https://scholar.dgist.ac.kr/handle/20.500.11750/60162 2026-09-30T07:14:32Z 2026-09-30T07:14:32Z Fatriansyah, Jaka Fajar Nafisah, Helya Chafshoh Hartoyo, Fernanda Lesmana, Gilbert Suhariadi, Iping Pradana, Agrin Febrian Krisdiawan, Andiko Putra Pratama Federico, Andreas Lockman, Zainovia Dhaneswara, Donanta Hur, Su-Mi Fang, Ping Kusrini, Eny Ulum, Reza Miftahul Adhika, Damar Rastri Santoso, Iman https://scholar.dgist.ac.kr/handle/20.500.11750/60852 2026-09-21T08:10:15Z 2025-12-31T15:00:00Z

Title: Deep Learning-Based Generation of Lead-Free Organic-Inorganic Hybrid Halide Perovskite Materials Using Conditional Variational Autoencoders Author(s): Fatriansyah, Jaka Fajar; Nafisah, Helya Chafshoh; Hartoyo, Fernanda; Lesmana, Gilbert; Suhariadi, Iping; Pradana, Agrin Febrian; Krisdiawan, Andiko Putra Pratama; Federico, Andreas; Lockman, Zainovia; Dhaneswara, Donanta; Hur, Su-Mi; Fang, Ping; Kusrini, Eny; Ulum, Reza Miftahul; Adhika, Damar Rastri; Santoso, Iman Abstract: Organic-inorganic hybrid perovskites (OIHP) are promising materials for photovoltaic applications. This study proposes a computational candidate-generation framework for discovering new lead-free OIHP compositions, targeting band-gap energy as the primary property, while volume per atom, atomization energy, and density were used as supporting material descriptors. The framework integrates two coupled models: an ANN-based predictor and a CVAE-based generator. The ANN predictor achieved R2 scores of 89%, 93%, 91%, and 93% for band gap energy, volume per atom, atomization energy, and density, respectively. The CVAE generator successfully produced lead-free perovskite candidate compositions with band gap energies ranging from 1.2 eV to 3.6 eV. DFT validation of the generated candidates showed that 10 of 11 compositions (90.9%) had band-gap deviations below 10%, with a mean deviation of 6.40%. The smallest deviation was 2.70% for CH3NH3CaI3, whose predicted band gap was confirmed by DFT calculation.

2025-12-31T15:00:00Z Tae, Sung Kwan Irianti, Gabriella Pasya Kim, Ye Chan Im, Seong-Gyun Kwon, S. Joon Hur, Su-Mi Kim, So Youn https://scholar.dgist.ac.kr/handle/20.500.11750/60624 2026-08-19T07:10:13Z 2026-03-31T15:00:00Z

Title: Quantitative control of orientational and positional disorder in nanopatterned arrays of metal-infiltrated block copolymers Author(s): Tae, Sung Kwan; Irianti, Gabriella Pasya; Kim, Ye Chan; Im, Seong-Gyun; Kwon, S. Joon; Hur, Su-Mi; Kim, So Youn Abstract: Correlated disorder is not uncommon in nature and often possesses unexpectedly unique properties, inspiring scientists to explore disorder as a functional design element. However, the controlled realization and reproduction of disordered nanostructures remain experimentally challenging, with the concept of "disorder" itself implying its multifaceted nature. Here, we present a methodology to tune structural disorder using metal-infiltrated block copolymers. Starting from a single-grain hexagonal lattice formed by sphere-forming block copolymer thin films, we intentionally introduce and modulate disorder by controlling annealing temperatures and the type of incorporated metals. We establish a robust analytical framework to quantify the order/disorder parameters, providing a clear yet precise assessment of structural irregularity. Supported by molecular dynamics simulations that reveal the mechanisms of disorder formation, we demonstrate a comprehensive dispersion spectrum of nanoparticles-ranging from highly ordered to disordered states. Supported by phononic bandgap calculations, we show that this continuum can serve as a platform for the controlled engineering of disordered wave-manipulating systems.

2026-03-31T15:00:00Z