Machine Learning Assisted Design of Complex and High Entropy Alloys by Hybrid HiPIMS/Pulsed-DC PVD Process for Low Carbon Energy Applications in Extreme Environments
Complex and high entropy alloys are attracting much attention currently thanks to their mechanical and corrosion resistance properties in harsh environments, in particular needed for carbon-free energy applications. However, their elaboration in bulk and in thin film form in a trial-and-error approach is impractical due to their complexity and the cocktail effect. The recent development of artificial intelligence brings a new possibility for their elaboration and adjustment of their properties. Firstly, we present an overview of Materials and data science research. Then we describe how DIADEM - French initiative for Materials and Data science convergence - tackles the development of innovative coatings for carbon-free energy applications (nuclear, high temperature electrolysis, ...) thanks to the development of a nationwide network of synthesis and characterization platforms - the DIADEM discovery hub. We describe in particular DIADEM-2D, an AI-driven Hybrid HiPIMS/Pulsed-DC PVD process using 4 cathodes in confocal combinatorial configuration. We present the high entropy alloy determination using data from the literature for corrosion resistance in molten salt media and nuclear accidental conditions. An element-independent model gathering deposition parameters and coating properties has been implemented allowing the design of protective coatings with a particular composition. We demonstrate the feasibility of this process and its accuracy.
High-entropy alloys (HEAs) have emerged as a powerful materials platform for electrocatalysis due to their tunable surface energetics, structural stability, and diverse local atomic environments arising from multielement interactions. Composed of several principal elements in near-equimolar ratios, HEAs leverage high configurational entropy to stabilize single-phase solid solutions, while surface heterogeneity creates new catalytic motifs in which collective electronic and geometric effects yield activities exceeding those of pure metals. These attributes make HEAs particularly promising for the hydrogen evolution reaction (HER).
Platinum-group-metal-containing HEAs (PGM-HEAs) exhibit exceptional HER activity and durability in acidic environments; however, their reliance on multiple precious metals limits large-scale deployment. Recent efforts demonstrate that partial substitution with earth-abundant transition metals can significantly reduce noble-metal content without sacrificing performance. Despite this progress, the atomic-scale origins of HER activity in HEAs—specifically the geometric and electronic descriptors governing optimal hydrogen binding—remain insufficiently understood.
Here, we present an integrated density functional theory (DFT) and machine learning (ML) framework for discovering PGM-lean HEAs optimized for HER. Equimolar binary and ternary alloys are constructed from a nine-metal design space (Pt, Pd, Ir, Rh, Fe, Co, Ni, Mo, W), yielding 120 unique compositions. Representative surface configurations are selected using Kennard–Stone sampling and modeled as close-packed FCC(111) and BCC(110) slabs. Hydrogen adsorption energetics, and local active-site descriptors are computed using DFT and analyzed using ML models to establish structure–property relationships.
Benchmark calculations on elemental FCC(111) surfaces reproduce expected periodic trends, with hydrogen adsorption free energies ranging from −0.46 eV on Ni(111) to −0.24 eV on Pt(111) at 1/4 monolayer coverage, consistent with d-band theory. Coverage effects are quantified by comparing 2×2×7 and 3×3×7 slab models, revealing stabilization of H* by approximately 0.02 eV at lower coverage. These results validate the computational methodology and establish a robust foundation for screening multicomponent HEA surfaces. The combined DFT–ML framework enables the rational identification of cost-effective, high-performance HEA electrocatalysts while providing fundamental insight into active-site chemistry in complex alloy systems. All computational data will be made available upon request to promote transparency and reproducibility.
Matthew R. Curry, Abdennaceur Karoui, Bijandra Kumar· ECS Meeting Abstracts· 0 citations
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.
Venkatesh Iyer, Nandhini Ravi· International Journal of Mod...· 0 citations
The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as transformative tools for navigating this complexity, enabling rapid prediction of electrochemical properties, de novo design of polymer electrolytes, and precise optimization of nanostructures for supercapacitors and batteries. This review systematically examines the application of ML techniques, including graph neural networks, Bayesian optimization, variational autoencoders, and transformer-based language models, for the discovery of energy storage polymer composites. The discussion critically evaluates ML-driven advancements across lithium-ion batteries, flexible energy storage devices, and solar energy materials, drawing on quantitative performance benchmarks reported in the primary literature. Emerging strategies such as active learning, multi-fidelity data fusion, physics-informed neural networks, and polymer-specific foundation models are discussed alongside persistent challenges related to data scarcity, model interpretability, and the translation gap between computational prediction and experimental synthesis. The review further addresses the landscape of open polymer property databases, the role of autonomous closed-loop experimentation in accelerating materials discovery, and the importance of reproducible, well-documented machine learning pipelines for the field to mature beyond proof-of-concept demonstrations. By consolidating evidence from verified primary sources and presenting original comparative analyses across methods and application domains, this review provides researchers, materials scientists, and computational chemists with an actionable, evidence-based perspective on the current state and future trajectory of AI-accelerated, sustainable energy storage polymer composite discovery.
Manas Kumar Yogi, D. Uma, Yamuna Mundru et al.· Journal of Polymer & Composi...· 0 citations
Developing efficient nickel–iron-based Oxygen Evolution Reaction (OER) catalysts via plasma-assisted electrodeposition holds great promise for the green hydrogen economy due to its compatibility with large-scale industrial manufacturing. However, the vast catalyst design space remains largely unexplored due to the inefficiency of traditional trial-and-error investigation. Herein, we develop a Machine Learning-guided Genetic Algorithm (ML-GA) paradigm with two complementary modes: exploration and exploitation. The exploration mode prioritizes population diversity while maintaining promising predicted performance to broadly sample the chemical space, while the exploitation mode focuses on high-performance regions to identify promising catalysts. Guided by this framework, NiFe/NiS catalysts were prioritized and synthesized via plasma-assisted electrodeposition, which demonstrated remarkable OER activity with low overpotentials of 219 mV and 315 mV at current densities of 10 mA cm
−2
and 1000 mA cm
−2
, respectively. This work demonstrates the effectiveness of the ML-GA approach in overcoming data limitations and accelerating the design of high-performance OER catalysts.
Yina Guo, Yansong Zhou, Zhuming Mao et al.· Plasma Science and Technolog...· 0 citations
This work demonstrates how machine learning models can accelerate the high-throughput screening of metal oxides’ oxygen defect thermodynamics to identify promising novel TCH candidates and discusses how liquid metal-mediated thermochemical redox can serve as a promising alternative approach due its drastically reduced operating temperatures and promising technoeconomic outlook.
Matthew D. Witman, A. Ambrosini, Sean R. Bishop et al.· ECS Meeting Abstracts· 0 citations
The production of green hydrogen through water splitting requires highly efficient electrocatalysts, but the current trial-and-error-based synthesis or discovery is time-consuming, costly and resource-intensive. Machine learning (ML) provides a powerful, data-driven alternative that can model complex structure-activity relationships across large chemical spaces at orders-of-magnitude speed. This review systematically overviews the life cycle of the electrocatalyst research and development application of ML. First, the thermodynamic and kinetic principles of the hydrogen and oxygen evolution reactions are summarised, along with some well-adopted and accepted activity descriptors. Then we explore data sources, featurization approaches, and algorithms, and discuss the model space, from a simple interpretable model to a graph neural network to a generative model, in the context of the ML toolkit. Strategic applications are discussed for high-throughput virtual screening of alloys and single-atom catalysts, as well as multifunctional activity prediction for overall water splitting, and stability optimisation under operating conditions. The topic of emerging frontiers is highlighted, including high-entropy alloys, amorphous materials, and linking atomic-scale understanding to device-level performance through integration with density functional theory. Finally, the problems of data scarcity, model interpretability and the discrepancy between computational predictions and industrial implementation are discussed, along with future directions for closed-loop discovery and self-driving laboratories. Incorporating ML into electrocatalyst design and combining it with autonomous experimentation will revolutionise this process, from simulation to energy solution, dramatically speeding it up.
Vamsi Krishna Kudapa, Shoaib Mohd, Vijayakumar Sivasundar et al.· Frontiers in Chemistry· 0 citations