Abstract
AbstractOver the decades, the spin dynamics of a large set of lanthanide complexes have been explored. Lanthanide-based molecular nanomagnets are bistable spin systems, generally conceptualised as classical bits, but many lanthanide complexes have also been presented as candidate quantum bits (qubits). Here, we offer a third alternative and model them as probabilistic bits (p-bits), where their stochastic behaviour constitutes a computational resource instead of a limitation. Employing an ad-hoc modelling tool for molecular spin p-bits and molecular nanomagnets, we simulate a minimal p-bit network under realistic conditions. Finally, we go back to a recently published dataset and screen the best lanthanide complexes for p-bit behaviour, lay out the performance of the different lanthanide ions and chemical families and offer some chemical design considerations.
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation
Reference51 articles.
1. Madaeni, F. et al. Convolutional neural network and long short-term memory models for ice-jam predictions. Cryosphere 2022, 1447 (2022).
2. Robert, S., Büttner, S., Röcker, C. & Holzinger, A. Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning (Springer International Publishing AG, 2016).
3. Mohamed Derbeli, C. N. & Barambones, O. Machine learning approach for modeling and control of a commercial heliocentris fc50 pem fuel cell system. Math 2021, 2068 (2021).
4. Wang, H. & Xuan, Y. A spatial pattern extraction and recognition toolbox supporting machine learning applications on large hydroclimatic datasets. Remote Sens. 2022, 3823 (2022).
5. Sonia, A., Kumar, K. & Iwendi, C. Time series data modeling using advanced machine learning and automl. Sustainability 2022, 15292 (2022).
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