Authors: Leandro Carreira | Lea Pillemont | Yasser Sami | N. Richard | Alain Estève | Matthieu Jonckheere | Carole Rossi
Source: Energetic Materials Frontiers (article, 2024-09-07)
Field: Materials Science — Materials Chemistry

Targeted nano-energetic material exploration through active learning algorithm implementation This paper presents an active learning-based method designed to guide experiments towards user-defined specific regions, termed ”regions of interest,” within vast and multi-dimensional thermite design spaces. Thermites composed of metallic reactant coupled to an inorganic oxidizer are non-explosive energetic materials which stay inert and stable until subjected to a sufficiently strong thermal stimulus, after which they undergo fast burning with release of high amount of chemical energy (up to 16 kJ⋅cm −3 ). They represent an interesting class of nano-engineered energetic material because of their high adiabatic flame temperature (>2600 °C) and customizable combustion properties. We introduced a new acquisition function combining linearly two factors with the usual standard deviation of a Gaussian Process Regression algorithm. A first factor guides the sampling towards the defined zone of interest, and a second, is an incentive function that encourages the exploration of under-sampled regions of the design space. We found that our algorithm effectively provides up to several tens of nanothermites that achieve specific desired properties well-distributed within the design space, after only 200 samplings, whereas, Latin Hypercube Sampling procedure samples less than 10 points of interest. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and when only a small dataset is available, as it is the case in thermite materials. But more generally, this research represents a significant advancement for large-scale problems in energetic materials science and engineering, where predicting the effect of feature modification is desired but limited simulations or experiments can be afforded due to their high cost. One of the key challenges in designing reactive materials is efficiently exploring the vast design space to achieve targeted properties, as traditional trial-and-error methods are impractical. To address this, we investigated active learning techniques guided by specific learning strategies to direct the sampling process toward predefined objectives, such as identifying thermite materials with user-specified properties. This study demonstrates how adaptive learning methods can significantly accelerate the search for optimal Al/CuO nanothermites for specific applications. Notably, the active learning approach proves to be more effective in navigating expansive design spaces, as it utilizes uncertainties and predictions from a surrogate model and an acquisition function that prioritizes exploration of unexplored data. Nano- ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Nanotechnology ; Engineering ; Materials science ; Chemical engineering Machine Learning in Materials Science ; Energetic Materials and Combustion ; Computational Drug Discovery Methods