arXiv subjects

Joao M. Pina

Publications and source records attributed to Joao M. Pina.

2 recordsLinked to original sources

arXiv2026

Permanent Lattice Compression of Lead-Halide Perovskite for Persistently Enhanced Optoelectronic Properties

Under mild mechanical pressure, halide perovskites show enhanced optoelectronic properties. However, these improvements are reversible upon decompression, and permanent enhancements have yet to be realized. Here, we report antisolvent-assisted solvent acidolysis crystallization that enables us to prepare methylammonium lead bromide single crystals showing intense emission at all four edges under ultraviolet light excitation. We study structural variations, edge-vs-center, in these crystals using micro-X-ray diffraction and find that the enhanced emission at the edges correlates with lattice compression compared to in the central areas. Time-resolved photoluminescence measurements show much longer-lived photogenerated carriers at the compressed edges, with radiative component lifetimes of ca. 1.4 us, 10 times longer than at the central regions. The properties of the edges are exploited to fabricate planar photodetectors exhibiting detectivities of 3x10^13 Jones, compared to 5x10^12 Jones at the central regions. The enhanced lifetimes and detectivities correlate to the reduced trap state densities and the formation of shallower traps at the edges due to lattice compression.

cond-mat.mtrl-sci↗

arXiv2021

Interpretable discovery of new semiconductors with machine learning

Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To provide guidance in experimental materials synthesis, these need to be coupled with an accurate yet effective search algorithm and training data consistent with experimental observations. Here we report an evolutionary algorithm powered search which uses machine-learned surrogate models trained on high-throughput hybrid functional DFT data benchmarked against experimental bandgaps: Deep Adaptive Regressive Weighted Intelligent Network (DARWIN). The strategy enables efficient search over the materials space of ~10$^8$ ternaries and 10$^{11}$ quaternaries$^{7}$ for candidates with target properties. It provides interpretable design rules, such as our finding that the difference in the electronegativity between the halide and B-site cation being a strong predictor of ternary structural stability. As an example, when we seek UV emission, DARWIN predicts K$_2$CuX$_3$ (X = Cl, Br) as a promising materials family, based on its electronegativity difference. We synthesized and found these materials to be stable, direct bandgap UV emitters. The approach also allows knowledge distillation for use by humans.

cond-mat.mtrl-sci↗