Recent advancements in geometric learning and representation­ learning from disciplines such as social networks, chemistry, and natural language processing have brought revolutionary and rapid changes in how data stored in sequences, graphs and manifolds can be modeled and used as the basis for predictions. Interestingly, the topological information of grain contacts, including those that represent the fragmentation of particles, and topologies of graphs that reflect feature correlations in grain ensembles, offer great opportunities for data driven learning approaches. Such approaches may assist in achieving many important research goals including: detecting emergent patterns and phase transitions, discovering underlying mechanisms, and deducing governing material laws and constitutive models. Recent advancements in geometric learning and representation¬ learning from disciplines such as social networks, chemistry, and natural language processing have brought revolutionary and rapid changes in how data stored in sequences, graphs and manifolds can be modeled and used as the basis for predictions. Interestingly, the topological information of grain contacts, including those that represent the fragmentation of particles, and topologies of graphs that reflect feature correlations in grain ensembles, offer great opportunities for data driven learning approaches. Such approaches may assist in achieving many important research goals including detecting emergent patterns and phase transitions, discovering underlying mechanisms, and deducing governing material laws and constitutive models. This topical collection is specifically aimed at harnessing the big data and machine learning revolution to impact the broad science of granular matter and to facilitate new ideas, collaborations, and synergies across the different disciplines that contribute to basic research of granular media. Specific topics of interest include, but are not limited to: (a) Unsupervised learning that generates low dimensional representations for predictions (b) Graph convolutional networks and applications of graph and complex network theories for material characterization and modelling (c) Generative adversarial machine learning approaches for synthesis of new materials (d) applications of group theory and equivalence for physics predictions (e) Uncertainty quantification (f) Physics inspired deep learning and spatio-temporal deep learning approaches (g) Any other creative applications of artificial and augmented intelligence approaches that advance the state-of-the-art knowledge on granular matter Specific topics of interest include, but are not limited to: (a) unsupervised learning that generates low dimensional representations for predictions, (b) graph convolutional networks and applications of graph and complex network theories for material characterization and modelling, (c) generative adversarial machine learning approaches for synthesis of new materials, (d) applications of group theory and equivalence for physics predictions, (e) uncertainty quantification, and (f) physics inspired deep learning and spatio-temporal deep learning approaches, and (g) any other creative applications of artificial and augmented intelligence approaches that advance the state-of-the-art knowledge on granular matter.

The United Nations Sustainable Development Goal 7 (UN SDG 7) aims for universal access to affordable, reliable, sustainable, and modern energy for all by 2030. To achieve this goal, recent decades have seen dramatic technological developments across multiple disciplines for driving down energy costs, improving efficiency, and enabling the integration of intermittent renewable sources into the grid. Amongst these technological advancements, micromechanics of granular matter also play an important role.

This topical collection is aimed at demonstrating how state-of-the-art research in granular matter makes contribution to the promotion of energy sustainability to meet the SDG 7.

Specific topics of interest include, but are not limited to:

• the interactions between the soils and foundations supporting offshore wind/wave/tidal energy harvest platforms

• heat storage and transfer through soil during geothermal energy harvest

• thermal behavior of granular solids in solar energy storage

• granular matters in battery technology

• micromechanics for enhancing carbon capture efficiency

• mitigating hydraulic erosion to ensure hydropower dam integrity

In this topical collection, the papers will address one of the application areas and have clear connections to the aims and scopes of the journal about understanding of the micromechanics and physics of Granular Matter.

All submissions will undergo rigorous external peer review handled by the editors-in-chief and co-editors, to ensure that the high-quality standards of the journal are met.