Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
arXiv:2606.02662v1 Announce Type: new Abstract: Machine learning has accelerated quantum chemistry but is hindered by the prohibitive cost of generating high fidelity training data. Multifidelity machine learning (MFML) mitigates this overhead by systematically combining abundant low fidelity data with sparse high fidelity data. In spite of its success, standard MFML schemes rely on pre-defined scaling factors to determine sparse data ratio across fidelities, often generating redundant multifidelity data resulting in a loss of efficiency. Here, we introduce an adaptive on-the-fly multifidelity
The increasing computational demands of quantum chemistry and other scientific fields are driving the need for more efficient machine learning algorithms to reduce data generation costs.
This development proposes a method to significantly reduce the cost and time associated with generating high-fidelity training data for complex scientific machine learning applications, accelerating research and development.
Machine learning models in fields like quantum chemistry can now be trained more efficiently by adaptively leveraging multi-fidelity data, moving away from reliance on static, pre-defined scaling factors.
- · AI researchers
- · Quantum chemists
- · Biotechnology sector
- · Materials science
- · Organizations with large, inefficient data generation pipelines
Faster development and deployment of ML models in scientific research, particularly in quantum chemistry.
Reduced barriers to entry for computationally intensive scientific fields by lowering data generation costs.
Potential for new discoveries in materials science and drug discovery through accelerated simulation and predictive modeling.
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Read at arXiv cs.LG