Sustainable Materials Discovery in the Era of Artificial Intelligence

• URL: http://arxiv.org/abs/2601.21527v1
• Date: Thu, 29 Jan 2026 10:42:44 GMT
• Title: Sustainable Materials Discovery in the Era of Artificial Intelligence
• Authors: Sajid Mannan, Rupert J. Myers, Rohit Batra, Rocio Mercado, Lothar Wondraczek, N. M. Anoop Krishnan,
• Abstract summary: We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment.<n>The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization.
• Score: 3.222363676081407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current AI workflows optimize performance first, deferring sustainability to post synthesis assessment. This creates inefficiency by the time environmental burdens are quantified, resources have been invested in potentially unsustainable solutions. The disconnect between atomic scale design and lifecycle assessment (LCA) reflects fundamental challenges, data scarcity across heterogeneous sources, scale gaps from atoms to industrial systems, uncertainty in synthesis pathways, and the absence of frameworks that co-optimize performance with environmental impact. We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment. The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization enabling simultaneous performance-sustainability navigation. Case studies spanning glass, cement, semiconductor photoresists, and polymers demonstrate both necessity and feasibility while identifying material-specific integration challenges. Realizing ML-LCA demands coordinated advances in data infrastructure, ex-ante assessment methodologies, multi-objective optimization, and regulatory alignment enabling the discovery of materials that are sustainable by design rather than by chance.

Related papers

• PeroMAS: A Multi-agent System of Perovskite Material Discovery [51.859972927223936]
  Perovskite Solar Cells (PSCs) are renowned for their superior optoelectronic performance and cost potential.<n>Existing AI approaches focus predominantly on discrete models, including material design, process optimization, and property prediction.<n>We propose a multi-agent system for perovskite material discovery, named PeroMAS.
  arXiv  Detail & Related papers  (2026-02-10T09:33:06Z)
• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration [63.61423859450929]
  This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses.<n>We identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery.
  arXiv  Detail & Related papers  (2026-01-20T18:46:42Z)
• Interpretable Hybrid Deep Q-Learning Framework for IoT-Based Food Spoilage Prediction with Synthetic Data Generation and Hardware Validation [0.5417521241272645]
  The need for an intelligent, real-time spoilage prediction system has become critical in modern IoT-driven food supply chains.<n>We propose a hybrid reinforcement learning framework integrating Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for enhanced spoilage prediction.
  arXiv  Detail & Related papers  (2025-12-22T12:59:48Z)
• Multimodal AI Systems for Enhanced Laying Hen Welfare Assessment and Productivity Optimization [0.0]
  Future of poultry production depends on replacing subjective, labor-intensive welfare checks with data-driven, intelligent monitoring ecosystems.<n>Traditional welfare assessments-limited by human observation and single-sensor data-cannot fully capture the complex, multidimensional nature of laying hen welfare in modern farms.<n>This work lays the foundation for a transition from reactive, unimodal monitoring to proactive, precision-driven welfare systems that unite productivity with ethical, science based animal care.
  arXiv  Detail & Related papers  (2025-08-11T05:17:16Z)
• Perovskite-R1: A Domain-Specialized LLM for Intelligent Discovery of Precursor Additives and Experimental Design [5.378023608941598]
  Perovskite solar cells (PSCs) have rapidly emerged as a leading contender in next-generation photovoltaic technologies.<n> challenges such as long-term stability, environmental sustainability, and scalable manufacturing continue to hinder their commercialization.<n>Precursor additive engineering has shown promise in addressing these issues by enhancing both the performance and durability of PSCs.<n>We introduce Perovskite-R1, a specialized large language model (LLM) with advanced reasoning capabilities tailored for the discovery and design of PSC precursor additives.
  arXiv  Detail & Related papers  (2025-07-22T07:48:32Z)
• Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output [51.88841610098437]
  We propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights.<n>We evaluate the system through three user tests: (1) no mention of sustainability to the user prior to the prompting process with generative AI, (2) sustainability goals communicated to the user before prompting, and (3) sustainability goals communicated along with quantitative CO2e data included in the generative AI outputs.
  arXiv  Detail & Related papers  (2025-06-17T22:33:11Z)
• Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction [69.38041171537573]
  Water quality is foundational to environmental sustainability, ecosystem resilience, and public health.<n>Deep learning offers transformative potential for large-scale water quality prediction and scientific insights generation.<n>Their widespread adoption in high-stakes operational decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges.
  arXiv  Detail & Related papers  (2025-03-13T01:50:50Z)
• Cooperative Resilience in Artificial Intelligence Multiagent Systems [2.0608564715600273]
  This paper proposes a clear definition of cooperative resilience' and a methodology for its quantitative measurement. The results highlight the crucial role of resilience metrics in analyzing how the collective system prepares for, resists, recovers from, sustains well-being, and transforms in the face of disruptions.
  arXiv  Detail & Related papers  (2024-09-20T03:28:48Z)
• AIMS-EREA -- A framework for AI-accelerated Innovation of Materials for Sustainability -- for Environmental Remediation and Energy Applications [0.0]
  AIMS-EREA is our novel framework to blend best of breed of Material Science theory with power of Generative AI. This also helps to eliminate the possibility of production of hazardous residues and bye-products of the reactions.
  arXiv  Detail & Related papers  (2023-11-18T12:35:45Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)

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.