arXiv:2509.17208v3 Announce Type: replace Abstract: Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and generating widespread all-atom (AA) data to combat this is computationally infeasible. We propose a novel active learning (AL) framework for CG neural network potentials in molecular dynamics (MD). Building on the CGSchNet model, our method employs root mean squared deviation (RMSD)-based frame selection from MD simulations in order to generate data on-the-fly by querying an oracle during the tra
The increasing computational demands of molecular dynamics simulations and the maturity of machine learning techniques for accelerating scientific discovery are converging to create new efficiencies.
This development allows for more accurate and efficient simulation of biomolecular systems, which is critical for drug discovery, material science, and understanding fundamental biological processes.
The ability to generate molecular dynamics data on-the-fly and refine machine-learned potentials significantly reduces the computational cost and time previously required for high-fidelity simulations.
- · Pharmaceutical R&D
- · Material Science
- · Biotechnology
- · AI/ML for scientific discovery
- · Traditional high-throughput screening methods
- · Computational chemistry relying solely on brute-force AA simulations
Faster and more cost-effective development of new drugs and materials due to improved simulation capabilities.
Increased pace of scientific breakthroughs in fields dependent on molecular understanding, leading to new intellectual property and products.
Potential for completely autonomous discovery platforms where AI systems design, simulate, and perhaps even synthesize novel molecules with minimal human intervention.
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Read at arXiv cs.LG