#computational-chemistry News & Analysis

3 articles tagged with #computational-chemistry. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

3 articles

AIBullisharXiv – CS AI · 5d ago7/10

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EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers

EquiformerV3, an advanced SE(3)-equivariant graph neural network, achieves significant improvements in efficiency, expressivity, and generality for 3D atomistic modeling. The new version delivers 1.75x speedup, introduces architectural innovations like SwiGLU-S² activations and smooth-cutoff attention, and achieves state-of-the-art results on major molecular modeling benchmarks including OC20 and OMat24.

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AIBullisharXiv – CS AI · Mar 56/10

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Overcoming the Combinatorial Bottleneck in Symmetry-Driven Crystal Structure Prediction

Researchers developed a new AI-powered framework for crystal structure prediction that uses large language models and symmetry-driven generation to overcome computational bottlenecks. The approach achieves state-of-the-art performance in discovering new materials without relying on existing databases, potentially accelerating materials science research.

AIBullisharXiv – CS AI · Mar 37/107

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Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions

Researchers introduce DeMol, a new dual-graph framework for molecular property prediction that explicitly models both atoms and chemical bonds to achieve superior accuracy. The approach addresses limitations of conventional atom-centric models by incorporating bond-level phenomena like resonance and stereoselectivity, establishing new state-of-the-art results across multiple benchmarks.

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