Machine Learning-Based Experimental Design in Materials Science

One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure-property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

show abstract

“…A more in-depth review of Bayesian optimization and Monte Carlo tree search in materials design can be found in ref. 399 .…”

Section: Adaptive Design Process and Active Learningmentioning

confidence: 99%

Recent advances and applications of machine learning in solid-state materials science

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Therefore, the combination of the two methods is indeed relevant and will keep being useful for the foreseeable future. Over the last few years, combinations of ML and DoE have been used to optimise materials design [158][159][160] or various synthetic procedures, 161,162 however to our knowledge this hasn't yet been expanded to the IL area.…”

Section: Consistencymentioning

confidence: 99%

A review on machine learning algorithms for the ionic liquid chemical space

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The growing toolkit for running high-throughput calculations [39][40][41][42], the potentially immense search space (e.g. often more than 10 10 viable compounds [43]), and the growing number of studies using adaptive design in the materials domain to guide both simulations [34,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59] as well as experiments [44,[60][61][62][63] makes computational materials design an exciting field to explore with Rocketsled. In the two following case studies, we demonstrate the applicability of Rocketsled to adaptive design for materials discovery.…”

Section: Application To the Materials Science Domain: Photocatalysis mentioning

confidence: 99%

Rocketsled: a software library for optimizing high-throughput computational searches

2019

View full text Add to dashboard Cite

A major goal of computation is to optimize an objective for which a forward calculation is possible, but no inverse solution exists. Examples include tuning parameters in a nuclear reactor design, optimizing structures in protein folding, or predicting an optimal materials composition for a functional application. In such instances, directing calculations in an optimal manner is important to obtaining the best possible solution within a fixed computational budget. Here, we introduce Rocketsled, an open-source Pythonbased software framework to help users optimize arbitrary objective functions. Rocketsled is built upon the existing FireWorks workflow software, which allows its computations to scale to supercomputing centers and for its objective functions to be complex, long-running, and error-prone workflows. Other unique features of Rocketsled include its ability to easily swap out the underlying optimizer, the ability to handle multiple competing objectives, the possibility to inject domain knowledge into the optimizer through feature engineering, incorporation of uncertainty estimates, and its parallelization scheme for running in high-throughput at massive scale. We demonstrate the generality of Rocketsled by applying it to optimize several common test functions (Branin-Hoo, Rosenbrock 2D, and Hartmann 6D). We highlight its potential impact through two example use cases for computational materials science. In a search for photocatalysts for hydrogen production among 18 928 perovskites previously calculated with density functional theory, the untuned Rocketsled Random Forest optimizer explores the search space with approximately 6-28 times fewer calculations than random search. In a search among 7394 materials for superhard candidates, Rocketsled requires approximately 61 times fewer calculations than random search to discover interesting candidates. Thus, Rocketsled provides a practical framework for establishing complex optimization schemes with minimal code infrastructure and enables the efficient exploration of otherwise prohibitively large search spaces.

show abstract

Machine Learning-Based Experimental Design in Materials Science

Cited by 14 publications

References 27 publications

Recent advances and applications of machine learning in solid-state materials science

Recent advances and applications of machine learning in solid-state materials science

A review on machine learning algorithms for the ionic liquid chemical space

Rocketsled: a software library for optimizing high-throughput computational searches

Contact Info

Product

Resources

About