[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
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Updated
Apr 10, 2026 - Python
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers
Bilingual catalyst design guidance skill for AI agents (Claude Code / OpenClaw / Hermes / Cursor / WorkBuddy). 催化剂设计指导技能
Implement SE(3)-equivariant graph attention transformers for efficient and expressive molecular modeling in PyTorch.
Notebook for calculating adsorption energy from the total energies in OC22 dataset.
This is repository for "Controllable molecular graph generation from natural-language chemical constraints"
Machine learning (BGMM/GP) code for the Catassembly Triad framework in JACS. Validates catalyst efficacy prediction via triad descriptors (attachability, controllability, detachability).
Inverse catalyst design with GP surrogates and multi-objective BO. Validated on published propane-dehydrogenation data: recovers Ga-Mo top-yield and Mg-modified low-deactivation families.
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