Predicting Young’s Modulus of Linear Polyurethane and Polyurethane–Polyurea Elastomers: Bridging Length Scales with Physicochemical Modeling and Machine Learning. Pugar JA, Gang C, Huang C, Haider KW, Washburn NR. ACS Applied Materials & Interfaces. 2022 Mar 30;14(14):16568-81.
Advancing cement‐based materials design through data science approaches. Rios RT, Childs CM, Smith SH, Washburn NR, Kurtis KE. RILEM Technical Letters (2021) 6:140‐149.
Elucidating the Physicochemical Basis of the Glass Transition Temperature in Linear Polyurethane Elastomers with Machine Learning. JA Pugar, CM Childs, C Huang, KW Haider, NR Washburn. The Journal of Physical Chemistry B 124 (43), 9722-9733.
Cheminformatics for Accelerated Design of Chemical Admixtures. Childs CM, Canbek O, Kirby TM, Zhang C, Zheng J, Szeto C, Póczos B, Kurtis KE, Washburn NR. Cement and Concrete Research 136, 106173.
Embedding Domain Knowledge for Machine Learning of Complex Material Systems Based on Small Datasets. Childs CM, Washburn NR. MRS Communications, (2019) 1-5.
Molecular Engineering of Superplasticizers for Metakaolin‐Portland Cement Blends with Hierarchical Machine Learning. Menon A, Childs CM, Poczós B, Washburn NR, Kurtis KE. Advanced Theory and Simulations. 2019 Apr 1;2(4).
Elucidating Multi-Physics Interactions in Suspensions for the Design of Polymeric Dispersants: A Hierarchical Machine Learning Approach. Menon A, Gupta C, Perkins KM, DeCost BL, Budwal N, Rios RT, Zhang K, Poczos B, Washburn NR. Molecular Systems Design & Engineering. 2017; 2: 263-273.