AI Labs Compress 10-Year Discovery Cycles Into Months

Explore how AI labs will reshape scientific experimentation and industrial discovery.

We stand at the threshold of a scientific revolution more profound than the microscope, more transformative than the computer, more consequential than the internet. The 2020s are emerging as the decade when artificial intelligence fundamentally redefines how humanity conducts scientific inquiry, discovers new materials, and solves civilization-scale challenges. This is not hyperbole—it is a data-driven observation of an inflection point already underway.

The statistics tell a compelling story. AI-related scientific publications have surged with an average annual growth rate accelerating from 10.5% before 2020 to 19.3% in subsequent years. The U.S. National Science Foundation is investing up to $100 million in a national network of “programmable cloud laboratories” to accelerate AI-enabled science. Google DeepMind’s autonomous A-Lab at Lawrence Berkeley National Laboratory has already discovered and produced more than 40 new materials with minimal human intervention. These are not distant futures—they are present realities pointing toward a dramatically different scientific landscape.

The Convergence Creating the AI Lab Revolution

Three technological streams are converging to create this transformative moment: artificial intelligence capable of hypothesis generation and experimental design, robotic automation that executes physical experiments with precision and speed, and comprehensive materials databases that provide the training data for increasingly accurate predictive models. Individually, each technology is powerful; together, they constitute a phase transition in scientific capability.

AI as Meta-Technology: Beyond Tool to Paradigm

Previous scientific instruments—telescopes, spectrometers, electron microscopes—extended human sensory capabilities. AI represents something fundamentally different: it extends human cognitive capabilities in hypothesis generation, pattern recognition across vast datasets, and exploration of combinatorial design spaces that exceed human comprehension. As Nature notes, “AI is no longer just a scientific tool but a meta-technology that redefines the very paradigm of discovery, unlocking new frontiers in human scientific exploration.”

This meta-technological character means AI does not simply make existing research methodologies faster or cheaper—it enables entirely new forms of scientific inquiry impossible through traditional approaches. Simreka’s Virtual Experiment Platform exemplifies this shift, allowing researchers to conduct reverse simulations where desired outcomes define the search for optimal inputs—inverting the traditional experimental logic.

From Digital Twins to Virtual-First Discovery

Early digital technologies created virtual representations of physical systems—digital twins that simulated real-world behavior. The current revolution inverts this relationship: increasingly, discovery happens first in virtual space, with physical synthesis reserved for validation of computationally identified candidates. This virtual-first paradigm compresses timelines, eliminates waste, and expands the explorable design space by orders of magnitude.

Biological compounds can now be precisely modeled down to the subatomic level, with quantum computers, machine learning algorithms, and AI collaborating to process extensive data and simulate complex biological systems. This enables researchers to accurately model molecules, proteins, and biochemical interactions—performing simulations, predicting outcomes, and designing experiments without requiring physical lab space. The result: enhanced flexibility and precision in experimental design, significant cost savings, faster research cycles, and reduced environmental footprint.

The Self-Driving Lab: Closing the Loop

The most dramatic manifestation of the AI lab revolution is the self-driving laboratory: fully autonomous systems that combine AI experimental design with robotic execution and real-time analysis. These labs operate in closed loops—AI proposes experiments, robots execute them, analytical instruments characterize results, and AI refines hypotheses based on outcomes—cycling through hundreds of iterations without human intervention.

According to research from the University of Chicago, self-driving labs could accelerate research 100 to 1,000 times, potentially compressing a 10-year operation into less than a few months and reducing costs from $100 million to under $1 million. Andrew I. Cooper’s team at the Materials Innovation Factory demonstrated this potential by using an AI-directed robotics lab to optimize a photocatalytic hydrogen generation process—running approximately 700 experiments in just 8 days.

The Architecture of the AI Lab: Components and Capabilities

Understanding how AI labs will reshape science requires examining their technical architecture. Modern AI lab platforms integrate multiple complementary capabilities into unified workflows.

The Role of Comprehensive Materials Databases

AI models are only as good as their training data. The emergence of comprehensive materials informatics platforms represents a critical enabler of the AI lab revolution. Simreka’s Databank – the World’s Largest Material Informatics Platform exemplifies this capability, consolidating material properties, formulation data, process parameters, and performance characteristics into AI-ready repositories.

