Call for Papers

Submit via CMT here.

We invite submissions on topics at the intersection of machine learning and physical sciences, including but not limited to:

  • Materials processing, manufacturing, and industrial applications
  • Structure-property-performance and multi-scale materials modelling
  • Fluid mechanics, thermal modelling, and transport phenomena
  • Materials science and discovery for energy applications
  • Machine learning surrogates for high-fidelity physical simulations
  • Physics-informed and hybrid machine learning models
  • Multi-scale and multi-fidelity learning
  • Data-efficient learning, active learning, and experimental design
  • Uncertainty quantification, robustness, and generalization in scientific ML
  • Multi-physics and PDE-based modelling in energy and materials systems
  • Power and energy systems (generation, storage, grids, optimisation, forecasting)
  • Transportation and mobility systems
  • Smart cities and integrated urban energy systems
  • Climate science, climate modelling, and earth system emulation
  • Interpretability, explainability, and scientific insight extraction in machine learning for scientific and industrial applications
  • Closed-loop and autonomous discovery systems integrating simulations and experiments
  • Inverse materials design and optimisation
  • Generative models for novel materials and molecular systems
  • Transfer learning and meta-learning across materials domains
  • Learning from heterogeneous, incomplete, and multi-modal scientific datasets
  • Self-supervised, weakly supervised, and few-shot learning for scientific and materials data

Submission Tracks

  • Full papers (max 16 pages including references, LNCS format) for oral presentation (~15min + 5min Q&A) and publication in Springer LNCS. Get the template here.

  • Extended abstracts (2 pages including references) for recently published work in top-tier venues (JMLR, JAIR, MLJ, PAMI, IJCAI, NeurIPS, ICLR, AISTATS, ICML, or other SJR Q1 / CORE A* venues). Authors should indicate the original publication venue in the submission form. Accepted abstracts will be presented orally (~10min + 5min Q&A). Get the template here.

Reviewing format

Double-blind review