Person: Cubuk, Ekin
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Cubuk
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Ekin
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Cubuk, Ekin
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Publication Computational Caches(ACM Press, 2013) Waterland, Amos; Angelino, Elaine Lee; Cubuk, Ekin; Kaxiras, Efthimios; Adams, Ryan Prescott; Appavoo, Jonathan; Seltzer, MargoCaching is a well-known technique for speeding up computation. We cache data from file systems and databases; we cache dynamically generated code blocks; we cache page translations in TLBs. We propose to cache the act of computation, so that we can apply it later and in different contexts. We use a state-space model of computation to support such caching, involving two interrelated parts: speculatively memoized predicted/resultant state pairs that we use to accelerate sequential computation, and trained probabilistic models that we use to generate predicted states from which to speculatively execute. The key techniques that make this approach feasible are designing probabilistic models that automatically focus on regions of program execution state space in which prediction is tractable and identifying state space equivalence classes so that predictions need not be exact.Publication Direct Observation of a Long-Lived Single-Atom Catalyst Chiseling Atomic Structures in Graphene(American Chemical Society (ACS), 2014) Wang, Wei Li; Santos, Elton J. G.; Jiang, Bin; Cubuk, Ekin; Ophus, Colin; Centeno, Alba; Pesquera, Amaia; Zurutuza, Amaia; Ciston, Jim; Westervelt, Robert; Kaxiras, EfthimiosFabricating stable functional devices at the atomic scale is an ultimate goal of nanotechnology. In biological processes, such high-precision operations are accomplished by enzymes. A counterpart molecular catalyst that binds to a solid-state substrate would be highly desirable. Here, we report the direct observation of single Si adatoms catalyzing the dissociation of carbon atoms from graphene in an aberration-corrected high-resolution transmission electron microscope (HRTEM). The single Si atom provides a catalytic wedge for energetic electrons to chisel off the graphene lattice, atom by atom, while the Si atom itself is not consumed. The products of the chiseling process are atomic-scale features including graphene pores and clean edges. Our experimental observations and first-principles calculations demonstrated the dynamics, stability, and selectivity of such a single-atom chisel, which opens up the possibility of fabricating certain stable molecular devices by precise modification of materials at the atomic scale.Publication Investigating Non-Periodic Solids Using First Principles Calculations and Machine Learning Algorithms(2016-05-18) Cubuk, Ekin; Kaxiras, Efthimios; Adams, Ryan; Aspuru-Guzik, Alan; Vlassak, JoostComputational methods are expected to play an increasingly important role in materials design. In order to live up to these expectations, simulations need to have predictive power. To achieve this, there are two hurdles, both relating to the complexity of physical interactions. The first is the quantum mechanical interactions of ions and electrons at short timescales, which have proven difficult to simulate using classical computation. While it is now possible to model some properties and materials using first principles methods (e.g. density functional theory), accuracy, consistency and computational efficiency need to be improved to meet the demands of high-throughput materials design. The second hurdle is the difficulty of predicting the outcomes of interactions between ions at longer timescales. These interactions are central to some of the biggest mysteries in condensed matter physics, such as the glass transition. Meanwhile, the field of machine learning and artificial intelligence has seen rapid progress in the last decade. Due to improvements in hardware, software, and methodology, machine learning algorithms are now able to learn complex tasks by mastering fundamental concepts from data. Thus, this thesis explores the applicability of machine learning to the main challenges facing computational materials design. First, as a case study, we investigate the lithiation of amorphous silicon. We show that large unit cells need to be simulated to model lithium-silicon alloys accurately. By analyzing the geometric structures of local neighborhoods of silicon atoms, it is possible to explain the macroscopic behavior from microscopic signatures. In response to the first hurdle as discussed above, we train neural networks to reproduce energies of silicon structures and silicon-lithium alloys, which allows us to study much larger unit cells. We then explore silicon neural networks in detail, in order to explain how this specific machine learning architecture can model quantum mechanical interactions. The following two chapters focus on the second hurdle which arises from complex ionic configurations. By studying Lennard-Jones supercooled liquids, we try to resolve two mysteries related to supercooled liquids: 1) why the dynamics are spatially heterogeneous, and 2) why the relaxation time increases super-exponentially as the temperature is lowered. Through machine learning, we can resolve the first mystery quantitatively. Furthermore, we show that the second can also be resolved in our framework, by using empirical measurements of the machine learned representation, which we call ``softness''. Finally, we discuss the physical meaning of softness, by comparing it to other measures and applying unsupervised learning and reduced curve-fitting models.