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<p>BIAS (Bayesian Inference with Accelerated Sampling) is a high-throughput parameter estimation framework designed to rapidly infer the root causes of device underperformance in real time. It integrates a deep neural network surrogate model with accelerated Markov Chain Monte Carlo sampling to efficiently explore high-dimensional parameter spaces and identify needle-like regions corresponding to the ground truth values of key physical parameters. BIAS is scalable to complex systems and has been used to infer eight underlying parameters in perovskite solar cell stacks with a combined speedup of 4800× from the surrogate model and accelerated sampling, compared to conventional Bayesian inference methods. Its rapid and robust inference capabilities render it suitable for integration into high-throughput fabrication workflows, enabling real-time feedback that links process variations to changes in material properties and their impact on device performance. By embedding BIAS in high-throughput fabrication cycles, researchers can accelerate the transition from novel materials to devices and obtain real-time insight into how novel materials' properties translate in the context of a device and the root cause of performance limitations.</p>

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High-throughput parameter estimation from experimental data using Bayesian Inference with accelerated sampling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Basita Das,
  • William E. Heymann,
  • Yueming Wang,
  • Uwe Rau,
  • Thomas Kirchartz,
  • Tonio Buonassisi

摘要

BIAS (Bayesian Inference with Accelerated Sampling) is a high-throughput parameter estimation framework designed to rapidly infer the root causes of device underperformance in real time. It integrates a deep neural network surrogate model with accelerated Markov Chain Monte Carlo sampling to efficiently explore high-dimensional parameter spaces and identify needle-like regions corresponding to the ground truth values of key physical parameters. BIAS is scalable to complex systems and has been used to infer eight underlying parameters in perovskite solar cell stacks with a combined speedup of 4800× from the surrogate model and accelerated sampling, compared to conventional Bayesian inference methods. Its rapid and robust inference capabilities render it suitable for integration into high-throughput fabrication workflows, enabling real-time feedback that links process variations to changes in material properties and their impact on device performance. By embedding BIAS in high-throughput fabrication cycles, researchers can accelerate the transition from novel materials to devices and obtain real-time insight into how novel materials' properties translate in the context of a device and the root cause of performance limitations.