Accelerating Polymer R&D with AI-Optimized Simulation
How a leading materials science company achieved 100x faster simulation speed and sub-10% error—enabling new insight into complex polymer behaviors
Executive Summary
A leading materials science company sought to improve the efficiency of polymer property simulations. While atomistic molecular dynamics (AAMD) offered high fidelity, each simulation required 2–3 days—constraining throughput and limiting exploration. Coarse-grained molecular dynamics (CGMD) — a simulation method that simplifies molecular detail to speed up computation— promised faster performance. However, widely used frameworks like Martini3 were originally developed for biomolecular systems such as proteins and lipids, not polymers. When applied to polymer simulations, they produced significant deviations in key material properties, limiting their utility for R&D.
To overcome this, the company implemented an AI-driven Multi-Objective Bayesian Optimization (MOBO) framework via the MIND Platform. This approach optimized coarse-grained molecular topologies for fidelity in density and radius of gyration (Rg), enabling simulations to converge in ~50 iterations with <10% error compared to atomistic benchmarks. Simulation time was reduced by 100x, enabling modeling of mesoscopic phenomena such as phase separation—previously inaccessible due to computational constraints.
The Challenge
The simulation team relied on AAMD to evaluate polymer properties. While scientifically robust, these simulations required 2–3 days per configuration, throttling how many materials could be evaluated in each R&D cycle. Attempts to accelerate this using Martini3-based CGMD approaches proved ineffective. Originally designed for biomolecular systems, Martini3 introduced large errors—up to 60% deviation—in key polymer metrics like density and radius of gyration (Rg), and lacked the flexibility to simulate mesoscopic polymer behaviors such as phase separation.
The team needed a new approach that preserved the fidelity of AAMD while delivering the performance of CGMD—at scale and with flexibility across diverse polymer systems.
Multiscale’s Solution
The company partnered with Multiscale Technologies and deployed the MIND Platform to address the speed-vs-accuracy dilemma. Together, they co-developed a Multi-Objective Bayesian Optimization (MOBO) framework that automatically tuned coarse-grained molecular topologies for fidelity and performance.
Rather than rely on out-of-the-box CGMD models, which were not optimized for polymers, the team used the MIND Platform to systematically re-parameterize coarse-grained molecular topologies for polymer-specific accuracy.
Key components included:
- Low-dimensional parameter optimization targeting start, middle, and end bonds
- Surrogate modeling using Gaussian Processes
- EHVI-based acquisition to balance exploration and accuracy
- Automated integration with Martini3 and internal toolchains
Configured for iterative learning and transferability, the solution enabled simulation of complex polymer behaviors—including phase separation—with higher confidence and faster turnaround.
Results
The joint effort delivered rapid, measurable improvements:
- 100x simulation speedup vs. atomistic methods
- <10% deviation from AAMD benchmarks in both density and Rg
- Accuracy improved from up to 60% error (Martini3) to <10% with Multiscale optimization
- Enabled modeling of mesoscopic behaviors like phase separation
- Achieved convergence in ~50 iterations per polymer system
Business Impact
By compressing simulation time from days to under an hour while maintaining scientific fidelity, the R&D team unlocked a new operational model for polymer research. They can now explore larger design spaces, test more hypotheses, and accelerate material screening without compromising accuracy.
The project also catalyzed broader adoption: the AI-optimized framework is now being extended to co-polymer modeling, with plans for rollout across a 100-person simulation team.