# Data Generation for Machine Learning Interatomic Potentials and Beyond

**URL:** <https://community.materialssquare.com/t/data-generation-for-machine-learning-interatomic-potentials-and-beyond/989>\
**Category:** News\
**Created:** [December 3, 2024, 6:56pm UTC](https://community.materialssquare.com/t/data-generation-for-machine-learning-interatomic-potentials-and-beyond/989 "2024-12-03T18:56:44Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![gabriele1](https://yyz1.discourse-cdn.com/flex003/user_avatar/community.materialssquare.com/gabriele1/32/20_2.png) [@gabriele1](https://community.materialssquare.com/u/gabriele1)\
**Post date:** [December 3, 2024, 6:56pm UTC](https://community.materialssquare.com/t/data-generation-for-machine-learning-interatomic-potentials-and-beyond/989/1 "2024-12-03T18:56:44Z")

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This review article discusses the essential role of data in developing machine learning interatomic potentials (MLIPs) and beyond, emphasizing their transformative impact on computational chemistry and material science. It highlights the challenges in creating robust training datasets that adequately capture the diverse and complex chemical and structural spaces required for reliable predictions. Techniques like active learning, uncertainty quantification, and advanced sampling methods, including molecular dynamics and metadynamics, are explored for efficient data generation. The review also addresses the importance of integrating equilibrium, non-equilibrium, and reactive data, as well as electronic state information, to enhance the accuracy and transferability of ML models. Additionally, the article provides a comprehensive overview of publicly available datasets and innovative approaches to build diverse, high-quality datasets, supporting advancements in materials discovery, molecular simulations, and quantum chemistry.

For more details, please continue reading the full article under the following link:

[https://pubs.acs.org/doi/10.1021/acs.chemrev.4c00572](https://pubs.acs.org/doi/10.1021/acs.chemrev.4c00572)

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In general, if you enjoy reading this kind of scientific news articles, I would also be keen to connect with fellow researchers based on common research interests in materials science, including the possibility to discuss about any potential interest in the Materials Square cloud-based online platform ( [www.matsq.com](http://www.matsq.com) ), designed for streamlining the execution of materials and molecular atomistic simulations!

Best regards,

Dr. Gabriele Mogni  
Technical Consultant and EU Representative  
Virtual Lab Inc., the parent company of the Materials Square platform  
Website: [Home | Virtual Lab Inc.](http://www.virtuallab.co.kr/en/)  
Email: gabriele@simulation.re.kr

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