Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Organic–inorganic metal halide perovskite solar cells represent the fastest advancing solar
cell technology in terms of energy conversion efficiency improvement, as seen in the last …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Abstract Machine learning models of material properties accelerate materials discovery,
reproducing density functional theory calculated results at a fraction of the cost,,,,–. To bridge …

Generative models trained on internet-scale data are capable of generating novel and
realistic texts, images, and videos. A natural next question is whether these models can …

Search for resource-efficient materials in vast compositional spaces is an outstanding
challenge in creating environmentally stable perovskite semiconductors. We demonstrate a …

Metal halide perovskites (MHPs) have catapulted to the forefront of energy research due to
the unique combination of high device performance, low materials cost, and facile solution …

Metal halide perovskites have attracted immense interest as a promising material for a
variety of optoelectronic and sensing applications. However, issues regarding long-term …

Halide perovskite materials serve as excellent candidates for solar cell and optoelectronic
devices. Recently, the design of the halide perovskite materials is greatly facilitated by …