What if the revolutionary materials essential for cleaner energy, accelerated electronics, quantum computing, superior batteries, or lighter aerospace components could be identified entirely through computational models before their physical creation in laboratories? This visionary approach, once abstract and futuristic, became the defining ambition of the National Centre of Competence in Research MARVEL when it was first conceived in 2011. Today, as MARVEL marks over a decade of pioneering contributions and transformative impact, it stands as a testament to the power of computational science to reshape the discovery and design of novel materials.
Officially launched in 2014, MARVEL aimed to fundamentally alter materials research by integrating quantum-mechanical simulations with the burgeoning power of high-performance computing and an emerging force in science: machine learning. This innovative integration sought not only to accelerate the pace of materials innovation but also to make it more predictive, systematic, reproducible, and inherently collaborative. The foresight behind this strategy has proved remarkably prescient, positioning computational materials science at the core of modern scientific inquiry and technological progress.
Nicola Marzari, MARVEL’s director and a professor at EPFL, reflects on the initiative’s continued relevance: major technology companies such as Google DeepMind, Microsoft, and Meta have recently ramped up efforts in materials design, paralleled by an influx of startups securing substantial investments exceeding hundreds of millions of dollars in early-stage funding. Moreover, the concept of agentic artificial intelligence, included in MARVEL’s initial vision, has now evolved into fully operational systems that are poised to revolutionize how materials are discovered and optimized.
Historically, materials science progressed through labor-intensive trial and error—synthesize, test, fail, and repeat. MARVEL disrupted this paradigm by weaving together expertise from physics, chemistry, computer science, and machine learning with experimental validation. This multidisciplinary approach enabled researchers to pre-select promising candidate materials computationally, dissect the mechanisms underpinning their behavior, and promote openness through sharing data and computational methods. As a result, MARVEL fostered a culture where discoveries could be systematically reproduced and extended across the global scientific community.
Across its twelve years, MARVEL contributed to key scientific advances in a variety of cutting-edge materials. Its researchers predicted and facilitated the experimental confirmation of novel quantum materials exhibiting unconventional electronic states highly promising for next-generation electronic devices and quantum information technologies. These breakthroughs provided not only isolated discoveries but also distilled complex phenomena into clear design principles, guiding the creation of materials with highly tailored properties for future technological applications.
At the software and methods level, MARVEL made enduring contributions by developing advanced electronic-structure techniques crucial for accurate quantum simulations. These methods were incorporated into open-source computational frameworks widely adopted by scientists worldwide, democratizing access to sophisticated simulation tools. In parallel, MARVEL was a trailblazer in embedding machine learning algorithms within materials modeling pipelines, creating AI systems capable of predicting properties from atomic scale structures with unprecedented accuracy.
The evolution of machine learning within MARVEL’s scope is particularly striking. Initially a promising adjunct, machine learning matured into a core computational instrument capable of accelerating atomistic simulations and forecasting complex materials properties, including spectroscopic signatures, chemical environments, electronic structures, diffusion dynamics, and more. This advancement laid the groundwork for today’s explosive interest in AI-powered materials research, elevating computational models from theoretical tools to practical engines of discovery.
Beyond fundamental science, MARVEL tackled materials challenges with tangible societal impact. The initiative’s portfolio spanned solar energy harvesting materials, catalysts for water splitting, solid-state battery electrolytes, and nanoporous materials for selective separations. It included molecular crystals relevant to pharmaceutical and chemical industries, ultrathin two-dimensional materials, and high-performance aerospace alloys. In its later phases, MARVEL expanded into spectroscopy, automated experimental platforms, and hybrid quantum-classical algorithms, continuously broadening the frontiers of computational materials research.
Integral to MARVEL’s philosophy was a commitment to open science and transparency. By prioritizing open-source software, verification protocols, and reproducibility standards, the initiative fostered greater reliability in computational results. It demonstrated that from simulations to experimental data, workflows could be integrated seamlessly into reproducible pipelines, sometimes even enabling computational steering of automated laboratory experiments. These pioneering efforts set new norms for how materials research should be conducted in the digital era.
A central pillar of MARVEL’s legacy is the establishment of a national Swiss digital ecosystem for materials science. This network, originally centered at EPFL and incorporating ETH Zurich, PSI, Empa, CSCS, and various universities, culminated in a robust platform supporting computational materials research nationwide. Critical components of this infrastructure include AiiDA and AiiDAlab, platforms facilitating reproducible workflows; the Materials Cloud, a portal for data dissemination and open access; and Lhumos, an innovative educational toolset designed to train new generations of computational materials scientists.
These digital infrastructures empower researchers to execute complex computational experiments with ease, meticulously track and record every analytical step, compare outputs from diverse simulation engines, publish datasets for reusability, and convert expert workflows into accessible analytical tools. This democratization and standardization have not only accelerated research but also fostered a global community united by shared resources and collaborative spirit.
MARVEL’s influence extends beyond academia, forging meaningful partnerships with a diverse industrial community spanning sectors such as energy, electronics, metallurgy, catalysis, and pharmaceuticals. The initiative cultivated collaborations with dozens of companies and shifted software development towards creating practical tools adaptable to industrial contexts. This transition from specialist-oriented software to industry-grade applications underscores MARVEL’s role in bridging scientific innovation with commercial utilization.
As the MARVEL program comes to a close, its twelve-year journey will be commemorated on 9 July at EPFL’s Rolex Forum in Lausanne with a full-day event gathering leading figures from academia and industry. Discussions will span quantum materials, design methodologies, machine learning advancements, and the expanding computational materials science community. The event will bring together luminaries from Europe, North America, China, and industry stakeholders such as Microsoft, BASF, and Stellantis, marking a celebration of both accomplishments and future prospects.
Ultimately, MARVEL has cemented a vision where materials of the future are increasingly imagined, optimized, shared, and validated digitally before ever undergoing synthesis in the laboratory. This digital-first paradigm has shifted how materials science is approached worldwide and has positioned Switzerland at the forefront of this scientific revolution. By championing open science, cutting-edge computational tools, and interdisciplinary collaboration, MARVEL has transformed materials research from an art of trial and error into a rigorous, data-driven endeavor poised to accelerate technological breakthroughs across a broad spectrum of fields.
Subject of Research: Computational design and discovery of novel materials.
Article Title: MARVEL at 12: Charting the Digital Revolution in Materials Science.
News Publication Date: Not specified in content.
Web References:
https://nccr-marvel.ch/events/2026-07-marvel-epfl
https://aiida.net/
https://www.aiidalab.net/
https://www.materialscloud.org/
https://www.lhumos.org/
Keywords
Materials science, computational materials discovery, quantum materials, electronic structure, machine learning, artificial intelligence, quantum chemistry, simulation reproducibility, high-performance computing, energy materials, automated experiments, open-source software, digital scientific ecosystem.
Tags: accelerated materials discovery with AIAI-driven aerospace materials developmentAI-powered computational materials sciencecollaborative materials research platformscomputational modeling of advanced batterieshigh-performance computing in materials researchmachine learning for materials innovationpredictive materials design using AIquantum mechanics in materials discoveryquantum simulations for cleaner energy materialsreproducible computational materials sciencetransformative impact of MARVEL computational science
