Daily Papers

Large Language Models (LLMs) promise to accelerate discovery by reasoning across the expanding scientific landscape. Yet, the challenge is no longer access to information but connecting it in meaningful, domain-spanning ways. In materials science, where innovation demands integrating concepts from molecular chemistry to mechanical performance, this is especially acute. Neither humans nor single-agent LLMs can fully contend with this torrent of information, with the latter often prone to hallucinations. To address this bottleneck, we introduce a multi-agent framework guided by large-scale knowledge graphs to find sustainable substitutes for per- and polyfluoroalkyl substances (PFAS)-chemicals currently under intense regulatory scrutiny. Agents in the framework specialize in problem decomposition, evidence retrieval, design parameter extraction, and graph traversal, uncovering latent connections across distinct knowledge pockets to support hypothesis generation. Ablation studies show that the full multi-agent pipeline outperforms single-shot prompting, underscoring the value of distributed specialization and relational reasoning. We demonstrate that by tailoring graph traversal strategies, the system alternates between exploitative searches focusing on domain-critical outcomes and exploratory searches surfacing emergent cross-connections. Illustrated through the exemplar of biomedical tubing, the framework generates sustainable PFAS-free alternatives that balance tribological performance, thermal stability, chemical resistance, and biocompatibility. This work establishes a framework combining knowledge graphs with multi-agent reasoning to expand the materials design space, showcasing several initial design candidates to demonstrate the approach.

Sustainable Aviation Fuels (SAF) are pivotal in the global effort to decarbonize the aviation sector and meet greenhouse gas (GHG) reduction targets established by international frameworks such as CORSIA and Brazil ProBioQAV. This study evaluates SAF potential to reduce lifecycle carbon emissions by up to 80% while being compatible with existing aviation infrastructure. Through bibliometric analysis, scenario evaluation, legal and regulatory framework analysis and economic modeling, the research examines two key SAF production technologies: Hydroprocessed Esters and Fatty Acids Synthetic Paraffinic Kerosene (HEFA-SPK) and Alcohol-to-Jet (ATJ) pathways in the Brazilian context. The findings reveal significant economic challenges, particularly high feedstock and production costs, which hinder SAF competitiveness with fossil fuels at recent and current market prices in Brazil, leading to the analysis of potential incentives and commercial conditions aiming to increase economic attractiveness of SAF production. Based on interviews with relevant stakeholders and decision makers in the industry, scenarios incorporating tax incentives, carbon credits, capital grants, and premium pricing for SAF and its biogenic by-products demonstrate that combined policy interventions and commercial arrangements, along with a regulated Carbon Market are essential for SAF economic viability. Future research is suggested to look at regional assessments of feedstock availability, supply chain logistics, and global market eligibility. This research provides insights for guiding public policy and private investment to support the transition to sustainable aviation in Brazil and beyond.

Many environmental remediation and energy applications (conversion and storage) for sustainability need design and development of green novel materials. Discovery processes of such novel materials are time taking and cumbersome due to large number of possible combinations and permutations of materials structures. Often theoretical studies based on Density Functional Theory (DFT) and other theories, coupled with Simulations are conducted to narrow down sample space of candidate materials, before conducting laboratory-based synthesis and analytical process. With the emergence of artificial intelligence (AI), AI techniques are being tried in this process too to ease out simulation time and cost. However tremendous values of previously published research from various parts of the world are still left as labor-intensive manual effort and discretion of individual researcher and prone to human omissions. AIMS-EREA is our novel framework to blend best of breed of Material Science theory with power of Generative AI to give best impact and smooth and quickest discovery of material for sustainability. This also helps to eliminate the possibility of production of hazardous residues and bye-products of the reactions. AIMS-EREA uses all available resources -- Predictive and Analytical AI on large collection of chemical databases along with automated intelligent assimilation of deep materials knowledge from previously published research works through Generative AI. We demonstrate use of our own novel framework with an example, how this framework can be successfully applied to achieve desired success in development of thermoelectric material for waste heat conversion.

Agricultural residues represent a vast, underutilized resource for renewable energy. This study combines empirical analysis from 179 countries with a case study of a pelletization facility to evaluate the global potential of agricultural pelletization for fossil fuel replacement. The findings estimate a technical availability of 1.44 billion tons of crop residues suitable for pellet production, translating to a 4.5% potential displacement of global fossil fuel energy use, equating to 22 million TJ and equivalent to 917 million tons of coal annually. The economically optimized scenario projects annual savings of $163 billion and a reduction of 1.35 billion tons of CO2 equivalent in emissions. Utilizing the custom-developed CLASP-P and RECOP models, the study further demonstrates that agricultural pellets can achieve competitive pricing against conventional fossil fuels in many markets. Despite logistical and policy challenges, agricultural pelletization emerges as a scalable, market-driven pathway to support global decarbonization goals while fostering rural economic development. These results reinforce the need for targeted investment, technological advancement, and supportive policy to unlock the full potential of agricultural pellets in the renewable energy mix.

