Most computationally predicted materials are never synthesized because conventional synthesis optimization is slow, expertise-dependent, and iterative. Here we present a closed-loop framework that automates this expert workflow by placing human tacit knowledge in the loop through a large language model (LLM) that distills synthesis knowledge from the literature, high-throughput hyperspectral imaging for rapid film evaluation, and multi-objective Bayesian optimization guided by experimental feedback. In a paired optimization campaign, LLM-assisted initialization produced more Pareto-optimal samples and higher hypervolume than a Latin hypercube sampling baseline at matched trial counts, and this advantage persisted throughout iterative optimization. We demonstrate the framework by synthesizing the previously unreported perovskite-inspired compound Rb3BiI6 as thin films and validating the optimized films by optical bandgap analysis and X-ray diffraction. The framework transforms synthesis prediction from single-shot recommendation to iterative learning, providing a generalizable strategy to accelerate automated and fully autonomous experimental materials discovery.
Developing reliable synthesis routes for complex materials remains a major bottleneck in accelerating materials discovery. This study establishes a large language model-based framework for predicting and optimizing synthesis conditions directly from the literature data. Key synthesis information, including target compo...
Dong Won Jeon, Dong Hwi Kim, Taeyang Jeon et al.· Advances in Materials· 0 citations
Autonomous experiments that integrate machine learning and robotics are reshaping materials research. By automating experimental workflows and efficiently searching high-dimensional parameter spaces, these approaches markedly accelerate materials discovery and process optimization.
Here, we report a modular self-drivi...
ABSTRACT Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual...
Javier Sanz Rodrigo, Nicholas A. Kryger-Nelson, Lena A. Mittmann et al.· Small· 1 citation
The Simulation-Calibrated Active Learning Estimator (SCALE), a closed-loop framework uniting high-throughput molecular dynamics, machine learning, and robotic synthesis to bridge the gap between simulation and experiment, is introduced.
Felix Arendt, T. Waurischk, Stefan Reinsch et al.· npj Computational Materials· 0 citations
Generative artificial intelligence is rapidly transforming materials design by enabling de novo exploration of immense chemical spaces. Yet a large proportion of AI-generated compositions remain implausible, violating established chemical principles, which limits the reliability and interpretability of generative mater...
Kinga O. Mastej, Panyalak Detrattanawichai, Hyunsoo Park et al.· 0 citations
This work introduces Top-K prompting as a robust training and inference paradigm to better capture diverse, plausible reaction predictions and establishes Top-K, plausibility-aware training as a practical new direction for robust future LLM-based synthesis planning.
B. Zagribelnyy, Ivan D. Ilin, N. Bondarev et al.· 1 citation
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