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Machine learning for electronically excited states of molecules
Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …
as well as photobiology and also play a role in material science. Their theoretical description …
Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing
Over the past decade, the wide-scale adoption of artificial intelligence (AI) and machine
learning (ML) has transformed the landscape of scientific research and development, which …
learning (ML) has transformed the landscape of scientific research and development, which …
Molecular excited states through a machine learning lens
Theoretical simulations of electronic excitations and associated processes in molecules are
indispensable for fundamental research and technological innovations. However, such …
indispensable for fundamental research and technological innovations. However, such …
Machine learning in nanoscience: big data at small scales
Recent advances in machine learning (ML) offer new tools to extract new insights from large
data sets and to acquire small data sets more effectively. Researchers in nanoscience are …
data sets and to acquire small data sets more effectively. Researchers in nanoscience are …
How machine learning can help select cap** layers to suppress perovskite degradation
Environmental stability of perovskite solar cells (PSCs) has been improved by trial-and-error
exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite …
exploration of thin low-dimensional (LD) perovskite deposited on top of the perovskite …
Multitask deep-learning-based design of chiral plasmonic metamaterials
The field of chiral plasmonics has registered considerable progress with machine-learning
(ML)-mediated metamaterial prototy**, drawing from the success of ML frameworks in …
(ML)-mediated metamaterial prototy**, drawing from the success of ML frameworks in …
Hot carriers in halide perovskites: how hot truly?
Slow hot carrier cooling in halide perovskites holds the key to the development of hot carrier
(HC) perovskite solar cells. For accurate modeling and pragmatic design of HC materials …
(HC) perovskite solar cells. For accurate modeling and pragmatic design of HC materials …
Laplace transform fitting as a tool to uncover distributions of reverse intersystem crossing rates in TADF systems
Donor–acceptor (D–A) thermally activated delayed fluorescence (TADF) molecules are
exquisitely sensitive to D–A dihedral angle. Although commonly simplified to an average …
exquisitely sensitive to D–A dihedral angle. Although commonly simplified to an average …
Structural Ordering in Ultrasmall Multicomponent Chalcogenides: The Case of Quaternary Cu‐Zn‐In‐Se Nanocrystals
The compositional tunability of non‐isovalent multicomponent chalcogenide thin films and
the extent of atomic ordering of their crystal structure is key to the performance of many …
the extent of atomic ordering of their crystal structure is key to the performance of many …
Opportunities for next-generation luminescent materials through artificial intelligence
Luminescent materials are continually sought for application in solid-state LED-based
lighting and display applications. This has traditionally required extensive experimental …
lighting and display applications. This has traditionally required extensive experimental …