Spectrally-Driven Active Learning Enables Femtojoule-Efficient Discovery of Photocatalysts in Under One Hour: The LuminaFemto AI Platform - Barack Ndenga (Publisher, 2025)
The rapid discovery of high-performance photocatalysts remains a critical challenge in sustainable chemistry, where traditional screening workflows are limited by high energy consumption and long experimental times. In this work, I introduce LuminaFemto AI, a simulation-based active-learning framework designed to autonomously identify efficient photocatalysts under an energy budget at the femtojoule scale. I generate synthetic spectral fingerprints that mimic UV–Vis absorption and photoluminescence profiles of candidate materials. A Gaussian-process surrogate model is trained to learn the nonlinear mapping between spectra and photocatalytic performance. At each iteration, an uncertainty-driven acquisition function selects the next most informative candidates to minimize both prediction error and cumulative energy cost. Numerical experiments on a library of 200 virtual materials show that LuminaFemto AI converges toward the optimal catalyst in fewer than 25 iterations, achieving a sub-1 h simulated discovery time while reducing per-experiment energy consumption by three orders of magnitude compared to random exploration. This framework establishes a quantitative connection between spectral information, learning efficiency, and energy-aware optimization, paving the way for autonomous, ultra-low-power laboratories for materials discovery. “Machine learning is transforming the field of materials discovery by enabling algorithms to learn to see, learn to estimate, and learn to search in compositional spaces previously inaccessible to human trial‑and‑error.” Keywords Active Learning Photocatalyst Discovery Femtojoule Optimization Spectral Analysis Gaussian Process Regression Autonomous Materials Discovery Energy-Aware Simulation Machine Learning in Chemistry High-Throughput Screening LuminaFemto AI
This repository contains the full Python prototype and associated data for LuminaFemto AI, an energy-aware, spectral-driven active learning framework for rapid discovery of photocatalysts. The framework simulates 200 virtual photocatalyst candidates, employing Gaussian Process regression to predict performance and guide experiment selection using a femtojoule-scale acquisition function. The code produces publication-ready figures illustrating: Convergence of predicted maximum performance Cumulative energy consumption Evolution of observed spectra Spectral Convergence to Femtojoule Optimum Condition (FOC) This work demonstrates a novel paradigm for energy-efficient, autonomous materials discovery, enabling rapid identification of optimal candidates while minimizing energy and experimental cost. The repository includes: Full Python code for simulation and visualization Instructions for installation and usage Supplementary figures and results