Decreasing the Computing Time of Bayesian Optimization using Generalizable Memory Pruning

Bayesian optimization (BO) suffers from long computing times when processing highly-dimensional or large data sets. These long computing times are a result of the Gaussian process surrogate model having a polynomial time complexity with the number of experiments. Running BO on high-dimensional or massive data sets becomes intractable due to this time complexity scaling, in turn, hindering experimentation. Alternative surrogate models have been developed to reduce the computing utilization of the BO procedure, however, these methods require mathematical alteration of the inherit surrogate function, pigeonholing use into only that function. In this paper, we demonstrate a generalizable BO wrapper of memory pruning and bounded optimization, capable of being used with any surrogate model and acquisition function. Using this memory pruning approach, we show a decrease in wall-clock computing times per experiment of BO from a polynomially increasing pattern to a sawtooth pattern that has a non-increasing trend without sacrificing convergence performance. Furthermore, we illustrate the generalizability of the approach across two unique data sets, two unique surrogate models, and four unique acquisition functions. All model implementations are run on the MIT Supercloud state-of-the-art computing hardware.Comment: Accepted as a paper in IEEE HPEC 202

Meeting Global Cooling Demand with Photovoltaics during the 21st Century

Space conditioning, and cooling in particular, is a key factor in human productivity and well-being across the globe. During the 21st century, global cooling demand is expected to grow significantly due to the increase in wealth and population in sunny nations across the globe and the advance of global warming. The same locations that see high demand for cooling are also ideal for electricity generation via photovoltaics (PV). Despite the apparent synergy between cooling demand and PV generation, the potential of the cooling sector to sustain PV generation has not been assessed on a global scale. Here, we perform a global assessment of increased PV electricity adoption enabled by the residential cooling sector during the 21st century. Already today, utilizing PV production for cooling could facilitate an additional installed PV capacity of approximately 540 GW, more than the global PV capacity of today. Using established scenarios of population and income growth, as well as accounting for future global warming, we further project that the global residential cooling sector could sustain an added PV capacity between 20-200 GW each year for most of the 21st century, on par with the current global manufacturing capacity of 100 GW. Furthermore, we find that without storage, PV could directly power approximately 50% of cooling demand, and that this fraction is set to increase from 49% to 56% during the 21st century, as cooling demand grows in locations where PV and cooling have a higher synergy. With this geographic shift in demand, the potential of distributed storage also grows. We simulate that with a 1 m$^3$ water-based latent thermal storage per household, the fraction of cooling demand met with PV would increase from 55% to 70% during the century. These results show that the synergy between cooling and PV is notable and could significantly accelerate the growth of the global PV industry

Stress effects on the Raman spectrum of an amorphous material: theory and experiment on a-Si:H

Strain in a material induces shifts in vibrational frequencies, which is a probe of the nature of the vibrations and interatomic potentials, and can be used to map local stress/strain distributions via Raman microscopy. This method is standard for crystalline silicon devices, but due to lack of calibration relations, it has not been applied to amorphous materials such as hydrogenated amorphous silicon (a-Si:H), a widely studied material for thin-film photovoltaic and electronic devices. We calculated the Raman spectrum of a-Si:H \ab initio under different strains $\epsilon$ and found peak shifts $\Delta \omega = \left( -460 \pm 10\ \mathrm{cm}^{-1} \right) {\rm Tr}\ \epsilon$. This proportionality to the trace of the strain is the general form for isotropic amorphous vibrational modes, as we show by symmetry analysis and explicit computation. We also performed Raman measurements under strain and found a consistent coefficient of $-510 \pm 120\ \mathrm{cm}^{-1}$. These results demonstrate that a reliable calibration for the Raman/strain relation can be achieved even for the broad peaks of an amorphous material, with similar accuracy and precision as for crystalline materials.Comment: 12 pages, 3 figures + supplementary 8 pages, 4 figure

Assessing the Device-performance Impacts of Structural Defects with TCAD Modeling

