arXiv:2509.21624v3 Announce Type: replace Abstract: Fundamental tasks in computational chemistry, from transition state search to vibrational analysis, rely on molecular Hessians, which are the second derivatives of the potential energy. Yet, Hessians are computationally expensive to calculate and scale poorly with system size, with both quantum mechanical methods and neural networks. In this work, we demonstrate that Hessians can be predicted directly from a deep learning model, without relying on automatic differentiation or finite differences. We observe that one can construct SE(3)-equivar
Advances in deep learning architectures, specifically SE(3)-equivariant networks, have enabled more accurate and efficient methods for simulating complex physical phenomena like molecular interactions.
Predicting Hessians directly and efficiently is crucial for fundamental tasks in computational chemistry, impacting drug discovery, materials science, and energy research.
The computational bottleneck associated with molecular Hessians in quantum mechanical methods and neural networks is significantly reduced, accelerating molecular simulations.
- · Pharmaceutical companies
- · Materials science researchers
- · AI/ML research labs
- · Computational chemists
- · Developers of less efficient Hessian calculation methods
- · Researchers reliant solely on traditional QM methods for large systems
Faster and more accurate computational chemistry simulations will become widely accessible.
Accelerated discovery of new drugs, materials with novel properties, and advanced energy storage solutions.
Potential for a paradigm shift in molecular engineering and design, driven by AI-powered simulation capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG