Machine learning (ML) in materials science refers to computational methods that learn statistical, structural, or physics-informed relationships from experimental, computational, and literature-derived materials data. These methods are used to predict materials properties, identify structure–property and process–structure–property–performance relationships, discover candidate materials, optimize synthesis and processing routes, and guide functional applications. ML is narrower than artificial intelligence (AI), which also includes broader reasoning, planning, search, and automation capabilities. It is also distinct from materials informatics, which is the wider data-centered framework that includes databases, descriptors, metadata, workflows, visualization, and knowledge management. ML can complement high-throughput computation by building surrogate models from density functional theory, finite-element simulation, molecular dynamics, or experimental data, but it is not identical to high-throughput screening itself. Unlike conventional physics-based modeling, which begins with explicit governing equations or mechanistic assumptions, ML usually infers predictive relationships from data; modern approaches increasingly combine both perspectives through physics-informed features, uncertainty quantification, and human expertise.
The intrinsic challenges of accessing historical dark data in materials science are described and contrasted with timely opportunities for leveraging massive amounts of experimental data from laboratories in going forwards; by exploiting electronic-lab notebooks, high-throughput experiments, and digital-twin technologies.
Jacqueline M. Cole· Advances in Materials· 0 citations
This work will present the current work on inverse material design, where AI methods—particularly generative pretrained transformers—are used to predict new material candidates based on desired properties, pushing the boundaries of materials innovation.
I. Gonzales, R. Ullberg, Andrew H Salij et al.· ECS Meeting Abstracts· 0 citations
This Perspective systematically discusses Physics-Grounded Materials AI (PhysMat AI) as a unifying perspective for integrating physical knowledge into materials intelligence through five complementary roles: physics as prior knowledge, descriptors, constraints, verifiers, and infrastructure.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang et al.· Advanced Functional Material...· 0 citations
It is argued that developing thermodynamics-informed ML constitutes one of the most important and least explored frontiers in materials discovery and that the next generation of ML models must move beyond static energy predictions towards a thermodynamic description of materials behaviour under realistic operating conditions.
Pol Benítez, Cibr'an L'opez, Claudio Cazorla· 0 citations
Machine learning force fields (MLFFs) combine the high accuracy of first-principles methods with the high efficiency of classical force fields, offering new opportunities for atomic-level studies of inorganic crystalline materials. We systematically summarize the research progress on MLFFs, elucidate their fundamental principles and developmental history, and categorically introduce the technical characteristics of representative models and relevant benchmarking platforms. We aim to review the advantages of MLFFs in overcoming traditional computational limitations across four domains: structural prediction and optimization, physical properties, defect and interface properties, and phase transitions and kinetic processes. The challenges of MLFFs are also examined in computational efficiency and simulation scale, accuracy and generalization ability, data requirements and training samples, model interpretability, and physical constraints, which offer a reference for the research and application of MLFFs in the field of inorganic crystalline materials.
Jing Yi, Yuxin Zhan, Yuanmao Hu et al.· Physical Chemistry, Chemical...· 0 citations
Artificial intelligence (AI) and machine learning (ML) are increasingly used as general-purpose research instruments across the physical sciences, accelerating tasks that were traditionally limited by trial-and-error experimentation, computational cost, or the sheer dimensionality of the underlying physics. Three areas illustrate this shift with particular clarity: computational materials discovery, nanostructured electrode design for energy storage, and space weather / heliophysics forecasting. Despite substantial progress in each area individually, limited work has examined them together, quantitatively, as expressions of a single underlying trend — the convergence of AI methodology with core physical and chemical science. Methods: This study used a narrative and scoping review methodology, incorporating quantitative benchmarks drawn directly from primary and independent critical sources. Peer-reviewed literature, preprints, direct observational monitoring data, and ResearchGate-hosted scholarly works published primarily between 2023 and 2026 were identified through structured searches combining terms from materials informatics, nanostructured energy-storage materials, and AI-based space weather forecasting. Sources were screened for topical relevance and synthesized thematically, with reported quantitative claims cross-checked against independent critical appraisal where available. Results: The synthesis identifies convergent innovation across three domains, with all three now quantitatively documented: (1) AI-driven inverse design (exemplified by a 2.2-million-structure materials search yielding roughly 380,000 candidate stable materials) is accelerating materials discovery, though independent re-analysis found only a small fraction of these structures met joint criteria of novelty, credibility, and utility; (2) nanostructured graphene–metal oxide composite electrodes span a wide reported performance envelope (specific capacitances from roughly 100 F/g to over 1000 F/g; energy densities up to roughly 100+ Wh/kg), with statistically designed synthesis optimization improving reproducibility; and (3) AI-assisted forecasting achieved approximately one-minute precision in reconstructing a major 2024 geomagnetic superstorm, in contrast to a roughly 40% amplitude error and nine-month timing error in the leading pre-cycle statistical/physical forecast of Solar Cycle 25's overall intensity. Discussion: A common methodological pattern recurs across all three domains: AI is used not to replace physical theory but to navigate high-dimensional parameter spaces that are analytically or computationally intractable by classical means alone, and the strongest, most defensible results are those subjected to independent, domain-expert critical appraisal rather than accepted at face value. Conclusion: Continued progress will depend on higher-quality shared datasets, physics-informed model architectures, standardized benchmarking protocols, and routine independent critical appraisal as a formal part of the AI-for-science publication cycle.
Keywords: artificial intelligence; materials discovery; inverse design; nanostructured electrodes; graphene–metal oxide composites; supercapacitors; space weather forecasting; geomagnetic storms; interdisciplinary science
R. Mishra, Divyansh Mishra, R. Agarwal· International journal of phy...· 0 citations