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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

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De Novo Inverse Design Superhard C-N Compounds via Global Machine Learning Interatomic Potentials and Multiobjective

Guanjian Cheng^1,2, Wan-Jian Yin^1,3

¹College of Energy, Soochow Institute for Energy and Materials InnovationS (SIEMIS), and Jiangsu Provincial Key Laboratory for Advanced Carbon Materials and Wearable Energy Technologies, Soochow University, Suzhou 215006, China.

The Journal of Physical Chemistry Letters

|April 24, 2025

Summary

This summary is machine-generated.

Researchers developed a novel variable-composition inverse material design (VC-IMD) approach to discover new superhard materials. This method identified 38 novel carbon-nitrogen compounds, including one potentially harder than diamond.

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Area of Science:

Materials Science
Computational Chemistry
Solid State Physics

Background:

Identifying materials harder than diamond is a significant challenge in materials science.
Existing computational materials databases lack comprehensive superhard compound data.

Purpose of the Study:

To develop an efficient computational approach for designing novel superhard materials.
To discover new carbon-nitrogen (C-N) compounds with hardness exceeding that of diamond.

Main Methods:

Implemented a variable-composition inverse material design (VC-IMD) approach.
Utilized an improved multiobjective optimization algorithm with structure similarity constraints.
Employed active learning to train global machine learning interatomic potentials (g-MLIPs).

Main Results:

Identified 38 novel and stable C-N superhard materials.
Discovered a new material, C3(P6422), with a calculated hardness of 97.4 GPa.
Achieved high precision in g-MLIPs within three iterations.

Conclusions:

The VC-IMD approach offers a new pathway for designing materials with targeted superhard properties.
The discovered C-N compounds represent significant advancements in the search for ultrahard materials.
This methodology accelerates the discovery of novel materials beyond existing databases.