Hamide Kavak | Searcharxiv

arXiv2026

Materials Informatics Across the Length Scales

Materials informatics is increasingly used to support modelling, analysis and design across the length scales of materials science, from atomistic simulations to microstructural characterisation and continuum descriptions. Despite rapid progress, the reliability and transferability of these approaches vary strongly with scale. Here we survey data-driven methods at the nanoscale, mesoscale, and micro-to-continuum levels, highlighting established capabilities as well as unresolved challenges. Machine-learning interatomic potentials, mesoscale surrogate and operator-learning models, and learning-based analysis of experimental microstructures are discussed, with emphasis on data quality, uncertainty, interpretability, and cross-scale consistency. We further examine the role of data standards, ontologies, and emerging tools, such as autonomous laboratories, where they directly affect multiscale workflows. This perspective clarifies what can be considered reliable today and identifies key obstacles to the broader integration of materials informatics across scales.

cond-mat.mtrl-sci↗

arXiv2023

Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization

High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, with the boom in development of high-throughput synthesis tools that champion production rates up to $10^4$ samples per hour with flexible form factors, most sample characterization methods are either slow (conventional rates of $10^1$ samples per hour, approximately 1000x slower) or rigid (e.g., designed for standard-size microplates), resulting in a bottleneck that impedes the materials-design process. To overcome this challenge, we propose a set of automated material property characterization (autocharacterization) tools that leverage the adaptive, parallelizable, and scalable nature of computer vision to accelerate the throughput of characterization by 85x compared to the non-automated workflow. We demonstrate a generalizable composition mapping tool for high-throughput synthesized binary material systems as well as two scalable autocharacterization algorithms that (1) autonomously compute the band gap of 200 unique compositions in 6 minutes and (2) autonomously compute the degree of degradation in 200 unique compositions in 20 minutes, generating ultra-high compositional resolution trends of band gap and stability. We demonstrate that the developed band gap and degradation detection autocharacterization methods achieve 98.5% accuracy and 96.9% accuracy, respectively, on the FA$_{1-x}$MA$_{x}$PbI$_3$, $0\leq x \leq 1$ perovskite semiconductor system.

eess.IV↗