AI and Supercomputing are Powering the Next Wave of Breakthrough Science - But at What Cost?
- URL: http://arxiv.org/abs/2511.12686v1
- Date: Sun, 16 Nov 2025 17:02:27 GMT
- Title: AI and Supercomputing are Powering the Next Wave of Breakthrough Science - But at What Cost?
- Abstract summary: We identify how AI and HPC interact to shape research outcomes across 27 fields.<n>Papers combining the two technologies are up to three times more likely to introduce novel concepts.<n>Findings suggest that the future of discovery will depend not only on algorithms and compute, but also on how equitably the world shares these transformative tools.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial intelligence (AI) and high-performance computing (HPC) are rapidly becoming the engines of modern science. However, their joint effect on discovery has yet to be quantified at scale. Drawing on metadata from over five million scientific publications (2000-2024), we identify how AI and HPC interact to shape research outcomes across 27 fields. Papers combining the two technologies are up to three times more likely to introduce novel concepts and five times more likely to reach top-cited status than conventional work. This convergence of AI and HPC is redefining the frontier of scientific creativity but also deepening global inequalities in access to computational power and expertise. Our findings suggest that the future of discovery will depend not only on algorithms and compute, but also on how equitably the world shares these transformative tools.
Related papers
- The AI-Enabled Scientific Frontier [1.6725669564247125]
We assemble a corpus of 2,507 head-to-head comparisons between AI and other scientific analysis techniques.<n>Relative to traditional statistics, AI often outperforms, but at a significantly higher computational cost.<n>But there are also nearly a quarter of cases where AI is both more expensive and performs worse than traditional statistical techniques.
arXiv Detail & Related papers (2026-09-14T19:22:54Z) - AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
arXiv Detail & Related papers (2025-11-26T02:10:28Z) - Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.<n>AGS aims to significantly reduce the time and resources needed for scientific discovery.<n>As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
arXiv Detail & Related papers (2025-03-28T14:00:27Z) - Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science [25.683422870223076]
We present a large-scale analysis of the AI4Science literature.<n>We quantitatively highlight key disparities between AI methods and scientific problems.<n>We explore the potential and challenges of facilitating collaboration between AI and scientific communities.
arXiv Detail & Related papers (2024-11-27T00:40:51Z) - Drivers and Barriers of AI Adoption and Use in Scientific Research [0.0]
We study the integration of AI in scientific research, focusing on the human capital of scientists and the external resources available within their network of collaborators and institutions.
Our results suggest that AI is pioneered by domain scientists with a taste for exploration' and who are embedded in a network rich of computer scientists, experienced AI scientists and early-career researchers.
arXiv Detail & Related papers (2023-12-15T14:49:13Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery
through Sophisticated AI System Technologies [116.09762105379241]
DeepSpeed4Science aims to build unique capabilities through AI system technology innovations.
We showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.
arXiv Detail & Related papers (2023-10-06T22:05:15Z) - Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems [268.585904751315]
New area of research known as AI for science (AI4Science)<n>Areas aim at understanding the physical world from subatomic (wavefunctions and electron density), atomic (molecules, proteins, materials, and interactions), to macro (fluids, climate, and subsurface) scales.<n>Key common challenge is how to capture physics first principles, especially symmetries, in natural systems by deep learning methods.
arXiv Detail & Related papers (2023-07-17T12:14:14Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - AI for Science: An Emerging Agenda [30.260160661295682]
This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling"
The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains.
Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers.
arXiv Detail & Related papers (2023-03-07T20:21:43Z) - Predicting the Future of AI with AI: High-quality link prediction in an
exponentially growing knowledge network [15.626884746513712]
We use AI techniques to predict the future research directions of AI itself.
For that, we use more than 100,000 research papers and build up a knowledge network with more than 64,000 concept nodes.
The most powerful methods use a carefully curated set of network features, rather than an end-to-end AI approach.
arXiv Detail & Related papers (2022-09-23T14:04:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.