These databases serve multiple functions: training data for machine learning models, validation datasets for accuracy assessment, historical context for understanding property-structure relationships, and discovery platforms for identifying patterns invisible to human analysis. As these databases grow and interconnect, network effects amplify their value—each additional data point improves model accuracy, enabling better predictions that guide more informative experiments, generating higher-quality data that further refines models.

Generative AI: The Cognitive Interface

Simreka’s MatIQ – the AI Co-Pilot for Material Innovation demonstrates how generative AI transforms researcher interaction with materials knowledge. Rather than requiring expertise in data science or programming, researchers engage through natural language: asking questions, requesting analyses, exploring hypotheses, and receiving synthesized insights drawn from millions of documents, experimental results, and scientific publications.

This cognitive interface democratizes access to expertise, enabling researchers across skill levels and geographic locations to leverage cutting-edge AI capabilities. MatQuest answers chemistry questions by mining patents and scientific literature; DocTalk extracts insights from technical documents; ImageXP interprets spectroscopy data and microscopy images; DataDive generates analytics from experimental datasets through conversational queries. Together, these capabilities compress what once required days of literature review and data analysis into minutes of interactive exploration.

Transforming Scientific Workflows: Before and After AI Labs

The practical impact of AI labs manifests in fundamentally transformed research workflows. Comparing traditional and AI-augmented approaches reveals the magnitude of change.

Traditional Materials Discovery Workflow

1. Literature Review (2-4 weeks): Researchers manually search publications, patents, and technical reports to understand prior art and identify promising directions
2. Hypothesis Generation (1-2 weeks): Based on literature and expert intuition, formulate hypotheses about material compositions or process parameters
3. Experimental Design (1 week): Plan synthesis procedures, analytical protocols, and testing regimens
4. Material Synthesis (2-6 weeks): Prepare candidate materials, often requiring custom procedures and troubleshooting
5. Characterization (2-4 weeks): Conduct analytical testing, interpret results, assess performance against targets
6. Analysis and Iteration (1-2 weeks): Evaluate outcomes, refine hypotheses, design follow-up experiments
7. Repeat Cycle: Typically 5-20 iterations required to identify viable candidates

Total Timeline: 12-36 months for materials development projects; 50-200+ physical experiments; success highly dependent on researcher experience and intuition.

AI Lab-Augmented Workflow

1. Automated Literature Mining (hours): MatIQ scans millions of documents, extracting relevant insights and identifying knowledge gaps
2. AI-Generated Hypotheses (hours to days): Machine learning models trained on comprehensive databases propose candidate materials and predict properties
3. Virtual Experimentation (days): Virtual Experiment Platform screens thousands of formulations computationally, identifying top candidates
4. Reverse Simulation (days): Specify desired properties; AI identifies optimal compositions and process parameters to achieve targets
5. Targeted Physical Validation (1-2 weeks): Synthesize and test only the most promising AI-identified candidates
6. Automated Analysis and Model Refinement (continuous): Physical results feed back to refine AI models, improving accuracy for next iteration
7. Rapid Iteration: Typically 2-5 physical validation cycles sufficient due to high accuracy of AI pre-screening

Total Timeline: 3-6 months for materials development projects; 10-30 physical experiments; 1,000-10,000+ virtual experiments; success rate dramatically higher due to computational pre-screening.

Sectoral Transformation: How AI Labs Will Reshape Industries

The impact of AI labs extends far beyond academic research—entire industrial sectors will be restructured around these new capabilities.

Sustainable Materials and Green Chemistry

Climate imperatives demand rapid development of sustainable alternatives to carbon-intensive materials. AI labs accelerate this transition by enabling rapid exploration of biodegradable polymers, recyclable composites, bio-based chemicals, and low-carbon manufacturing processes. Simreka’s AI-Powered Formulation Generator allows researchers to specify sustainability constraints—renewable feedstocks, recyclability, biodegradability—as design parameters, with AI proposing formulations that satisfy both performance and environmental criteria.

The alternative—slow, incremental development through traditional R&D—cannot meet the timeline required by climate targets. AI labs provide the only realistic pathway to developing and deploying sustainable materials at the scale and speed the planet requires.