Advanced solar cell architectures like passivated emitter and rear (PERC) and heterojunction with intrinsic thin layer (HIT) are increasingly sensitive to bulk recombination. Present device models consider homogeneous bulk lifetime, which does not accurately reflect the effects of heterogeneously distributed defects. To determine the efficiency potential of multicrystalline silicon (mc-Si) in next-generation architectures, we present a higher-dimensional numerical simulation study of the impacts of structural defects on solar cell performance. We simulate these defects as an interfacial density of traps with a single mid-gap energy level using Shockley-Read-Hall (SRH) statistics. To account for enhanced recombination at the structural defects, we apply a linear scaling to the majority-carrier capture cross-section and scale the minority-carrier capture cross-section with the inverse of the line density of traps. At 300 K, our simulations of carrier occupation and recombination rate match literature electron-beam-induced current (EBIC) data and first-principles calculations of carrier capture, emission, and recombination for all the energy levels associated with dislocations decorated with metal impurities. We implement our model in Sentaurus Device, determining the losses across different device architectures for varying impurity decoration of grain boundaries.DoD/National Defense Science & Engineering Graduate Fellowship (NDSEG

Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)

Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. For example, only $0.82\%$ out of $146$k total materials in the open-access Materials Project database have a negative Poisson's ratio. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence to a global optimum or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI. ZoMBI actively extracts knowledge from the previously best-performing evaluated experiments to iteratively zoom in the sampling search bounds towards the global optimum "needle" and then prunes the memory of low-performing historical experiments to accelerate compute times by reducing the algorithm time complexity from $O(n^3)$ to $O(\phi^3)$ for $\phi$ forward experiments per activation, which trends to a constant $O(1)$ over several activations. Additionally, ZoMBI implements two custom adaptive acquisition functions to further guide the sampling of new experiments toward the global optimum. We validate the algorithm's optimization performance on three real-world datasets exhibiting Needle-in-a-Haystack and further stress-test the algorithm's performance on an additional 174 analytical datasets. The ZoMBI algorithm demonstrates compute time speed-ups of 400x compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3x more highly optimized than those discovered by similar methods MiP-EGO, TuRBO, and HEBO.Comment: Paper 16 pages; SI 6 page

Ten-percent solar-to-fuel conversion with nonprecious materials

Direct solar-to-fuels conversion can be achieved by coupling a photovoltaic device with water-splitting catalysts. We demonstrate that a solar-to-fuels efficiency (SFE) > 10% can be achieved with nonprecious, low-cost, and commercially ready materials. We present a systems design of a modular photovoltaic (PV)–electrochemical device comprising a crystalline silicon PV minimodule and low-cost hydrogen-evolution reaction and oxygen-evolution reaction catalysts, without power electronics. This approach allows for facile optimization en route to addressing lower-cost devices relying on crystalline silicon at high SFEs for direct solar-to-fuels conversion.National Science Foundation (U.S.). Faculty Early Career Development Program (ECCS-1150878)Singapore. National Research Foundation (Singapore-MIT Alliance for Research and Technology. Low Energy Electronic Systems Research Program)Chesonis Family Foundatio

Meeting Global Cooling Demand with Photovoltaics during the 21st Century

Space conditioning, and cooling in particular, is a key factor in human productivity and well-being across the globe. During the 21st century, global cooling demand is expected to grow significantly due to the increase in wealth and population in sunny nations across the globe and the advance of global warming. The same locations that see high demand for cooling are also ideal for electricity generation via photovoltaics (PV). Despite the apparent synergy between cooling demand and PV generation, the potential of the cooling sector to sustain PV generation has not been assessed on a global scale. Here, we perform a global assessment of increased PV electricity adoption enabled by the residential cooling sector during the 21st century. Already today, utilizing PV production for cooling could facilitate an additional installed PV capacity of approximately 540 GW, more than the global PV capacity of today. Using established scenarios of population and income growth, as well as accounting for future global warming, we further project that the global residential cooling sector could sustain an added PV capacity between 20-200 GW each year for most of the 21st century, on par with the current global manufacturing capacity of 100 GW. Furthermore, we find that without storage, PV could directly power approximately 50% of cooling demand, and that this fraction is set to increase from 49% to 56% during the 21st century, as cooling demand grows in locations where PV and cooling have a higher synergy. With this geographic shift in demand, the potential of distributed storage also grows. We simulate that with a 1 m$^3$ water-based latent thermal storage per household, the fraction of cooling demand met with PV would increase from 55% to 70% during the century. These results show that the synergy between cooling and PV is notable and could significantly accelerate the growth of the global PV industry