Twisto-electrochemical activity volcanoes in Trilayer Graphene

In this work, we develop a twist-dependent electrochemical activity map, combining a tight-binding electronic structure model with modified Marcus-Hush-Chidsey kinetics in trilayer graphene. We identify a counterintuitive rate enhancement region spanning the magic angle curve and incommensurate twists of the system geometry. We find a broad activity peak with a ruthenium hexamine redox couple in regions corresponding to both magic angles and incommensurate angles, a result qualitatively distinct from the twisted bilayer case. Flat bands and incommensurability offer new avenues for reaction rate enhancements in electrochemical transformations.Comment: 6 pages, 4 figures, Supporting Informatio

ACED: Accelerated Computational Electrochemical systems Discovery

Large-scale electrification is vital to addressing the climate crisis, but many engineering challenges remain to fully electrifying both the chemical industry and transportation. In both of these areas, new electrochemical materials and systems will be critical, but developing these systems currently relies heavily on computationally expensive first-principles simulations as well as human-time-intensive experimental trial and error. We propose to develop an automated workflow that accelerates these computational steps by introducing both automated error handling in generating the first-principles training data as well as physics-informed machine learning surrogates to further reduce computational cost. It will also have the capacity to include automated experiments "in the loop" in order to dramatically accelerate the overall materials discovery pipeline.Comment: 4 pages, 1 figure, accepted to NeurIPS Climate Change and AI Workshop 2020, updating acknowledgements and citation

Computational frameworks to enable accelerated development of defect-tolerant photovoltaic materials

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2019Cataloged from PDF version of thesis.Includes bibliographical references (pages 103-112).Widespread adoption of carbon-free energy technologies, including and especially photovoltaics (PV), is vital to address the issue of climate change. Absent sweeping policy action, this will only happen if these technologies become the most economically viable energy source. While PV has become cheaper in recent years, technoeconomic modeling suggests that current PV technologies cannot come down in price enough to be deployed at the scale required by future energy-mix scenarios that avert catastrophic climate change. This issue can be addressed by developing new technologies - in particular, ones that rely on materials that can be manufactured at drastically cheaper costs than present-day ones. To perform well in a PV device, silicon (the active material in approximately 90% of devices on the market today) must be free of detrimental metallic impurities at levels of parts per billion; the process to achieve this purity requires expensive equipment and large energy expenditures.In contrast, hybrid halide perovskites (a new class of PV materials developed in the past decade) are extremely defect-tolerant. Synthesized using solution-based methods at ambient temperatures and pressures, they contain orders of magnitude more defects and yet achieve power conversion efficiencies comparable to silicon-based devices. Unfortunately, these materials suffer from lack of long-term stability as well as concerns surrounding toxicity since all high-performing variants to date contain lead. This work centers on accelerating the process of discovering other defect-tolerant materials that would share the remarkable optoelectronic properties of the perovskites without suffering these drawbacks, and focuses on two particular areas. The first is aimed at understanding the atomic-scale physics enabling the defect-tolerant behavior of the perovskites in order to formulate screening/design criteria for new materials.The primary reason that perovskites perform so well even in the presence of defects is that the energy states due to the most abundant defects are all shallow in nature, i.e., close in energy to the band edges, and hence contribute very little to nonradiative recombination current losses. I identified several novel mechanisms for this behavior that have strong explanatory power for systems that have been examined in detail; this improved understanding also promises to aid in prediction of future compounds. To complement the theoretical/computational identification of new candidate materials, the second thrust of this thesis is accelerating their experimental characterization.I have developed open-source software that enables the use of high-throughput experimental measurements (e.g., photocurrent as a function of voltage, temperature, and light intensity), in concert with device simulation run on high-performance computers and Bayesian parameter estimation, to construct probability distributions over unknown input parameters of those device simulations. This enables extraction of multiple parameters (in realistic, device-relevant contexts) from a single set of inexpensive, automatable measurements. This approach has the potential to supplant traditional direct characterization methods, which can be time-consuming and subject to confounding factors such as different sample preparation requirements. Taken together, these two primary thrusts can dramatically accelerate PV materials discovery.First, we reduce the search space of materials by defining better selection criteria, focusing limited experimental bandwidth on only the most promising candidate compounds. Second, once a material has been synthesized, it can be characterized and optimized rapidly through the Bayesian inference technique. While I have focused primarily on PV materials, many aspects of this work could be applicable in a broader array of energy materials studies such as batteries or thermoelectrics. If we can speed up the process of discovering and developing new materials systems, then we can speed up the adoption of the resulting energy technologies, thereby lowering costs, reducing emissions, and improving lives.by Rachel Chava Kurchin.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Materials Science and Engineerin

