Experiments are not just for validation; they are a form of computation. By treating nature as a 'Physics Processing Unit' (PPU) working alongside digital GPUs, we can integrate physical experimentation directly into the computational loop, creating a powerful hybrid system for materials discovery.
The traditional scientific method in materials science—hypothesize, experiment, learn—is being replaced. AI enables a new paradigm: treating the vast space of all possible molecules as a searchable database. Scientists can now query for materials with desired properties, radically accelerating discovery.
To create a complex automated science platform, first build modular tools that human experts use in a manual workflow. Observe their process to identify bottlenecks and needed components (e.g., a stability test). Then, incrementally build agents to automate the orchestration of these proven tools.
While hard-coding physical symmetries (equivariance) into a model is theoretically efficient, it can fail in practice. Prof. Welling explains that these constraints can complicate the optimization landscape, making it harder to find good minima. Sometimes, abundant data augmentation with a simpler model yields superior results.
Contrary to sci-fi visions, the immediate future of AI in science is not the fully autonomous 'dark lab.' Prof. Welling's vision is to empower human domain experts with powerful tools. The scientist remains crucial for defining problems, interpreting results, and making final judgments, with AI as a powerful collaborator.
A deep, non-obvious connection exists between generative AI (diffusion models, RL) and the physics of non-equilibrium systems. Prof. Max Welling notes their mathematical foundations are the same. This allows AI researchers to borrow theorems from physics and physicists to use AI models, fueling cross-disciplinary innovation.
A career in science can evolve from pursuing pure intellectual curiosity (like quantum gravity) to prioritizing tangible impact. Prof. Max Welling describes this shift as a natural evolution with age, where a new dimension of making a positive impact on issues like climate change becomes a primary motivator.