Large language models on graphs: A comprehensive survey

B **, G Liu, C Han, M Jiang, H Ji… - IEEE Transactions on …, 2024‏ - ieeexplore.ieee.org
Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

A comprehensive survey of scientific large language models and their applications in scientific discovery

Y Zhang, X Chen, B **, S Wang, S Ji, W Wang… - arxiv preprint arxiv …, 2024‏ - arxiv.org
In many scientific fields, large language models (LLMs) have revolutionized the way text and
other modalities of data (eg, molecules and proteins) are handled, achieving superior …

ChatGPT as research scientist: probing GPT's capabilities as a research librarian, research ethicist, data generator, and data predictor

SA Lehr, A Caliskan, S Liyanage, MR Banaji - Proceedings of the National …, 2024‏ - pnas.org
How good a research scientist is ChatGPT? We systematically probed the capabilities of
GPT-3.5 and GPT-4 across four central components of the scientific process: as a Research …

Delocalized, asynchronous, closed-loop discovery of organic laser emitters

F Strieth-Kalthoff, H Hao, V Rathore, J Derasp… - Science, 2024‏ - science.org
Contemporary materials discovery requires intricate sequences of synthesis, formulation,
and characterization that often span multiple locations with specialized expertise or …

A hitchhiker's guide to geometric gnns for 3d atomic systems

A Duval, SV Mathis, CK Joshi, V Schmidt… - arxiv preprint arxiv …, 2023‏ - arxiv.org
Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …

Does invariant graph learning via environment augmentation learn invariance?

Y Chen, Y Bian, K Zhou, B **e… - Advances in Neural …, 2023‏ - proceedings.neurips.cc
Invariant graph representation learning aims to learn the invariance among data from
different environments for out-of-distribution generalization on graphs. As the graph …

Introduction to quantum computing and its integration applications

A Khang, V Abdullayev, AV Alyar, M Khalilov… - … and principles of …, 2024‏ - igi-global.com
As humans continue to evolve, they discover new worlds. One of them is the Quantum
World, one of the main discoveries of the last century. The Quantum World, which is the …

Temperature excavation to boost machine learning battery thermochemical predictions

Y Wang, X Feng, D Guo, H Hsu, J Hou, F Zhang, C Xu… - Joule, 2024‏ - cell.com
Advancing battery technologies requires precise predictions of thermochemical reactions
among multiple components to efficiently exploit the stored energy and conduct thermal …

Data-efficient operator learning via unsupervised pretraining and in-context learning

W Chen, J Song, P Ren… - Advances in …, 2025‏ - proceedings.neurips.cc
Recent years have witnessed the promise of coupling machine learning methods and
physical domain-specific insights for solving scientific problems based on partial differential …

Universal physics transformers: A framework for efficiently scaling neural operators

B Alkin, A Fürst, S Schmid, L Gruber… - Advances in …, 2025‏ - proceedings.neurips.cc
Neural operators, serving as physics surrogate models, have recently gained increased
interest. With ever increasing problem complexity, the natural question arises: what is an …