Advanced Energy Storage

Electric vehicle adoption and renewable energy integration depend critically on next-generation battery technologies. AI labs are transforming battery materials development: exploring novel cathode and anode materials, optimizing electrolyte formulations, designing solid-state battery architectures, and improving manufacturing processes. The combinatorial complexity of battery chemistry—involving multiple components with intricate interactions—makes this domain ideally suited to AI-driven exploration.

Autonomous labs can simultaneously optimize energy density, cycle life, charging speed, safety, cost, and sustainability—multi-objective optimizations that overwhelm traditional experimental approaches. The result: acceleration of innovations that enable longer-range EVs, grid-scale energy storage, and consumer electronics with dramatically improved battery life.

Pharmaceutical Discovery and Personalized Medicine

Drug discovery faces notorious timelines and costs: 10-15 years and billions of dollars to bring new therapeutics to market. AI labs are compressing these timelines by predicting drug-target interactions, optimizing molecular structures for efficacy and safety, identifying repurposing opportunities for existing drugs, and designing personalized treatments based on patient genetics.

Virtual experimentation enables screening of millions of candidate molecules, identifying promising leads before expensive clinical trials. While regulatory frameworks appropriately maintain rigorous validation requirements, the efficiency gains in pre-clinical development translate directly to faster availability of life-saving treatments.

Semiconductor and Electronics Materials

The semiconductor industry faces simultaneous challenges: continued miniaturization approaching physical limits, growing demand for specialized chips, supply chain vulnerabilities, and sustainability pressures. The NSF’s $100 million investment in AI-programmable cloud laboratories specifically targets semiconductor materials, recognizing that traditional R&D approaches cannot address these challenges at sufficient speed.

AI labs enable exploration of novel semiconductor materials beyond silicon, optimization of manufacturing processes for yield and energy efficiency, development of specialized materials for quantum computing and neuromorphic chips, and identification of supply chain alternatives for critical materials. These capabilities will prove essential as the electronics industry navigates its most significant technological transitions in decades.

The Global AI Lab Ecosystem: Democratization and Decentralization

A remarkable characteristic of the emerging AI lab landscape is its democratizing potential. Unlike previous waves of scientific infrastructure—particle accelerators, space telescopes, genome sequencers—that concentrated capability in elite institutions with massive budgets, AI labs can be deployed at scales from individual researchers to global enterprises.

Cloud-Based Programmable Laboratories

The NSF’s vision of programmable cloud laboratories represents a fundamental shift in access to advanced research capabilities. Researchers anywhere can connect via internet to sophisticated AI-driven experimental platforms, submitting virtual experiments, requesting physical validations, and accessing analytical results without requiring local laboratory infrastructure.

This model mirrors cloud computing’s transformation of software development: converting capital expenditures into operational expenses, eliminating barriers to entry, enabling rapid scaling, and fostering innovation through broad access. A graduate student in a developing nation and a scientist at a Fortune 500 company can leverage the same AI lab capabilities—competing and collaborating on equal technological footing.

Open Science and Collaborative Discovery

AI labs generate unprecedented quantities of structured experimental data. When shared openly, this data creates compounding benefits: improved AI model accuracy through larger training sets, validation of predictions across independent laboratories, acceleration of scientific progress through reduced duplication, and democratization of knowledge that enables global participation.

Initiatives like the Materials Project, which makes data for all known inorganic compounds publicly available, exemplify this open science ethos. Simreka‘s platform architecture supports both proprietary enterprise deployments and collaborative research networks, recognizing that different contexts require different data governance models.

Global Self-Driving Lab Networks

Self-driving labs are emerging globally: the Hitosugi-Shimizu lab in Japan, Cronin and Cooper labs in the United Kingdom, Swiss CAT+ in Switzerland, Ada in Canada, and multiple facilities across the United States including the A-Lab, and labs led by Abolhasani, Ahmadi, Buonassisi, Fenning, Amassian, Brown, and Coley. These represent not isolated facilities but nodes in an emerging global network.

According to research published in Chemical Reviews, an international team recently demonstrated how they combined AI-guided experiments happening in five labs located around the world to find 21 new candidate materials for organic solid-state lasers. This distributed discovery model—where AI coordinates experiments across geographically dispersed facilities—presages a future where scientific discovery operates as a globally distributed, continuously operating network.

Challenges and Limitations: What AI Labs Cannot (Yet) Do

Enthusiasm for AI labs must be tempered by clear-eyed assessment of current limitations and unresolved challenges.

