EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
Indian Institute of Technology Delhi（印度理工学院德里分校）；Aalborg University（奥尔堡大学）；Intel Labs（英特尔实验室）；Sorbonne University（索邦大学）
Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M Smedskjaer, Sayan Ranu, N M Anoop Krishnan
Equivariant graph neural networks force fields (EGraFFs) have shown great promise in modelling complex interactions in atomic systems by exploiting the graphs' inherent symmetries. Recent works have led to a surge in the development of novel architectures that incorporate equivariance-based inductive biases alongside architectural innovations like graph transformers and message passing to model atomic interactions. However, thorough evaluations of these deploying EGraFFs for the downstream task of real-world atomistic simulations, is lacking. To this end, here we perform a systematic benchmarking of 6 EGraFF algorithms (NequIP, Allegro, BOTNet, MACE, Equiformer, TorchMDNet), with the aim of understanding their capabilities and limitations for realistic atomistic simulations. In addition to our thorough evaluation and analysis on eight existing datasets based on the benchmarking literature, we release two new benchmark datasets, propose four new metrics, and three challenging tasks. The new datasets and tasks evaluate the performance of EGraFF to out-of-distribution data, in terms of different crystal structures, temperatures, and new molecules. Interestingly, evaluation of the EGraFF models based on dynamic simulations reveals that having a lower error on energy or force does not guarantee stable or reliable simulation or faithful replication of the atomic structures. Moreover, we find that no model clearly outperforms other models on all datasets and tasks. Importantly, we show that the performance of all the models on out-of-distribution datasets is unreliable, pointing to the need for the development of a foundation model for force fields that can be used in real-world simulations. In summary, this work establishes a rigorous framework for evaluating machine learning force fields in the context of atomic simulations and points to open research challenges within this domain.
Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所状态关键实验室）；University of Chinese Academy of Sciences（中国科学院大学）
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of Cambridge（剑桥大学）；University of Minnesota（明尼苏达大学）
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所处理器重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Bayesian E(3)-Equivariant Interatomic Potential with Iterative Restratification of Many-body Message Passing
Sungkyunkwan University（成均馆大学）；Institute for Basic Science (IBS)（基础科学研究院）；Ulsan National Institute of Science and Technology (UNIST)（蔚山科学技术院）；Korea Institute of Science and Technology Information（韩国科学技术信息研究院）；Gwangju Institute of Science and Technology（光州科学技术院）；KAIST（韩国科学技术院）
Spin-Weighted Spherical Harmonics Enable Complete and Scalable $\mathrm{E}(3)$-Equivariant Networks
Department of Computer Science and Engineering, Texas A&M University（德克萨斯农工大学计算机科学与工程系）；Lambda, Inc.（Lambda公司）；Department of Materials Science and Engineering, Texas A&M University（德克萨斯农工大学材料科学与工程系）；Department of Electrical and Computer Engineering, Texas A&M University（德克萨斯农工大学电气与计算机工程系）；Department of Physics and Astronomy, Texas A&M University（德克萨斯农工大学物理与天文学系）；Department of Mechanical Engineering, Texas A&M University（德克萨斯农工大学机械工程系）
Operator Forces For Coarse-Grained Molecular Dynamics
Department of Mathematics and Computer Science, Freie Universität, Berlin, Germany（数学与计算机科学系，弗里堡大学，柏林，德国）；Microsoft Research AI for Science, Berlin, Germany（微软研究院人工智能科学部，柏林，德国）；Department of Physics, Rice University, Houston, Texas 77005, USA（物理系，里士大学，休斯顿，德克萨斯州77005，美国）
Elign: Equivariant Diffusion Model Alignment from Foundational Machine Learning Force Fields
Department of Computer Science, Yale University, New Haven, USA（计算机科学系，耶鲁大学，新 Haven，USA）；Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, USA（计算生物学与生物医学信息学项目，耶鲁大学，新 Haven，USA）；IQuestLab
A General Framework for Equivariant Neural Networks on Reductive Lie Groups
University of Cambridge（剑桥大学）；ENS Paris-Saclay（巴黎萨克雷高等师范学院）；Université Paris-Saclay（巴黎萨克雷大学）；Massachusetts Institute of Technology（麻省理工学院）；EIA University（EIA大学）；University of British Columbia（不列颠哥伦比亚大学）
