The Frontier of UQ in MLIPs: Lessons from Our Panel on MLIPs
The rapid adoption of Machine Learning Interatomic Potentials (MLIPs) has transformed how we approach materials development. As error rates in foundational models continue to drop, the temptation is to view these models as infallible black boxes. But as any user of LLMs knows, foundation models have jagged performance, where accuracy can drop precipitously on similar looking tasks. For large language models, a human can glance at a response and say, "that’s wrong." In materials science, however, detecting those errors is far more complex.
In order to determine if a material prediction is correct or just a simulation artifact, uncertainty quantification is necessary to determine this autonomously. Here we recap our recent panel discussion that featured insights from Jenna Pope (Pacific Northwest National Laboratory) and Nicholas Wimer (National Laboratory of the Rockies) on their recent papers.
The Uncertainty Bottleneck
The fundamental challenge in MLIPs isn't just about accuracy; it’s about trust. As we continue to expand the use of machine learning models to predict material properties, we routinely fall back to intuition to validate machine learning model accuracy or if we are uncertain about experimental measurements. As materials development gets more complicated and simulations have to be used more to understand new materials that are being developed, the ability for humans and agents to intuit that a value is correct or not decreases rapidly making them unable to disambiguate between true physical behavior and model-induced noise.
With this backdrop, uncertainty quantification enables us to overcome the intuition gap to determine if a model output is reliable and trustworthy. Therefore, the discussion kicked off by framing the two primary types of uncertainty that we must navigate:
- Epistemic Uncertainty: Arising from model accuracy. The failure mode here is inability of models to accurately model the data.
- Aleatoric Uncertainty: Arising from data noise or measurement limitations.
These two concepts frame many of the methods being developed, where certain techniques are targeted to address one or the other, often providing synergistic benefits for hybrid approaches that combine multiple UQ strategies into a single architecture.
The Myth of Deep Ensembles
One of the most sobering takeaways from the session was the debunking of the "silver bullet" approach in deep ensembles. Nicholas Wimer presented findings from an extensive benchmarking campaign covering hundreds of models and hyperparameters. His conclusion? Deep ensembles, while often touted as the gold standard for epistemic uncertainty, are not universal. His research revealed that ensembles do not consistently outperform simpler, cheaper heuristic approaches. This was reinforced by the lack of a consistent champion UQ architecture across different chemistries or dataset splits.
Quantile Regression: A Powerful add-on to Foundation Models
A promising method to add on to pretrained models that are competitive with Deep Ensembles but at a fraction of the cost is quantile regression. Jenna Pope presented her research that demonstrated superior diagnostic power through targeted, post-hoc methods. By applying quantile regression to pre-trained foundation models, Pope shows that we can accurately identify when a model enters unphysical regimes. She showed how this is a critical capability for interfacial properties, such as the water-gold interface, where simple error margins fail to capture the nuance of the simulation.
Hot Takes from the Session
Our panelists weren't shy about sharing the field's uncomfortable truths. Here are the key "hot takes" that defined our discussion:
- Deep Ensembles: They remain popular not because they are universally superior, but because they are simple to understand and easy to implement in HPC environments.
- Aleatoric Modeling: Given the high-dimensional space of materials, true aleatoric uncertainty modeling remains an elusive goal, perhaps even impossible with current data densities and model architectures.
- The Metrics Gap: While a universal metrics platform would be ideal, we currently lack a standard for defining "good" UQ.
- Ranking isn't Enough: Even when we master ranking uncertain data, ranking is insufficient if we don't understand if the worst data is making the materials properties inaccurate. To address this, we need to have regression.
Moving From Ranking to Regression
To move beyond the current state of the art, where ranking is used to identify the worst points, UQ needs to improve to be able to identify how bad the worst points are. Relying solely on ranking assumes that knowing "this structure is worse than that one" is sufficient, but it fails to tell us why or by how much a model is failing. A more precise path forward requires transitioning from qualitative ranking to quantitative regression. This requires moving from ranking to regressing the uncertainty accurately for in and out of distribution data.
At PHIN, this is exactly where our models shine. Our latest UQ architecture enables high quality regression across orders of magnitude. This is a significant improvement on the ranking performance that our previous models had.