The Validation Imperative

AI predictions require physical validation—particularly for applications involving safety, regulatory approval, or novel phenomena where training data is sparse. While virtual experimentation dramatically reduces the number of physical experiments required, it does not eliminate the need for rigorous experimental confirmation. Establishing appropriate validation protocols that balance efficiency with rigor remains an evolving challenge.

Data Quality and Availability

AI models trained on poor-quality or biased data produce unreliable predictions. Historical experimental data often lacks standardization, contains measurement errors, and reflects publication bias favoring positive results. Building high-quality materials databases requires sustained investment in data curation, standardization protocols, and quality assurance—unglamorous but essential work that underpins AI lab effectiveness.

Interpretability and Scientific Understanding

Black-box AI models can identify effective materials without explaining why they work. This tension between predictive power and mechanistic understanding raises epistemological questions: Is prediction without explanation sufficient? Does AI-enabled discovery risk creating “orphan innovations” whose behavior we cannot truly understand? The scientific community is grappling with how to maintain the explanatory depth that characterizes scientific knowledge while leveraging AI’s predictive capabilities.

Infrastructure and Investment Requirements

While cloud-based platforms lower barriers to entry, building sophisticated AI lab capabilities still requires substantial investment in data infrastructure, computational resources, robotic systems, and skilled personnel. Organizations must navigate capital allocation decisions, build hybrid teams combining domain expertise with data science capabilities, and establish new workflows and quality systems.

Regulatory and Ethical Considerations

Autonomous discovery systems raise questions about accountability, intellectual property, safety validation, and the role of human judgment in scientific decision-making. Regulatory frameworks designed for traditional R&D methodologies require updating to accommodate AI-driven approaches while maintaining appropriate safeguards.

The Next Decade: Predictions and Implications

Projecting the trajectory of AI labs over the next decade requires extrapolating from current capabilities while acknowledging inevitable surprises. Nevertheless, several trends appear robust.

Prediction 1: Autonomous Discovery Becomes Standard Practice (2025-2028)

By 2028, autonomous experimental design using AI will be the default approach for materials development in leading organizations, with human researchers focusing on problem formulation, validation, and interpretation rather than designing individual experiments. Self-driving labs will operate continuously, exploring vast design spaces and identifying candidates for human evaluation. The researcher’s role evolves from experimentalist to director—setting objectives, interpreting results, and making strategic decisions informed by AI-generated insights.

Prediction 2: Virtual-First Discovery Inverts R&D Economics (2026-2030)

The cost structure of R&D will fundamentally shift as virtual experimentation becomes primary and physical synthesis becomes validation. Organizations will invest heavily in computational infrastructure and AI capabilities while downsizing traditional laboratory footprints. This economic inversion will favor organizations that build robust AI lab capabilities early, creating competitive moats difficult for late adopters to overcome.

Prediction 3: Global Discovery Networks Enable Distributed Science (2027-2032)

Interconnected networks of self-driving labs will enable distributed discovery where AI coordinates experiments across facilities worldwide, optimizing resource utilization and accelerating progress on global challenges. International collaborations will operate seamlessly, with virtual experiment platforms serving as collaboration infrastructure. This distributed model will prove particularly valuable for addressing civilization-scale challenges—climate change, pandemics, resource scarcity—requiring coordinated global scientific effort.

Prediction 4: AI-Designed Materials Transform Industries (2028-2035)

Materials discovered and optimized by AI will begin displacing conventionally developed alternatives across major industries. Construction materials with dramatically lower carbon footprints, electronic materials enabling new device capabilities, medical materials for regenerative medicine, and aerospace materials with unprecedented performance characteristics will demonstrate AI lab capabilities in commercial applications. By 2035, the majority of newly commercialized materials in advanced economies will have involved AI-driven discovery.

Prediction 5: Scientific Publishing Adapts to AI-Generated Research (2026-2030)

The explosion of AI-generated experimental data will overwhelm traditional peer review and publishing models. New paradigms will emerge: automated validation of computational predictions, structured data repositories with DOIs, AI-readable publication formats enabling machine learning on scientific literature, and evolving standards for attributing credit in human-AI collaborative discovery. The definition of scientific contribution will broaden to encompass dataset curation, algorithm development, and problem formulation alongside traditional experimental work.

