Scientific large language models: A survey on biological & chemical domains
Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …
natural language comprehension, representing a significant stride toward artificial general …
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …
proteins, and materials, represent them as geometric graphs with atoms embedded as …
DPA-2: a large atomic model as a multi-task learner
D Zhang, X Liu, X Zhang, C Zhang, C Cai… - npj Computational …, 2024 - nature.com
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes
in atomic modeling, simulation, and design. AI-driven potential energy models have …
in atomic modeling, simulation, and design. AI-driven potential energy models have …
Where did the gap go? reassessing the long-range graph benchmark
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of
graph learning tasks strongly dependent on long-range interaction between vertices …
graph learning tasks strongly dependent on long-range interaction between vertices …
MolE: a foundation model for molecular graphs using disentangled attention
Abstract Models that accurately predict properties based on chemical structure are valuable
tools in the chemical sciences. However, for many properties, public and private training sets …
tools in the chemical sciences. However, for many properties, public and private training sets …
DPA-2: Towards a universal large atomic model for molecular and material simulation
D Zhang, X Liu, X Zhang, C Zhang, C Cai, H Bi… - arxiv preprint arxiv …, 2023 - arxiv.org
The rapid development of artificial intelligence (AI) is driving significant changes in the field
of atomic modeling, simulation, and design. AI-based potential energy models have been …
of atomic modeling, simulation, and design. AI-based potential energy models have been …
DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models
Recent advances in decentralized deep learning algorithms have demonstrated cutting-
edge performance on various tasks with large pre-trained models. However a pivotal …
edge performance on various tasks with large pre-trained models. However a pivotal …
Generative ai in medicine
The increased capabilities of generative AI have dramatically expanded its possible use
cases in medicine. We provide a comprehensive overview of generative AI use cases for …
cases in medicine. We provide a comprehensive overview of generative AI use cases for …
Graphfm: A scalable framework for multi-graph pretraining
Graph neural networks are typically trained on individual datasets, often requiring highly
specialized models and extensive hyperparameter tuning. This dataset-specific approach …
specialized models and extensive hyperparameter tuning. This dataset-specific approach …
Reducing the cost of quantum chemical data by backpropagating through density functional theory
Density Functional Theory (DFT) accurately predicts the quantum chemical properties of
molecules, but scales as $ O (N_ {\text {electrons}}^ 3) $. Sch\" utt et al.(2019) successfully …
molecules, but scales as $ O (N_ {\text {electrons}}^ 3) $. Sch\" utt et al.(2019) successfully …