Designing High-Entropy Alloys With Low Stacking Fault Energy Through Interpretable Machine Learning

Today's article comes from the journal of Machine Learning and Knowledge Extraction. The authors are Maltoni et al., from the University of Bologna, in Italy. In this paper, They want to know two things: How a transformer actually implements multi-step deductive reasoning internally. And whether that computation differs when the reasoning happens through generated intermediate tokens versus through the model's layers.

Today's article comes from the journal of Memories - Materials, Devices, Circuits and Systems. The authors are Shih et al., from Imec, in Belgium. In this paper they're showcasing a new type of computer memory. It works by storing binary information as ordered chains of two different nanoparticle types inside billions of liquid-filled nanocapillaries.

Today's article comes from the journal of Data-Centric Engineering. The authors are Savasta et al., from the Olesinki firm, in the United Kingdom. In this paper they're taking a new algorithmic approach to motorboat design. Whereas a normal process might need to solve for geometric fit, adjacency, or accessibility, these designers also need to consider something that is much trickier to codify: visual aesthetics.

Today's article comes from the journal of Machine Learning: Science and Technology. The authors are Harb et al., from Argonne National Laboratory, in Illinois. In this paper, they're showcasing a new prompt-level reasoning enhancement framework, that they claim can improve LLM performance on ChemBench by several percentage points.

Today's article comes from the journal of Materials Genome Engineering Advances. The authors are Shu et al., from Xi'an Jiaotong University, in China. In this paper, they've taken DeepSeek-R1 and fine-tuned it for a very specific task: lattice-structure design.