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Utility-Scale Shared Energy Storage: Business models for utility-scale shared energy storage systems and customer participation

https://doi.org/10.1109/MELE.2021.3115558

Ben-Idris, Mohammed ; Brown, Michael ; Egan, Matthew ; Huang, Zhenyu ; Mitra, Joydeep ( December 2021 , IEEE Electrification Magazine)

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Automatic Routability Predictor Development Using Neural Architecture Search

https://doi.org/10.1109/ICCAD51958.2021.9643483

Chang, Chen-Chia ; Pan, Jingyu ; Zhang, Tunhou ; Xie, Zhiyao ; Hu, Jiang ; Qi, Weiyi ; Lin, Chun-Wei ; Liang, Rongjian ; Mitra, Joydeep ; Fallon, Elias ; et al ( November 2021 , International Conference on Computer-Aided Design (ICCAD))

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A user-friendly application for predicting the outcome of co-crystallizations

https://doi.org/10.1039/d0ce01074j

Sarkar, Nandini ; Mitra, Joydeep ; Vittengl, Molly ; Berndt, Lexi ; Aakeröy, Christer B. ( October 2020 , CrystEngComm)
null (Ed.)
An automated application, CoForm, was used for predicting the outcomes of attempted co-crystallizations between two active pharmaceutical ingredients, loratadine and desloratadine, and 41 potential co-formers from the general interest (OGI) list. The predictive abilities of the app were compared to structure-informatics tools based on hydrogen-bond propensity (HBP) and molecular complementarity (MC). The results indicate that CoForm delivered a success rate of 78% for both loratadine and desloratadine compared to 76% and 54%, respectively (HBP), and 39% and 22%, respectively (MC).
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