Prediction 6: Workforce Transformation Requires New Skills (2025-2030)

The scientific workforce will require new skill combinations: domain expertise in chemistry, materials science, or biology paired with proficiency in AI tools, data analysis, and computational methods. Educational institutions will restructure curricula to emphasize these hybrid capabilities. Organizations will face skills gaps as traditional experimentalists require retraining while data scientists need domain knowledge. The transition period—roughly 2025-2030—will create competitive advantages for organizations that invest proactively in workforce development.

Strategic Imperatives for Organizations

The AI lab revolution presents both opportunity and risk. Organizations must act strategically to position themselves advantageously.

Build Data Infrastructure Now

Historical experimental data represents competitive advantage in an AI-driven world. Organizations should immediately begin consolidating, standardizing, and curating their experimental archives—even if current AI capabilities seem insufficient. Data assets compound in value; delays in data infrastructure development create compounding disadvantages. Platforms like Simreka’s Databank provide the architecture for capturing and leveraging institutional knowledge.

Pursue Strategic Pilots with Clear Metrics

Rather than waiting for perfect AI lab capabilities, launch targeted pilots in high-value applications. Focus on use cases where virtual experimentation delivers measurable impact: formulation optimization, property prediction, process design. Establish explicit success criteria and learn through iteration. Early experience builds organizational capabilities that prove invaluable as AI lab technologies mature.

Invest in Hybrid Teams and Skills Development

The transition to AI-augmented R&D requires people, not just technology. Build teams combining domain expertise with data science capabilities. Provide training in AI tools for existing researchers while ensuring data scientists understand domain context. Create career paths that reward hybrid skills. Organizations that view AI labs purely as technology investments while neglecting workforce development will struggle with adoption.

Participate in Open Science Ecosystems

While protecting proprietary competitive advantages, engage with open science initiatives, collaborative research networks, and pre-competitive consortia. The network effects of AI labs—where model accuracy improves with data volume—create incentives for strategic information sharing. Organizations that isolate themselves risk falling behind ecosystems that leverage collective intelligence.

Prepare for Regulatory Evolution

Engage proactively with regulatory bodies to help shape frameworks for AI-driven discovery. Standards for validation, quality assurance, and safety assessment must evolve to accommodate autonomous research methods while maintaining rigor. Organizations involved in developing these standards position themselves advantageously relative to those that adopt reactively.

Conclusion

The decade ahead will witness the most profound transformation in scientific methodology since the Scientific Revolution. AI labs combining virtual experimentation, autonomous discovery, and global collaboration networks are not incremental improvements but paradigm shifts—changing what questions we can ask, how rapidly we can answer them, and who can participate in the process.

The implications extend far beyond R&D efficiency. Accelerated materials discovery enables responses to civilization-scale challenges—climate change, resource scarcity, pandemic preparedness—at timelines that matter. Democratized access to advanced research capabilities shifts competitive dynamics and reduces geographic concentration of innovation. The relationship between human creativity and machine capability evolves as AI moves from tool to collaborator to autonomous explorer.

This future is not predetermined. The trajectory of AI lab development will be shaped by strategic choices organizations and policymakers make today: investments in data infrastructure and computational capabilities, decisions about open versus proprietary approaches, commitments to workforce development, and governance frameworks balancing innovation with safety. Organizations that recognize this inflection point and act decisively will lead their sectors; those that wait for certainty will find themselves competing from positions of structural disadvantage.

Simreka’s Virtual Experiment Platform, MatIQ – the AI Co-Pilot for Material Innovation, AI-Powered Formulation Generator, and Databank – the World’s Largest Material Informatics Platform represent not just tools but infrastructure for this emerging paradigm—platforms that enable organizations to participate in the AI lab revolution regardless of size or location.

The decade of AI labs has begun. The only question is whether your organization will lead it, follow it, or be disrupted by it.

Frequently Asked Questions

Q1. What distinguishes AI labs from traditional computational chemistry or materials modeling?

Traditional computational methods require researchers to formulate specific hypotheses and design individual simulations. AI labs autonomously generate hypotheses, design experiments, interpret results, and refine approaches through iterative learning—operating in closed loops with minimal human direction. Additionally, AI labs integrate multiple capabilities—virtual experimentation, literature mining, formulation generation, data analysis—into unified platforms like Simreka’s Virtual Experiment Platform rather than requiring researchers to master separate specialized tools.

