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
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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.
Email: gabriele@simulation.re.kr
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