The application of Artificial Intelligence (AI), particularly Machine Learning (ML), has demonstrated remarkable success in thermodynamics, enabling the development of highly accurate and efficient models for molecular simulation, materials design, and property prediction. Pioneering work, such as the creation of High-Dimensional Neural Network Potentials (HDNNPs) by Behler and Parrinello [1] and the subsequent rise of Machine Learning Force Fields (MLFFs), has allowed for molecular dynamics (MD) simulations to achieve near-quantum accuracy at classical computational scales, rapidly advancing the prediction of thermodynamic properties.

The core laws of thermodynamics govern nearly every process in nature and engineering, from energy storage to materials synthesis. The rise of Machine Learning (ML) has offered an unprecedented opportunity to accelerate this field. Early models, exemplified by Machine Learning Force Fields (MLFFs) like the High-Dimensional Neural Network Potentials (HDNNPs) [1], have already proven capable of providing quantum-level accuracy at classical speeds for molecular simulations, representing a massive leap in predictive modeling capability.

Despite initial triumphs, data-driven AI models faced a critical hurdle: they often acted as “black boxes” that violated the very physical laws they were meant to model. This resulted in a “consistency crisis,” where ML predictions failed to uphold fundamental Maxwell relations and were prone to catastrophic failure when applied outside their training domain (poor extrapolation). Furthermore, a major theoretical gap exists in applying AI to Non-Equilibrium Thermodynamics (NET), the science governing complex, real-world dynamic processes.

How can the power of AI be harnessed to accelerate thermodynamic discovery without sacrificing the physical rigor and consistency required for robust scientific and engineering applications? Specifically, how can researchers embed the principles of thermodynamics into AI models to guarantee physical law adherence, enhance model interpretability, and finally tackle the complex challenges of non-equilibrium dynamics?

Physics-Informed and Thermodynamics-Inspired AI

The literature points toward a decisive shift from purely data-driven models to Physics-Informed Neural Networks (PINNs) and Thermodynamics-Inspired AI. Key solutions include:

• Guaranteed Consistency: Developing models like the Free Energy Neural Network (FE-NN), which use Automatic Differentiation (AD) to explicitly model the free energy potential, ensuring all Maxwell relations and the laws of thermodynamics are mathematically preserved [3].
• Enhanced Interpretability: Applying thermodynamic concepts, such as Interpretation Entropy [4], to explain the decisions of complex ML models, turning black boxes into more transparent tools.
• The Future Frontier: Integrating the foundational mathematical principles of Non-Equilibrium Thermodynamics (NET) with modern generative models (like Diffusion Models) to build the next generation of AI capable of mastering dynamic, complex systems and accelerating materials and energy discovery [5].

This evolution represents a synthesis: the computational speed of AI is now being married to the unshakable laws of physics, paving the way for trustworthy, universal, and high-impact thermodynamic modeling.

References:

[1] J. Behler and M. Parrinello, “Generalized neural-network representation of high-dimensional potential-energy surfaces,” Phys. Rev. Lett., vol. 98, no. 14, p. 146401, 2007.
[3] D. G. Rosenberger, K. M. Barros, T. C. Germann, and N. E. Lubbers, “Machine learning of consistent thermodynamic models using automatic differentiation,” Phys. Rev. E, vol. 105, no. 4, p. 045301, 2022.
[4] S. Mehdi and P. Tiwary, “Thermodynamics-inspired explanations of artificial intelligence,” Nat. Commun., vol. 15, no. 1, p. 7752, 2024.
[5] J. Sohl-Dickstein, E. Weiss, J. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” Proc. Annu. Conf. Mach. Learn. Res., vol. 37, pp. 2256–2265, 2015.