Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Development of an educational video to support guideline panels in incorporating patient values and preferences into recommendation-making: qualitative one-on-one interviews and brainstorming meetings. [PDF]
Zeng L +11 more
europepmc +1 more source
Becoming monstrous: Beauty norms, body image, and discursive limits on compassion in The Substance. [PDF]
Joy P.
europepmc +1 more source
Spatial and temporal distribution of Ixodes scapularis and tick-borne pathogens across the northeastern United States. [PDF]
Price LE +18 more
europepmc +1 more source
Is the Cat-Owner Relationship Related to Cat-Wildlife Conflicts? [PDF]
Schüttler E +3 more
europepmc +1 more source
Retrospective radiographic myelogram measurements and long-term outcomes in horses undergoing cervical interbody fusion surgery: 22 cases. [PDF]
England D +3 more
europepmc +1 more source
Controlling the Dynamic Behavior of Microposts in Solution via Diffusion-Convection. [PDF]
Moradi M, Shklyaev OE, Balazs AC.
europepmc +1 more source
Unification of drags and confluence of drag rewriting [PDF]
Drags are a recent, natural generalization of terms which admit arbitrary cycles. A key aspect of drags is that they can be equipped with a composition operator so that rewriting amounts to replace a drag by another in a composition. In this paper, we develop a unification algorithm for drags that allows to check the local confluence property of a set ...
Fernando Orejas Valdes +1 more
exaly +6 more sources
Drags: A compositional algebraic framework for graph rewriting [PDF]
We are interested in a natural generalization of term-rewriting techniques to what we call drags, viz. finite, directed, ordered, rooted multigraphs, each vertex of which is labeled by a function symbol.
Jean-Pierre Jouannaud
exaly +2 more sources