Bayesim: A tool for adaptive grid model fitting with Bayesian inference

Bayesian inference is a widely used and powerful analytical technique in fields such as astronomy and particle physics but has historically been underutilized in some other disciplines including semiconductor devices. In this work, we introduce bayesim, a Python package that utilizes adaptive grid sampling to efficiently generate a probability distribution over multiple input parameters to a forward model using a collection of experimental measurements. We discuss the implementation choices made in the code, showcase two examples in photovoltaics, and discuss general prerequisites for the approach to apply to other systems. Program summary: Program Title: Bayesim Program Files doi: http://dx.doi.org/10.17632/fch5m6p3nn.1 Licensing provisions: GNU General Public License 2 Programming language: Python 3 Supplementary material: uncertainty_figure.png Nature of problem: Many parameters in forward numerical models, e.g. for photovoltaic (PV) device behavior, are difficult to measure via direct experiment. In addition, in early-stage PV materials (and many other systems of interest), there are many unknown parameters and a desire to know their values in a small amount of time to enable high-throughput materials screening, making the time investment for direct measurement prohibitive. However, measurement of electrical properties of devices is comparatively easy, cheap, and automatable. Solution method: We employ Bayesian inference to “invert” the device models using the high-throughput experimental data, running the model forward with many combinations of parameters and generating a probability distribution over the inputs. This enables fitting of a number of parameters limited only by the quantity of experimental data and computational power available. The code is available open-source as a Python package and includes features such as adaptive grid fitting, model uncertainty calculation, and a variety of visualization options. Additional comments: The code is freely available on Github (https://github.com/pv-lab/bayesim) and thoroughly documented online (https://pv-lab.github.io/bayesim/_build/html/index.html). ©2019 Elsevier B.V

How Much Physics is in a Current-Voltage Curve? Inferring Defect Properties from Photovoltaic Device Measurements

Defect-assisted recombination processes are critical to understand, as they frequently limit photovoltaic (PV) device performance. However, the physical parameters governing these processes can be extremely challenging to measure, requiring specialized techniques and sample preparation. And yet the fact that they limit performance as measured by current-voltage (JV) characterization indicates that they must have some detectable signal in that measurement. In this work, we use numerical device models that explicitly account for these parameters with high-throughput JV measurements and Bayesian inference to construct probability distributions over recombination parameters, showing the ability to recover values consistent with previously-reported literature measurements. The Bayesian approach enables easy incorporation of data and models from other sources; we demonstrate this with temperature dependence of carrier capture cross-sections. The ability to extract these fundamental physical parameters from standardized, automated measurements on completed devices is promising for both established industrial PV technologies and newer research-stage ones.Peer reviewe

Economically sustainable scaling of photovoltaics to meet climate targets

To meet climate targets, power generation capacity from photovoltaics (PV) in 2030 will have to be much greater than is predicted from either steady state growth using today's manufacturing capacity or industry roadmaps. Analysis of whether current technology can scale, in an economically sustainable way, to sufficient levels to meet these targets has not yet been undertaken, nor have tools to perform this analysis been presented. Here, we use bottom-up cost modeling to predict cumulative capacity as a function of technological and economic variables. We find that today’s technology falls short in two ways: profits are too small relative to upfront factory costs to grow manufacturing capacity rapidly enough to meet climate targets, and costs are too high to generate enough demand to meet climate targets. We show that decreasing the capital intensity (capex) of PV manufacturing to increase manufacturing capacity and effectively reducing cost (e.g., through higher efficiency) to increase demand are the most effective and least risky ways to address these barriers to scale. We also assess the effects of variations in demand due to hard-to-predict factors, like public policy, on the necessary reductions in cost. Finally, we review examples of redundant technology pathways for crystalline silicon PV to achieve the necessary innovations in capex, performance, and price.United States. Department of Energy. Office of Energy Efficiency and Renewable Energy (NSF Cooperative Agreement No. EEC-1041895)United States. Department of Defense (American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowship