Opportunities for Next-Generation Luminescent Materials through Artificial Intelligence

Molecular excited states through a machine learning lens

“…Training ML models for the prediction of excited-state properties can aid the search of optoelectronic materials. 178,179 For example, ML trained on excitation energies of fluorescent molecules can be used not only for emission spectrum simulation but also for the design of materials that emit at a required wavelength for application as LED or a fluorescent label in cell imaging. [47][48][49] More often than not, the compounds that are intended to be used in complex photodevices are selected based on macroscopic properties such as power conversion efficiency (PCE) or fluorescence quantum yield that ultimately depend on atomic-scale molecular excited-state properties.…”

Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in The Upstream of Development of Novel Energy Technologies

“…determine directly solar cell performance. Machine learning is now more and more often used to help design better solar cells and specifically materials for solar cells and other optoelectronic applications [12][13][14][15]65,66]. It is used to predict better active materials, to optimize device performance or even optimize fabrication processes [15].…”

“…One area where ML is gaining more and more traction are novel energy conversion and storage technologies. These techniques are, in particular, intensely explored for application to the development of technologies typically associated with sustainable generation and use of energy such as advanced types (organic and inorganic materials based) of solar cells and LED (light-emitting diodes) [10][11][12][13][14][15][16][17][18][19][20][21][22], inorganic and organic metal ion batteries [23,24], fuel cells and generally heterogeneous catalysis including electro-and photocatalysis [25][26][27][28][29][30][31][32][33][34]. This is natural in the sense that the development of these technologies often passes through optimization and balancing of multiple factors acting simultaneously and to opposite ends; for example, in the case of organic solar cells, there is an optimum to be sought between the donor's bandgap, the band offset between the donor and the acceptor, the reorganization energies of both the donor and the acceptor, the charge transfer integral etc.…”

Local Structure Modulation-Induced Highly Efficient Red-Emitting Ba2Gd1–xYxNbO6:Mn4+ Phosphors for Warm WLEDs

“…Nowadays, artificial light sources have tremendously prolonged daytime and changed our lifestyle. White light-emitting diodes (WLEDs) are becoming prevalent among artificial light sources because of the prominent merits, including low power consumption, miniaturization, lasting durability, and environmental friendliness. − Phosphors play a vital role in phosphor-converted WLEDs. However, there are still weaknesses of high correlated color temperature (CCT) and low color rendering index (CRI) in the existing commercial WLEDs coupling Y 3 Al 5 O 12 :Ce 3+ (YAG:Ce 3+ ) phosphors with blue InGaN chips. , Significantly, the introduction of red luminescence phosphors is an effective and available strategy to solve the above problems. , Various red-emitting phosphors have been developed so far, such as Eu 3+ -/Sm 3+ -/Eu 2+ -/Ce 3+ -doped phosphors. , However, these phosphors still have several drawbacks, such as the expensive dopants of rare-earth elements, low color purity, and harsh reaction conditions .…”