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Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA

The rapidly increasing demand for generative artificial intelligence (AI) models requires extensive server installation with sustainability implications in terms of the compound energy–water–climate impacts. Here we show that the deployment of AI servers across the United States could generate an annual water footprint ranging from 731 to 1,125 million m3 and additional annual carbon emissions from 24 to 44 Mt CO2-equivalent between 2024 and 2030, depending on the scale of expansion. Other factors, such as indus…

Nature Sustainability · Climate

Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA

Evidence-backed gain

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These…

Climate

Evidence-backed gain

A survey on large language model based autonomous agents

Abstract Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autono…

Science

Evidence-backed problem

Deepfake Technology and Gender-Based Violence: A Scoping Review

Online violence against women (OVAW) is a growing global problem with deepfakes in gender-based violence as one manifestation of this that has recently attracted considerable attention. This scoping review aims to explore emerging complexities in current academic understandings of deepfake in relation to its use in gender-based violence. The review considers how these issues impact and shape what is currently known about deepfakes in relation to OVAW. Articles were collected between July and September 2024 and t…

Science

Evidence-backed gain

Artificial intelligence for quantum computing

Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on development…

Science

Evidence-backed gain

Applications and Advances of Machine Learning in the Development of Solid-State Electrolytes for Lithium-Ion Batteries

Solid-state electrolytes (SSEs) have attracted considerable attention for their ability to effectively suppress lithium dendrite growth and enhance the safety and life cycle of lithium-ion batteries (LIBs). However, the commercialization of SSEs has been hindered by low ionic conductivity, limited mechanical strength, and poor interfacial compatibility. Recently, machine learning (ML) has arisen as a helpful tool in SSE studies owing to its efficient data processing and pattern recognition capabilities. This pap…

Science

Evidence-backed gain

AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications

Enzyme engineering drives innovation in biotechnology, medicine, and industry, yet conventional approaches remain limited by labour-intensive workflows, high costs, and narrow sequence diversity. Artificial intelligence (AI) is revolutionising this field by enabling rapid, precise, and data-driven enzyme design. Machine learning and deep learning models such as AlphaFold2, RoseTTAFold, ProGen, and ESM-2 accurately predict enzyme structure, stability, and catalytic function, facilitating rational mutagenesis and…

Science

The carbon and water footprints of data centers and what this could mean for artificial intelligence

Although there are ways to estimate the global power demand of artificial intelligence (AI) systems, it remains challenging to quantify the associated carbon and water footprints. The lack of distinction between AI and non-AI workloads in the environmental reports of data center operators makes it possible to assess the environmental impact of AI workloads only by approximating them through data centers' general performance metrics. The environmental disclosure of tech companies is, however, often insufficient t…

Climate

The carbon and water footprints of data centers and what this could mean for artificial intelligence

Can Generative AI improve social science?

Generative AI that can produce realistic text, images, and other human-like outputs is currently transforming many different industries. Yet it is not yet known how such tools might influence social science research. I argue Generative AI has the potential to improve survey research, online experiments, automated content analyses, agent-based models, and other techniques commonly used to study human behavior. In the second section of this article, I discuss the many limitations of Generative. I examine how bias…

Science

Self-optimizing machine learning potential assisted automated workflow for highly efficient complex systems material design

Abstract Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled…

Science

Digital materials ecosystem: from databases to AI agents for autonomous discovery

The concept of a digital materials ecosystem represents a new paradigm in materials research, where data, theory, and automation are integrated into a unified and iterative framework. By combining reliable databases, physical frameworks, and intelligent data analysis, materials discovery is evolving from empirical exploration toward a systematic and predictive science. The rapid growth of data and artificial intelligence (AI) has enabled the identification of complex structure-property relationships, while advan…

Science

Thinking Machines: Mathematical Reasoning in the Age of LLMs

Large Language Models (LLMs) have demonstrated impressive capabilities in structured reasoning and symbolic tasks, with coding emerging as a particularly successful application. This progress has naturally motivated efforts to extend these models to mathematics, both in its traditional form, expressed through natural-style mathematical language, and in its formalized counterpart, expressed in a symbolic syntax suitable for automatic verification. Yet, despite apparent parallels between programming and proof cons…

Science

ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models

ChatMOF is an artificial intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4, GPT-3.5-turbo, and GPT-3.5-turbo-16k), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid and formal structured queries. The system is comprised of three core components (i.e., an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety…

Science

ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models

The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization

Abstract Future tools for supporting collaborations between technology and sustainable development include artificial intelligence (AI) applications in sustainable Urbanization roles. This article highlights the various applications of AI in advancing sustainable urbanization. From urban planning to disaster management, AI technology is revolutionizing the way cities are designed and managed. By leveraging data analytics, machine learning, and predictive modeling, AI is helping city officials make informed decis…

Climate

The smart future for sustainable development: Artificial intelligence solutions for sustainable urbanization

Synthesis of covalent organic frameworks for photocatalytic hydrogen peroxide production guided by large language models

The photosynthetic production of hydrogen peroxide (H2O2) from water and oxygen presents a sustainable alternative to the energy-intensive anthraquinone process. Covalent organic frameworks (COFs) have emerged as promising photocatalysts for H2O2 generation. However, most existing COF photocatalysts yield H2O2 at concentrations too low for practical applications, largely due to ongoing challenges in simultaneously optimizing photocatalytic activity and structural stability. Here, we introduce a large language mo…

Science

Synthesis of covalent organic frameworks for photocatalytic hydrogen peroxide production guided by large language models