Kostya Novoselov

Oct 14, 2024 | nature.com | Rui Su |RUI ZHU |Deying Luo |Pengru Huang |Linjie Dai |Pietro Caprioglio | +16 more

AbstractObtaining micron-thick perovskite films of high quality is key to realizing efficient and stable positive (p)-intrinsic (i)-negative (n) perovskite solar cells1,2, but it remains a critical challenge. Here, we report an effective method for producing high-quality, micron-thick formamidinium-based perovskite films by forming coherent grain boundaries, where high-Miller-index-oriented grains grow on the low-Miller-index-oriented grains in a stabilized atmosphere.

Jul 22, 2024 | onlinelibrary.wiley.com | Chang Liu |Yujiao Bo |Qi Ge |Kostya Novoselov

Conflict of Interest The authors declare no conflict of interest. Supporting Information Filename Description lpor202400615-sup-0001-SuppMat.docx16.4 MB Supporting Information References 1, , , , , , Science 2024, 383, 1455. 2, , , , , , , , , Adv. Funct. Mater. 2024, 34, 2309500. 3, , , , , , ACS Nano 2023, 17, 23194. 4, , , , , , , , J. Mater. Chem. A 2024, 12, 3589. 5, , , , , J. Colloid Interface Sci. 2024, 653, 56. 6, , , , , , , , Adv. Mater. 2023, 35, 2202193.

Dec 28, 2023 | nature.com | Xiaoli Chen |Haijun Yu |Kostya Novoselov |Kedar Hippalgaonkar

AbstractOne of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail.

Apr 11, 2023 | pnas.org | Kostya Novoselov |Zahra A. Aldawood |Luigi Mancinelli |Xuehui Geng

Skip to main content Polygonal tessellations as predictive models of molecular monolayersContributed by Kostya S. Novoselov; received January 2, 2023; accepted March 10, 2023; reviewed by M.F. Crommie and Marjorie SenechalSignificancePattern prediction of two dimensions (2D) molecular networks has so far relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning.