Symbolic regression in materials science

Wang, Yiqun; Wagner, Nicholas; Rondinelli, James M.

doi:10.1557/mrc.2019.85

Cited by 176 publications

(106 citation statements)

References 78 publications

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“…Nevertheless, there are approaches that can aid in model interpretability to some degree. For example, the symbolic regression method is able to construct a mathematical formula for predicting target values, and such simple formulas may shed light on the physical processes. In deep learning for images, there have been attempts trying to unlock the “black box” of CNN models using class activation maps or activation atlas maps .…”

Section: Perspectivesmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

398

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Perspectivesmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

398

281

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“…Symbolic regression, especially genetic programming‐based symbolic regression (GPSR), is a classical AI algorithm . It is different from the traditional numerical regression because the functional relationship between variables is not given.…”

Section: Basics Of MLmentioning

confidence: 99%

Artificial Intelligence to Power the Future of Materials Science and Engineering

Sha

Guo

Qing

et al. 2020

Advanced Intelligent Systems

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Artificial intelligence (AI) has received widespread attention over the last few decades due to its potential to increase automation and accelerate productivity. In recent years, a large number of training data, improved computing power, and advanced deep learning algorithms are conducive to the wide application of AI, including material research. The traditional trial‐and‐error method is inefficient and time‐consuming to study materials. Therefore, AI, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry where a computer is only a calculator, using hard‐coded formulas provided by human experts. Herein, the application of AI in material innovation is reviewed, including material design, performance prediction, and synthesis. The realization details of AI techniques and advantages over conventional methods are emphasized in these applications. Finally, the future development direction of AI is expounded from both algorithm and infrastructure aspects.

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“…Recent applications of SR for physical models can be found in civil engineering [32] and material science [33]. Although successfully proven, the earlier proposed SR was based on a heuristic search that could terminate the optimization in local minima solutions, potentially producing less suitable models than possible.…”

Section: Introductionmentioning

confidence: 99%

A new formulation for symbolic regression to identify physico-chemical laws from experimental data

Neumann

Cao

Russo

et al. 2020

Chemical Engineering Journal

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A modification to the mixed-integer nonlinear programming (MINLP) formulation for symbolic regression was proposed with the aim of identification of physical models from noisy experimental data. In the proposed formulation, a binary tree in which equations are represented as directed, acyclic graphs, is fully constructed for a pre-defined number of layers. The introduced modification results in the reduction in the number of required binary variables and removal of redundancy due to possible symmetry of the tree formulation. The formulation was tested using numerical models and was found to be more efficient than the previous literature example with respect to the numbers of predictor variables and training data points. The globally optimal search was extended to identify physical models and to cope with noise in the experimental data predictor variable. The methodology was proven to be successful in identifying the correct physical models describing the relationship between shear stress and shear rate for both Newtonian and non-Newtonian fluids, and simple kinetic laws of chemical reactions. Future work will focus on addressing the limitations of the present formulation and solver to enable extension of target problems to larger, more complex physical models.

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Symbolic regression in materials science

Cited by 176 publications

References 78 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Artificial Intelligence to Power the Future of Materials Science and Engineering

A new formulation for symbolic regression to identify physico-chemical laws from experimental data

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