Machine learning enabled discovery of application dependent design principles for two-dimensional materials

Abstract

The unique electronic and mechanical properties of two-dimensional (2D) materials make them promising next-generation candidates for a variety of applications. Large-scale searches for high-performing 2D materials are limited to calculating descriptors with computationally demanding first-principles density functional theory. In this work, we alleviate this issue by extending and generalizing crystal graph convolutional neural networks to systems with planar periodicity and train an ensemble of models to predict thermodynamic, mechanical and electronic properties. We carry out a screening of nearly 45,000 structures for two separate applications: mechanical strength and photovoltaics. By collecting statistics of the screened candidates, we investigate structural and compositional design principles that impact the properties of the structures surveyed. Our approach recovers some well-accepted design rules: hybrid organic-inorganic perovskites with lead and tin tend to be good candidates for solar cell applications and titanium based MXenes usually have high stiffness coefficients. Interestingly, other members of the group 4 elements also contribute to increasing the mechanical strength of MXenes. For all-inorganic perovskites, we discover some compositions that have not been deeply studied in the field of photovoltaics and thus open up paths for further investigation. We open-source the code-base to spur further development in this space.

A review on accelerated development of skin-like MXene electrodes: from experimental to machine learning

“…They generate massive data, which are tedious to store, process and analyze-for instance, Venturi et al screened 24 000 MXene structures to determine their underlying design principles. 143 Simulating the same for one structure using DFT took up to 500 CPU (Central Processing Unit) hours. However, an ML-based method could predict the same properties within 20 GPU (Graphic Processing Unit) minutes.…”

Quantum mechanics/molecular mechanics studies on the intrinsic properties of typical ester oil molecules

“…In material genetic engineering, multi-scale material simulation plays an increasingly important role in exploring the microscopic mechanism of materials, predicting new materials and properties, and simulating material properties in extreme environments [1][2][3][4][5]. At the same time, computational simulation has gradually emerged as a powerful tool that aids in experiments to explain certain phenomena and understand the microscopic mechanism of materials [6].…”

Machine Learning Assisted Prediction of Cathode Materials for Zn‐Ion Batteries

“…of thermodynamic, mechanical, and electronic properties for 2-D materials, [31] the formation energy of compounds, [32] and the methane adsorption properties of metal-organic frameworks, [33] etc.…”

“…One particular example is the recent development of the crystal graph convolutional neural network (CGCNN), [ 29 ] an ML tool that encodes the crystal structure as a "graph," has enabled the accelerated design of crystalline materials with desired properties, while also providing fundamental insights on the elementary/structural contributions to the energetics and properties of the materials. It has been successfully applied to the design of various crystalline materials for specific properties and applications, including the screening of solid electrolytes for dendrite suppression in lithium battery, [ 30 ] prediction of thermodynamic, mechanical, and electronic properties for 2‐D materials, [ 31 ] the formation energy of compounds, [ 32 ] and the methane adsorption properties of metal‐organic frameworks, [ 33 ] etc.…”