Random Pinning Glass Model
  Glass transition where viscosity of liquids increases dramatically upon decrease of temperature without any major change in structural properties, remains one of the most challenging problems in condensed matter physics (Cavagna, 2009; Berthier and Biroli, 2011) in spite of tremendous research efforts in last decades. On the other hand disordered freezing of spins in a magnetic materials with decreasing temperature, the so-called spin glass transition, is relatively better understood (Mezard, Parisi and Virasoro, 1987; Castellani and Cavagna, 2005). Previously found similarity between some spin glass models with the structural glasses (Kirkpatrick and Thirumalai, 1987; Kirkpatrick and Wolynes, 1987; Kirkpatrick and Wolynes, 1987; Franz and Parisi, 1999; Moore and Drossel, 2002) inspired development of theories of structural glasses (Kirkpatrick, Thirumalai and Wolynes, 1989; Barrat, Franz and Parisi, 1997; M\'ezard and Parisi, 1999; Lubchenko and Wolynes, 2007; Biroli and Bouchaud, 2012) based on the scenario of spin glass transition. This scenario though looks very appealing is still far from being well established. One of the main differences between standard spin systems to molecular systems is the absence of quenched disorder and the presence of translational invariance: it often assumed that this difference is not relevant, but this conjecture is still far from being established. The quantities, which are well defined and characterized for spin models, are not easily calculable for molecular glasses due to the lack of quenched disorder which breaks the translational invariance in the system and the characterization of the similarity between the spin and the structural glass transition remained an elusive subject still now. In this study we introduced a model structural glass with built in quenched disorder which alleviates this main difference between the spin and molecular glasses thereby helping us to compare these two systems: the possibility of producing a good thermalization at rather low temperatures is one of the advantages of this model. 
Smarajit Karmakar and Giorgio Parisi
cond-mat.stat-mech cond-mat.dis-nn