# Workshop on Machine Learning Multiscale Processes

Given low-level theory and computationally-expensive simulation code, how can we model complex systems on a useful time scale?

Fundamental laws of Nature, Standard Model of Physics, and the most applied part of it, quantum mechanics, are well established. Theoretically, the dynamics of anything starting from a hydrogen atom and all the way to Earth's climate follow those equations. The problem is complexity [Dirac 1929]. An exact computation of a modest system containing 100 atoms is still beyond the capability of modern computers.

Some of the greatest scientific achievements resulted from breakthroughs in scale transitions: renormalization, density functional theory, Higgs boson, multiscale models for complex chemical systems, climate modeling, protein folding. Those achievements are highly regarded because they are impactful – but also unique and can't be readily applied to different systems.

Encouraged by the recent successes, this workshop aims to enable the development of universal AI methods that would be able to find efficient and accurate approximations, and use them for some of the most pressing and high-impact scientific problems that have computational complexity as the limiting factor to an in silico solution, such as:

- High-temperature superconductivity
- Fusion power
- Weather prediction
- Living organism digital twins
- Catalysts

If we solve scale transition, we solve science.

We are looking for contributions that will bring us closer to the building an AI that can advance from low–level theory and computationally–expensive simulation code to modeling complex systems on a useful time scale. All submissions will be evaluated based on their relevance to this goal.

United by its goal, the workshop invites researchers working at all scales of nature: from the Planck length to the size of Universe, including quantum physics, chemistry, biology, materials science, mesoscopic physics, climate & weather, and astrophysics. We also look forward to cross–pollination of diverse methodologies: dimensionality reduction, manifold learning, Hamiltonian learning, PDE, ODE, symbolic reasoning, RL–based theory exploration, tuning computational models with experimental data, operator learning, physics–informed neural networks, surrogate modelling, digital twins, and more.

## Tracks 
### New scientific result 
A normal paper that presents a new scientific result. Such papers are evaluated on a balance of novelty, significance, and technical quality. Page limit is 6 pages. Publication of code and data is encouraged, but not mandatory. Reviewers are allowed to consider open source as a positive contribution to the study significance.

### Dataset or benchmark 
A work that presents a new dataset or benchmark – a way to measure progress in the field. Upon paper acceptance, the dataset must be open and available to the community; source code must be released under an OSI–approved license. In terms of evaluation, technical quality and significance are the most important criteria. Page limit is 6 pages.

### Findings and open challenges 
This is the track for significance and novelty. Submissions can have no code and experiments at all, but the authors still carry the burden to convince the reviewers that their ideas are worth exploring. We are looking for submissions introducing and discussing overlooked scientific questions and potential future directions for a given application area. We encourage submission that address open challenges and describe: 1. Why the current research and state-of-the-art fall short for a given challenges; 2. What directions the authors believe the community can focus on to help address the open challenge. Page limit is 6 pages. Track idea by AI4AM

### Engineering 
Working with complex systems requires good software engineering. In this track we are looking for contributions that introduce advancements in modelling software for complex systems. Contributions can be tools, libraries, frameworks, or infrastructure. The most important criteria are technical quality and significance. The code must be released under an OSI–approved license. 

### Negative result 
A paper that presents a thorough experimental investigation of approaches which, despite considerable effort, did not improve over the current state-of-the-art methods. Submissions should detail the experimental design, document the encountered challenges, and provide a critical analysis of the negative findings along with lessons learned to guide future research. Emphasis is placed on technical rigor, reproducibility, and the broader impact of learning from failure. Page limit is 6 pages. Publication of code and data is encouraged, but not mandatory.