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Interpretations of pre-collisional configurations of Greater India are highly controversial and predict distinct processes during the India-Asia collision. To better determine the possible pre-collisional configuration(s) of Greater India, we conduct a mass-balance analysis combined with previously published geologic, paleomagnetic, and geodynamic evidence. The mass-balance analysis determines the magnitude of northern Greater India (NGI) width needed to provide sufficient crustal accretion to form the Tethyan-Greater Himalaya orogenic wedge in Cenozoic time. Applying endmember crustal thicknesses of 10–40 km to a mass-balance equation yields a broad range of plausible pre-collisional NGI widths of ∼3016±1000 km and ∼956±283 km, respectively, which we further assess considering contrasting models/evidence. The integrated evidence requires a thin NGI continental crust to form 1) continuous Tethyan-Greater Himalayan crustal thickening, 2) a narrow foredeep width of Himalayan foreland basin, 3) continuous Gangdese arc magmatism with oceanic-subduction-style mantle wedge, and 4) low-magnitude exhumation in the North Himalaya and Gangdese arc-forearc from ∼60-30 Ma. Adding the structurally restored ∼740 km wide southern Greater India, the synthesized analyses yield two possible configurations: 1) an ∼1350±440 km wide and ∼23-30 km thick NGI indicating an ∼2080±450 km wide Greater India with ∼500-1000 km wide oceanic basin systems in both Asia and NGI; and 2) a ≥1815±630 km wide and ∼10-23 km thick Zealandia-type NGI indicating a ≥2550±640 km wide pre-collisional Greater India without or with limited ∼500-1000 km Xigaze back-arc oceanic basin. The former is conditionally consistent with the integrated evidence by assuming no Cenozoic oceanic subduction initiation within NGI and predicts multi-stage collision since ∼60 Ma. The latter is consistent with the integrated evidence and predicts an approximate-single-stage collision at ∼60 Ma. Both configurations predict significant post-collisional NGI crustal shortening that may have been accommodated by the Eocene-Oligocene Greater Himalayan structural discontinuities. 

The development of reliable, yet computationally efficient interatomic forcefields is key to facilitate the modeling of glasses. However, the parameterization of novel forcefields is challenging as the high number of parameters renders traditional optimization methods inefficient or subject to bias. Here, we present a new parameterization method based on machine learning, which combines ab initio molecular dynamics simulations and Bayesian optimization. By taking the example of glassy silica, we show that our method yields a new interatomic forcefield that offers an unprecedented agreement with ab initio simulations. This method offers a new route to efficiently parameterize new interatomic forcefields for disordered solids in a non-biased fashion.