Tourism Recommender Systems (TRS) have traditionally focused on providing personalized travel suggestions, often prioritizing user preferences without considering broader sustainability goals. Integrating sustainability into TRS has become essential with the increasing need to balance environmental impact, local community interests, and visitor satisfaction. This paper proposes a novel approach to enhancing TRS for sustainable city trips using Large Language Models (LLMs) and a modified Retrieval-Augmented Generation (RAG) pipeline. We enhance the traditional RAG system by incorporating a sustainability metric based on a city's popularity and seasonal demand during the prompt augmentation phase. This modification, called Sustainability Augmented Reranking (SAR), ensures the system's recommendations align with sustainability goals. Evaluations using popular open-source LLMs, such as Llama-3.1-Instruct-8B and Mistral-Instruct-7B, demonstrate that the SAR-enhanced approach consistently matches or outperforms the baseline (without SAR) across most metrics, highlighting the benefits of incorporating sustainability into TRS.

Material and product life cycles are based on complex value chains of technology-specific elements. Resource strategy aspects of essential and strategic raw materials have a direct impact on applications of new functionalized materials or the development of novel products. Thus, an urgent challenge of modern materials science is to obtain information about the supply risk and environmental aspects of resource utilization, especially at an early stage of basic research. Combining the fields of materials science, industrial engineering and resource strategy enables a multidisciplinary research approach to identify specific risks within the value chain, aggregated as the so-called resource criticality. Here, we demonstrate a step-by-step criticality assessment in the sector of basic materials research for multifunctional hexagonal manganite YMnO3, which can be a candidate for future electronic systems. Raw material restrictions can be quantitatively identified, even at such an early stage of materials research, from eleven long-term indicators including our new developed Sector Competition Index. This approach for resource strategy for modern material science integrates two objective targets: reduced supply risk and enhanced environmental sustainability of new functionalized materials, showing drawbacks but also benefits towards a sustainable materials research and development.

Tourism affects not only the tourism industry but also society and stakeholders such as the environment, local businesses, and residents. Tourism Recommender Systems (TRS) can be pivotal in promoting sustainable tourism by guiding travelers toward destinations with minimal negative impact. Our paper introduces a composite sustainability indicator for a city trip TRS based on the users' starting point and month of travel. This indicator integrates CO2e emissions for different transportation modes and analyses destination popularity and seasonal demand. We quantify city popularity based on user reviews, points of interest, and search trends from Tripadvisor and Google Trends data. To calculate a seasonal demand index, we leverage data from TourMIS and Airbnb. We conducted a user study to explore the fundamental trade-offs in travel decision-making and determine the weights for our proposed indicator. Finally, we demonstrate the integration of this indicator into a TRS, illustrating its ability to deliver sustainable city trip recommendations. This work lays the foundation for future research by integrating sustainability measures and contributing to responsible recommendations by TRS.

The environmental impact of Artificial Intelligence (AI) is emerging as a significant global concern, particularly regarding model training. In this paper, we introduce GREEN (Guided Recommendations of Energy-Efficient Networks), a novel, inference-time approach for recommending Pareto-optimal AI model configurations that optimize validation performance and energy consumption across diverse AI domains and tasks. Our approach directly addresses the limitations of current eco-efficient neural architecture search methods, which are often restricted to specific architectures or tasks. Central to this work is EcoTaskSet, a dataset comprising training dynamics from over 1767 experiments across computer vision, natural language processing, and recommendation systems using both widely used and cutting-edge architectures. Leveraging this dataset and a prediction model, our approach demonstrates effectiveness in selecting the best model configuration based on user preferences. Experimental results show that our method successfully identifies energy-efficient configurations while ensuring competitive performance.

Machine learning inference occurs at a massive scale, yet its environmental impact remains poorly quantified, especially on low-resource hardware. We present ML-EcoLyzer, a cross-framework tool for measuring the carbon, energy, thermal, and water costs of inference across CPUs, consumer GPUs, and datacenter accelerators. The tool supports both classical and modern models, applying adaptive monitoring and hardware-aware evaluation. We introduce the Environmental Sustainability Score (ESS), which quantifies the number of effective parameters served per gram of CO_2 emitted. Our evaluation covers over 1,900 inference configurations, spanning diverse model architectures, task modalities (text, vision, audio, tabular), hardware types, and precision levels. These rigorous and reliable measurements demonstrate that quantization enhances ESS, huge accelerators can be inefficient for lightweight applications, and even small models may incur significant costs when implemented suboptimally. ML-EcoLyzer sets a standard for sustainability-conscious model selection and offers an extensive empirical evaluation of environmental costs during inference.

Eight percent of global carbon dioxide emissions can be attributed to the production of cement, the main component of concrete, which is also the dominant source of CO2 emissions in the construction of data centers. The discovery of lower-carbon concrete formulae is therefore of high significance for sustainability. However, experimenting with new concrete formulae is time consuming and labor intensive, as one usually has to wait to record the concrete's 28-day compressive strength, a quantity whose measurement can by its definition not be accelerated. This provides an opportunity for experimental design methodology like Bayesian Optimization (BO) to accelerate the search for strong and sustainable concrete formulae. Herein, we 1) propose modeling steps that make concrete strength amenable to be predicted accurately by a Gaussian process model with relatively few measurements, 2) formulate the search for sustainable concrete as a multi-objective optimization problem, and 3) leverage the proposed model to carry out multi-objective BO with real-world strength measurements of the algorithmically proposed mixes. Our experimental results show improved trade-offs between the mixtures' global warming potential (GWP) and their associated compressive strengths, compared to mixes based on current industry practices. Our methods are open-sourced at github.com/facebookresearch/SustainableConcrete.