Publication date: 17th February 2025
Machine learning is profoundly reshaping how we approach scientific research, offering new opportunities to both accelerate and enhance traditional methodologies. This shift is also significant in materials science, where the complexity of understanding material properties and optimizing manufacturing processes has long posed challenges. The application of machine learning techniques is now enabling faster discovery, more accurate predictions, and better-informed decision-making [1]. In the context of photovoltaic devices—where the efficiency of material properties directly impacts the device performance—this technological revolution is proving to be especially valuable.
In this presentation, I will delve into the application of generative models, a subset of machine learning, and their role in enhancing the speed and accuracy of numerical simulations aimed at exploring material properties, in particular dynamical properties. These machine learning models can significantly accelerate numerical simulations by identifying internal relevant material degrees of freedom [2]. Additionally, I will discuss how machine learning techniques are being used to analyze and process experimental data [3,4]. Often, experimental data can be noisy or incomplete, and integrating it with simulation results can be a complex task. However, by leveraging machine learning algorithms, it becomes possible to identify hidden patterns, fill in missing information, and reconcile experimental measurements with theoretical models. This integration enables the creation of a unified data stream that provides a comprehensive view of material performance, encompassing both simulated predictions and real-world observations. The goal of combining generative models with data analysis is to streamline the entire research process, from simulation to experimentation [5]. This approach reduces the time and resources traditionally required to design, test, and optimize new materials, especially in fields like photovoltaics, where improving efficiency is crucial. By using machine learning to accelerate these steps, we can significantly shorten the cycle time for the development of advanced materials, driving innovation in energy technologies and beyond.
nanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. Our worldwide conferences cover cutting-edge materials topics like perovskite solar cells, photovoltaics, optoelectronics, solar fuel conversion, surface science, catalysis and two-dimensional materials, among many others.
Previously nanoGe Spring Meeting (NSM) and nanoGe Fall Meeting (NFM), MATSUS is a multiple symposia conference focused on a broad set of topics of advanced materials preparation, their fundamental properties, and their applications, in fields such as renewable energy, photovoltaics, lighting, semiconductor quantum dots, 2-D materials synthesis, charge carriers dynamics, microscopy and spectroscopy semiconductors fundamentals, etc.
International Conference on Hybrid and Organic Photovoltaics
International Conference on Hybrid and Organic Photovoltaics (HOPV) is celebrated yearly in May. The main topics are the development, function and modeling of materials and devices for hybrid and organic solar cells. The field is now dominated by perovskite solar cells but also other hybrid technologies, as organic solar cells, quantum dot solar cells, and dye-sensitized solar cells and their integration into devices for photoelectrochemical solar fuel production.
Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and Optoelectronics
The main topics of the Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and Optoelectronics (IPEROP) are discussed every year in Asia-Pacific for gathering the recent advances in the fields of material preparation, modeling and fabrication of perovskite and hybrid and organic materials. Photovoltaic devices are analyzed from fundamental physics and materials properties to a broad set of applications. The conference also covers the developments of perovskite optoelectronics, including light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling.
The International Conference on Perovskite Thin Film Photovoltaics Perovskite Photonics and Optoelectronics (NIPHO) is the best place to hear the latest developments in perovskite solar cells as well as on recent advances in the fields of perovskite light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling.