Artificial Intelligence as a Convergence Catalyst in the Physical and Chemical Sciences: Advances in Materials Discovery, Nanostructured Energy Storage, and Space Weather Physics

The emergence of the catalysis artificial intelligence paradigm transforms modern chemical and catalytic research

Recent advancements in data infrastructure, computational statistics, and artificial intelligence (AI) have inaugurated a transformative era for chemical sciences. These computational paradigms facilitate the optimization of complex systems at an unprecedented rate, transcending the limitations of traditional trial-and-error methodologies. This innovative convergence of domain-specific scientific knowledge and advanced heuristics is pivotal for the engineering of next-generation, sustainable chemical processes characterized by minimized energy footprints and enhanced selectivity. Significantly, this evolution fosters a deep integration between heterogeneous and homogeneous catalysis and the diverse analytical tools emerging from the digital frontier, potentially catalyzing a new “Catalysis–AI” paradigm. However, comprehensive bibliometric analyses reveal a significant break; despite the proliferation of AI literature, its substantive implementation in experimental catalysis remains nascent. The current landscape is hindered by data silos and a lack of standardized descriptors. Consequently, the formulation of robust explanatory and predictive hypotheses—anchored in both physical chemistry and data-driven insights—is imperative. Establishing such a framework is essential for achieving a fundamental understanding of active sites and reaction mechanisms, ultimately addressing the urgent scientific and technological imperatives of modern industry. However, bibliometric analyses reveal that a robust connection between diverse catalytic fields and emerging AI tools remains unestablished. A gap particularly evident in Ibero-American countries despite their strong catalysis tradition. This disconnects risks creating a substantial disparity in advancement rates compared to other scientific domains. Ultimately, bridging this divide is essential to unlock a future human-machine synergy capable of accelerating the design of novel catalytic materials. The graphical abstract was generated using NotebookLM, based on the following prompt: Minimalist scientific style with a white background. Display a shower of loose puzzle pieces of different sizes orchestrated from largest to smallest that generate a waveform entering through the left side of a computer that displays basic programming code on its screen. Connected to the right side of this computer by a cable is an android. This android acts as a teacher, writing science-related information on a screen. This whiteboard should contain a reaction coordinate graph, a heterogeneous catalyst model, and chemical molecules. The graphical abstract was generated using NotebookLM, based on the following prompt: Minimalist scientific style with a white background. Display a shower of loose puzzle pieces of different sizes orchestrated from largest to smallest that generate a waveform entering through the left side of a computer that displays basic programming code on its screen. Connected to the right side of this computer by a cable is an android. This android acts as a teacher, writing science-related information on a screen. This whiteboard should contain a reaction coordinate graph, a heterogeneous catalyst model, and chemical molecules. Recent advancements in artificial intelligence have significantly impacted science and technology. Despite the concerns and potential risks associated with artificial intelligence, the responsible integration of these tools is essential for the advancement of science and technology, especially in the field of catalysis. It is necessary to integrate catalysis, with the diverse tools of artificial intelligence, ultimately giving rise to a new paradigm of catalysis–AI. The disconnect between catalysis and artificial intelligence is particularly pronounced in Ibero-American countries, which have served as fundamental pillars in the advancement of catalytic knowledge for decades.

Integrating Mathematics, Artificial Intelligence, and Chemistry for Digital Materials Discovery

Artificial Intelligence for Lithium-Ion Batteries: Closed-Loop Discovery, State Prediction, and Trustworthy Management

Artificial intelligence (AI) is transforming lithium-ion battery (LIB) research by linking heterogeneous data, predictive modeling, mechanistic interpretation, and experimental validation within a closed-loop paradigm. AI does not uniquely enable nonlinear representation; rather, it complements electrochemical, statistical, and control models by learning high-dimensional relationships among composition, structure, processing, interfacial chemistry, operating history, and performance. This review critically examines three connected domains: AI-assisted materials discovery, battery state prediction, and trustworthy intelligent management. To distinguish this contribution from recent topic-specific surveys, we organize methods along four complementary axes—model architecture, learning strategy, physics integration, and deployment strategy—and compare landmark studies using quantitative evidence such as independent cell count, validation design, reported error, experimental budget, and closed-loop acceleration. Materials applications include cathodes, anodes, liquid and solid electrolytes, and electrode-electrolyte interphases; operational applications include state of charge, state of health, remaining useful life, degradation diagnosis, safety warning, and digital twin-enabled management. The critical synthesis identifies data leakage, inconsistent metadata, domain shift, uncalibrated uncertainty, black-box reasoning, computational constraints, and limited external validation as recurring barriers. Future progress will depend less on model complexity alone than on FAIR data, cell-independent evaluation, physics-informed learning, uncertainty-aware decisions, autonomous experimentation, human oversight, and deployment-aware design. These elements define an evidence chain for moving LIB AI from proof-of-concept prediction toward reproducible, closed-loop, and trustworthy battery innovation.

NIST Artificial Intelligence for Materials Science (AIMS) 2026

This work pursues a multi-tier development strategy in which machine learning (ML) algorithms are combined with exact physical symmetries and constraints to significantly accelerate computations of electronic structure and atomistic dynamics.

AI-Assisted Discovery of Novel Alloys for Extreme Environmental Conditions

The discovery of advanced alloys capable of withstanding extreme environmental conditions such as high temperatures, intense radiation, corrosive atmospheres, and mechanical stress is critical for applications in aerospace, nuclear energy, deep-sea exploration, and space missions. Traditional experimental and computational approaches to alloy design are often time-consuming and resource-intensive. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful tools to accelerate the discovery process by enabling high-throughput screening, property prediction, and design optimization. This paper presents a comprehensive review and methodology for AI-assisted alloy discovery, focusing on the integration of data-driven models with physical principles, high-fidelity simulations, and experimental validation. We highlight successful case studies, discuss the challenges of data scarcity and model interpretability, and propose a framework for closed-loop design that incorporates generative models and active learning. This AI-driven approach represents a paradigm shift toward faster, more cost-effective discovery of next-generation materials for extreme environments.

Artificial Intelligence for Chemical Synthesis: Tools, Challenges, and Emerging Trends

Chemical research is no longer confined strictly to the lab bench or trial and error. Artificial intelligence is transforming the field, helping to predict molecular behavior, find potential designs, and automate certain aspects of the discovery process, introducing a new kind of intuition. Over the past decade, advances in machine learning, natural language processing, robotics, and automation have enabled new areas of research. These are broadening the applications for retrosynthetic analysis, reaction optimization, and computer-aided synthetic planning. This study examines the evolution of computer-aided synthesis, describing its development from rule-based approaches to advanced deep learning and hybrid systems that leverage large datasets. Thus, it focuses on AI platforms that integrate predictive algorithms with rapidly evolving robotic systems. Such technologies enable rapid hypothesis generation, reaction screening, and the improvement of synthetic methods. The review encompasses synthesis analysis tools, recommendation algorithms, and autonomous labs that deliver discoveries more quickly and minimize waste and environmental impact. It examines current challenges, such as data scarcity, sporadic reporting, model interpretability, and practical applications. More broadly, the need for sustainable, collaborative research has increased, and cross-border work through cloud-based laboratories and shared databases enables chemists worldwide to share resources. The review identifies beneficial trends and ongoing challenges, with a view to providing opportunities for AI to make chemistry greener, accelerate discovery, and improve decision-making across academic and industrial settings. AI is not replacing chemists but rather enhancing creativity and intuition, bringing together research that traditional methods would never have allowed, on a scale never before possible without AI.