Publications by authors named "B M Rubenstein"
Is stochastic thermodynamics the key to understanding the energy costs of computation?
Proc Natl Acad Sci U S A
November 2024
Article Synopsis
- The relationship between thermodynamics and computation has fascinated scientists since the 19th century, but its significance has grown due to the rising energy costs of digital devices.
- Real-world computers, including biological ones like brains, operate under various physical constraints, which impact their thermodynamic dynamics and efficiency.
- The emerging field of stochastic thermodynamics offers new analytical techniques to explore how these constraints affect the fundamental thermodynamic properties of systems performing computation.
Key to being able to accurately model the properties of realistic materials is being able to predict their properties in the thermodynamic limit. Nevertheless, because most many-body electronic structure methods scale as a high-order polynomial, or even exponentially, with system size, directly simulating large systems in their thermodynamic limit rapidly becomes computationally intractable. As a result, researchers typically estimate the properties of large systems that approach the thermodynamic limit by extrapolating the properties of smaller, computationally-accessible systems based on relatively simple scaling expressions.
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- Accurate mapping of potential energy surfaces (PESs) is essential for understanding atomic rearrangements in physical and chemical processes.
- Stochastic electronic structure methods like Quantum Monte Carlo (QMC) provide precise energy calculations but struggle with force computations necessary for algorithms that find minimum-energy pathways (MEP) and transition states (TS).
- The study introduces a modified surrogate Hessian method to identify MEPs and TSs efficiently, using a force-free QMC approach, validated through examples and a hybrid DFT-QMC method that enhances the accuracy of thermodynamic and kinetic calculations.
Molecular data storage offers the intriguing possibility of higher theoretical density and longer lifetimes than today's electronic memory devices. Some demonstrations have used deoxyribonucleic acid (DNA), but bottlenecks in nucleic acid synthesis continue to make DNA data storage orders of magnitude more expensive than electronic storage media. Additionally, despite its potential for long-term storage, DNA faces durability challenges from environmental degradation.
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