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Developing a solar cell simulator based on JAX adsa
QI: MIT Quest for Intelligence
Computational science is playing a prominent role in developing technologies for climate change mitigation. In fact, the predicting power of first-principles calculations, combined with the ever-increasing computing capability, enables fast material and device screening. To further boosting inverse design, novel methods are tapping into machine learning, where some concepts are readily usable. For example, once the a surrogate model based on artificial neural networks has been developed, the well-known backpropagation algorithm can aid the material search Within this context, our group is developing a solar cell simulator based on JAX, a novel Python-based framework which facilitates hardware-agnostic code and automatic differentiation. The aim of this tool is to provide an effective solver which can be seemlessly integrated in ML-based inverse design workflow. The UROP will help develop such a tool and use it for building a surrogate model based on artificial neural networks.
Fluency in Python and basic of calculus are required.