Q2. How soon will self-driving labs be accessible to typical research organizations?

Cloud-based virtual experimentation platforms like Simreka’s Virtual Experiment Platform are accessible today, providing immediate benefits without requiring robotic infrastructure. Physical self-driving labs with full autonomy currently exist primarily at leading research institutions, but modular components are becoming increasingly affordable through 3D printing and open-source designs. Most organizations should expect to access virtual AI lab capabilities immediately (2025), hybrid virtual-physical workflows within 2-3 years (2026-2028), and full autonomous discovery systems within 5-7 years (2028-2032).

Q3. Will AI labs eliminate the need for human researchers?

No—AI labs augment rather than replace human researchers, but they fundamentally change the nature of research work. Humans remain essential for problem formulation, strategic direction, validation and interpretation, ethical judgment, and creative leaps. The researcher’s role evolves from conducting individual experiments to orchestrating AI-driven discovery systems with the help of co-pilots like MatIQ, interpreting their outputs, and making strategic decisions based on AI-generated insights.

Q4. What are the biggest barriers to adopting AI lab capabilities?

The primary barriers are organizational rather than technical: data infrastructure (historical experimental data scattered across incompatible systems), skills gaps (researchers lacking AI proficiency; data scientists lacking domain knowledge), cultural resistance (skepticism about AI predictions; attachment to traditional methodologies), unclear ROI, and capital allocation. Technical limitations are steadily improving and generally less constraining than organizational factors. Consolidating institutional data on Simreka’s Databank is often the highest-leverage first move.

Q5. How do AI labs address sustainability and environmental concerns?

AI labs advance sustainability through multiple mechanisms: reducing material waste by screening formulations virtually before physical synthesis (70-90% reduction typical), decreasing energy consumption by replacing laboratory equipment with computation, accelerating development of green alternatives to carbon-intensive materials, and enabling circular economy innovations. Tools like Simreka’s AI-Powered Formulation Generator let teams encode sustainability constraints—renewable feedstocks, recyclability, biodegradability—directly as design parameters.

Q6. What role will AI labs play in addressing global challenges like climate change?

Climate imperatives demand materials innovations at unprecedented speed and scale: next-generation solar cells, advanced batteries, carbon capture materials, sustainable aviation fuels, low-carbon cement and steel, biodegradable plastics, and countless others. Traditional R&D timelines—often 10-20 years from discovery to commercialization—are incompatible with climate targets. AI labs provide the only realistic pathway to accelerating discovery at the required pace; teams can explore that pathway hands-on through a Simreka demo. The NSF’s $100 million investment in AI-programmable cloud laboratories reflects this recognition.

Bibliographical Sources

1. National Science Foundation (2024). “NSF to invest in new national network of AI-programmable cloud laboratories.” Available at: https://www.nsf.gov/news/nsf-invest-new-national-network-ai-programmable-cloud
2. Axios (2024). “Self-driving labs are helping scientists find new materials faster.” Available at: https://www.axios.com/2024/08/09/ai-self-driving-science-labs-research
3. University of Chicago (2024). “Self-driving lab accelerates discovery process for materials with multiple applications.” Available at: https://pme.uchicago.edu/news/self-driving-lab-accelerates-discovery-process-materials-multiple-applications
4. Nature (2025). “AI for Science 2025.” Available at: https://www.nature.com/articles/d42473-025-00161-3
5. NCBI / Chemical Reviews (2024). “Self-Driving Laboratories for Chemistry and Materials Science.” Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC11363023/
6. Gensler (2024). “How AI and Emerging Technologies Will Transform the Future of Labs.” Available at: https://www.gensler.com/blog/ai-emerging-technologies-future-of-labs
7. MIT Technology Review (2023). “Eric Schmidt: This is how AI will transform how science gets done.” Available at: https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science/
8. Enthought (2024). “6 Predictions: How AI Will Transform Scientific R&D In The Next Decade.” Available at: https://www.enthought.com/blog/predictions-how-ai-will-transform-scientific-rd-in-the-next-decade
9. World Economic Forum (2025). “AI-powered innovation can democratize breakthrough science.” Available at: https://www.weforum.org/stories/2025/06/ai-innovation-democratizes-breakthrough-science/

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