Related papers: Autonomous Optimization of an Organic Solar Cell i…
This paper gives a thorough overview of Solar Car Optimized Route Estimation (SCORE), novel route optimization scheme for solar vehicles based on solar irradiance and target distance. In order to conduct the optimization, both data…
Chemical process optimization maximizes production efficiency and economic performance, but optimization algorithms, including gradient-based solvers, numerical methods, and parameter grid searches, become impractical when operating…
This paper presents and evaluates the performance of an optimal scheduling algorithm that selects the on/off combinations and timing of a finite set of dynamic electric loads on the basis of short term predictions of the power delivery from…
Particle-in-cell simulations are among the most essential tools for the modeling and optimization of laser-plasma accelerators, since they reproduce the physics from first principles. However, the high computational cost associated with…
Autonomous 3D acquisition of outdoor environments poses special challenges. Different from indoor scenes, where the room space is delineated by clear boundaries and separations (e.g., walls and furniture), an outdoor environment is spacious…
This paper presents a theoretical interpretation and explores possible improvements of a widely adopted rule-based control for residential solar photovoltaics (PV) paired with battery storage systems (BSS). The method is referred to as…
The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexity and literally infinite diversity of conjugated molecular…
Bayesian optimization (BO) has shown impressive results in a variety of applications within low-to-moderate dimensional Euclidean spaces. However, extending BO to high-dimensional settings remains a significant challenge. We address this…
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces. By adapting ideas from deep metric learning, we use label…
While renewable energies are achieving parity around the globe, efforts to reach higher solar cell efficiencies becomes ever more difficult as they approach the limiting efficiency. The so-called third generation concepts attempt to break…
Organic heterojunction solar cells are analyzed within a minimal model that includes the essential physical features of such systems. The dynamical properties of this model, calculated using a master equation approach, account for the…
Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. However, BO is practically limited to…
The paper presents a topology optimization approach that designs an optimal structure, called a self-supporting structure, which is ready to be fabricated via additive manufacturing without the usage of additional support structures. Such…
With the decline of fossil fuel reserves and the escalating global average temperature, the quest for environmentally friendly and renewable energy sources has gained significant momentum. Focus has turned to wind and photovoltaic energy,…
Photovoltaics (PV) are a versatile and compact route to harness solar power. One critical challenge with current PV is persevering the optimal panel orientation angle with respect to the sun for efficient energy conversion. We…
AI-powered autonomous experimentation (AI/AE) can accelerate materials discovery but its effectiveness for electronic materials is hindered by data scarcity from lengthy and complex design-fabricate-test-analyze cycles. Unlike experienced…
Bayesian optimization has emerged as a powerful strategy to accelerate scientific discovery by means of autonomous experimentation. However, expensive measurements are required to accurately estimate materials properties, and can quickly…
Organic photovoltaics (OPVs) are promising candidates for future sustainable technologies, including applications within the renewable energy sector, such as solar cells and indoor light recycling, and photodetection. However, the…
Organic photovoltaics (OPVs) are promising candidates for solar-energy conversion, with device efficiencies continuing to increase. However, the precise mechanism of how charges separate in OPVs is not well understood because low dielectric…
We introduce dPV, an end-to-end differentiable photovoltaic (PV) cell simulator based on the drift-diffusion model and Beer-Lambert law for optical absorption. dPV is programmed in Python using JAX, an automatic differentiation (AD) library…