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Molecular representation learning has emerged as a promising approach for modeling molecules with deep learning in chemistry and beyond. While 3D geometric models effectively capture molecular structure, they typically process single static conformers, overlooking the inherent flexibility and dynamics of molecules. In reality, many molecular properties depend on distributions of thermodynamically accessible conformations rather than single structures. Recent works show that learning from conformer ensembles can improve molecular representations, but existing approaches either produce unphysical structures through averaging or require restrictive molecular alignment. In this paper, we propose Symmetry-Preserving Conformer Ensemble networks (SPiCE), which introduces two key innovations: (1) geometric mixture-of-experts for selective processing of scalar and vector features, and (2) hierarchical ensemble encoding that combines ensemble level representation with cross-conformer integration. Crucially, SPiCE ensures physically meaningful representations by maintaining joint equivariance to geometric transformations of individual conformers and conformer permutations. Extensive experiments demonstrate that SPiCE consistently outperforms existing conformer ensemble methods and state-of-the-art structural aggregation models across quantum mechanical and biological property prediction tasks. 

Per- and Polyfluoroalkyl Substances (PFAS) are an emerging class of persistent organic pollutants. Although their thermal/chemical stability and water/stain repellence enable their widespread use in various products, such as personal care products, food packaging and firefighting foams, these properties also make them particularly resistant to degradation. This unwelcome persistence, with their trace concentrations, environmental prevalence, bioaccumulation and probable toxicities, poses a potential threat to environmental and human health. As such, much work is directed to finding ways to efficiently abate PFAS in the environment. Per- and Polyfluoroalkyl Substance Treatment Technologies provides a thorough review of the current state of research in treatment technologies for removing PFAS from the environment, particularly water. Beginning with a brief introduction to PFAS challenges and research needs, it covers established and promising technologies for PFAS removal from drinking water, wastewater, and groundwater. This is a great book for environmental engineers, environmental chemists, and industrialists interested in pollution remediation.