to both internal and external sources of capital.