Special Talk: Engineering a Paradigm Shift by Autonomous Design of Perovskite Nanocrystal Alloys

Speaker: Prof. Yehonadav Bekenstein is an Associate Professor at the Technion, Faculty of Materials Science and Engineering

Abstract: For nearly a century, alloy design has been governed by the Hume–Rothery rules, which assert strict miscibility limits based on ionic-size mismatch and lattice strain. Halide perovskites appeared to adhere closely to these size matching constraints, for example chloride–iodide alloys were considered fundamentally unstable inaccessible for optoelectronic materials. In this talk, I will show that these rules break down at the nanoscale, with major implications for alloy design and phase-stability engineering.

Using high-throughput colloidal nanocrystal experiments integrated with a cluster-expansion model trained on DFT, we completely mapped the stability phase space of ternary halide alloys across thousands of anion-exchange reactions. We reveal that nanocrystal size dramatically modifies miscibility behavior. Smaller nanocrystals suppress halide segregation and defect formation, producing bright and stable Cl–Br–I solid solutions that are forbidden in theory and do not occur in bulk. This establishes nanoscale miscibility engineering as a new design principle that extends and in some cases overturns classical alloy rules. The same framework guides exploration of lead-free double perovskite nanocrystals. These multi compund materials with the elpasolite crystal structure (Cs₂BB′X₆), are electronicaly different then lead based perovskites. To enginer tunable and usedful excitonic properties multi-cation alloying and doping is used which expands the experimental parameter space to six different elements per unit cell, and many more syntheitc descriptors. The example I will discuss focuses on alloying of the B site with Na-K-Li, and resulting self-trapped excitonic properties. To accelerate these materials discovery we turn to self-driving laboratories (SDLs) that autonomously focus and narrow the vast multidimensional spaces by ancoring theoretical predictions in real-life data. By directing robotic synthesis, real-time optical and structural characterization, and machine-learning feedback, SDL platforms provide a scalable pathway to rapidly discover key halide -perovskite properties, alloy stability, identify new phases, and uncover materials with unprecedented combinations of properties.

Short Bio: Prof. Yehonadav Bekenstein is an Associate Professor at the Technion, in the Faculty of Materials Science and Engineering, where he leads research in autonomous materials discovery, focusing on light-emitting and energy-related Nano-materials.

Prof. Bekenstein earned all his degrees in Physics and Chemistry from the Hebrew University of Jerusalem. He then conducted postdoctoral research as a Rothschild Postdoctoral Fellow at the University of California, Berkeley.

His achievements have been recognized with several prestigious awards, including the Krill Prize for Excellence in Scientific Research and the Goldberg Prize from the Technion.