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Abstract
It is safe to say that every invention that has changed the world has depended on materials. At present, the demand for the development of materials and the invention or design of new materials is becoming more and more urgent since peoples’ current production and lifestyle needs must be changed to help mitigate the climate. Structure-property relationships are a vital paradigm in materials science. However, these relationships are often nonlinear, and the pattern is likely to change with length scales and time scales, posing a huge challenge. With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic structure but by using backpropagation to overcome local minimax traps and perform a fast calculation of the gradient information for a target function concerning the design variable to find the optimizations. The methodologies have been applied to various materials including polymers, photonics, inorganic materials, porous materials, 2-D materials, etc. Different types of design problems require different approaches, for which many algorithms and optimization approaches have been demonstrated in different scenarios. In this mini-review, we will not specifically sum up machine learning methodologies, but will provide a more material perspective and summarize some cut-edging studies.
Highlights
The revolution of materials gave name to different eras of civilization [1,2]
The invention of the computer stimulated its application in the scientific field, leading to computational chemistry with computer simulations such as the appearance of Gaussian 70, which can perform ab initio calculations, density functional theory (DFT)-based method, etc. [4,5]
machine learning (ML)-based inverse design uses backpropagation to overcome local minimax traps and performs a quick calculation of the gradient information for a target function concerning the design variable to find the optimizations. In this mini-review, inverse design based on ML and their cut-edging application in several important materials have been reviewed in a limited way
Summary
The revolution of materials gave name to different eras of civilization [1,2]. One of the hallmarks of industrialized society is our increasing extravagance in the use of materials. ML-based inverse design uses backpropagation to overcome local minimax traps and performs a quick calculation of the gradient information for a target function concerning the design variable to find the optimizations In this mini-review, inverse design based on ML and their cut-edging application in several important materials have been reviewed in a limited way. They first construct a population that is always valid for the existing dataset and a second stage was built to select suitable molecular descriptors to ensure the validity of the generMaterials 2022, 15, x FOR PEER REVIEaWted molecules They showed that the model can preserve the molecular core and opt7imofiz2e0 target protein properties across generations through cannabidiol molecular optimization.
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