Revolutionising autonomous labs for accelerated materials discovery
Revolutionising autonomous labs for accelerated materials discovery
Revolutionised autonomous labs for accelerated
materials discovery
Our loop of discovery
Our loop of discovery
Our loop
of discovery
Dunia pioneers a paradigm shift in materials discovery by seamlessly integrating the Design-Make-Test-Analyze (DMTA) cycle. Our innovative closed-loop process marks a departure from traditional methods, leveraging the power of artificial intelligence and robotic precision to redefine the landscape of materials innovation.
Artificial Intelligence
Tailored for real-world data, Dunia’s physics-informed AI integrates principles of physics and empirical verification into its algorithms, providing a robust, insightful approach to problem-solving.
Robotic Automation
Precision meets efficiency as Dunia's cutting edge robotic platform rapidly executes electrochemical experiments, meticulously capturing data to ensure traceable, reproducible results.
Using Dunia’s physics-informed algorithm, our AI accelerates the ideation process, enabling innovative, unbiased material design that can optimise for desired characteristics.
Using Dunia’s physics-informed algorithm, our AI accelerates the ideation process, enabling innovative, unbiased material design that can optimise for desired characteristics.
Using Dunia’s physics-informed algorithm, our AI accelerates the ideation process, enabling innovative, unbiased material design that can optimise for desired characteristics.
Using Dunia’s physics-informed algorithm, our AI accelerates the ideation process, enabling innovative, unbiased material design that can optimise for desired characteristics.
Our robotic tools ensures reproducible and reliable material production. Comprehensive metadata capture guarantees all datapoints can be traced back to the sample of origin.
Our robotic tools ensures reproducible and reliable material production. Comprehensive metadata capture guarantees all datapoints can be traced back to the sample of origin.
Our robotic tools ensures reproducible and reliable material production. Comprehensive metadata capture guarantees all datapoints can be traced back to the sample of origin.
Our robotic tools ensures reproducible and reliable material production. Comprehensive metadata capture guarantees all datapoints can be traced back to the sample of origin.
Our testing set-up is designed for industrial scalability. By simulating relevant processing conditions and timescales, we ensure that all of our results translate seamlessly to industry application.
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Our testing set-up is designed for industrial scalability. By simulating relevant processing conditions and timescales, we ensure that all of our results translate seamlessly to industry application.
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Processing Condition - 1
Processing Condition - 2
Processing Condition - 3
Our testing set-up is designed for industrial scalability. By simulating relevant processing conditions and timescales, we ensure that all of our results translate seamlessly to industry application.
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Processing Condition - 1
Processing Condition - 2
Processing Condition - 3
Our testing set-up is designed for industrial scalability. By simulating relevant processing conditions and timescales, we ensure that all of our results translate seamlessly to industry application.
Our algorithms meticulously dissect and interpret massive datasets, uncovering key insights that drive the next iteration of the design phase; completing the closed-loop cycle and ensuring continuous optimisation.
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Our algorithms meticulously dissect and interpret massive datasets, uncovering key insights that drive the next iteration of the design phase; completing the closed-loop cycle and ensuring continuous optimisation.
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Processing Condition - 1
Processing Condition - 2
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Our algorithms meticulously dissect and interpret massive datasets, uncovering key insights that drive the next iteration of the design phase; completing the closed-loop cycle and ensuring continuous optimisation.
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Our algorithms meticulously dissect and interpret massive datasets, uncovering key insights that drive the next iteration of the design phase; completing the closed-loop cycle and ensuring continuous optimisation.
Optimising for
what matters
Optimising for
what matters
Activity
Catalysts reduce the energy barrier for chemical reactions. With the right catalyst, transformation that would take years occur in a matter of seconds. In other words, catalysts make impossible chemistry possible. Catalysts directly impact one of the major cost drivers for greener chemicals, the energy efficiency.
Catalysts reduce the energy barrier for chemical reactions. With the right catalyst, transformation that would take years occur in a matter of seconds. In other words, catalysts make impossible chemistry possible. Catalysts directly impact one of the major cost drivers for greener chemicals, the energy efficiency.
Stability
Large-scale industrial processes run for years without interruption. Yet, most catalyst candidates are only tested for a few hours. Consequently, lack of stability is one of the most common reason for laboratory results failing to translate to real-world operation. Dunia’s platform focuses on extensive testing and uses lifetime prediction models for early detection of failure modes.
Large-scale industrial processes run for years without interruption. Yet, most catalyst candidates are only tested for a few hours. Consequently, lack of stability is one of the most common reason for laboratory results failing to translate to real-world operation. Dunia’s platform focuses on extensive testing and uses lifetime prediction models for early detection of failure modes.
Selectivity
Catalysts direct chemical reactions to desired products. This feature does not only reduce waste, it minimizes the need for costly separation steps. For climate-critical processes such as electrochemical CO2 reduction, selectivity is an unresolved challenge that can only be addressed with a new generation of breakthrough catalysts.
Catalysts direct chemical reactions to desired products. This feature does not only reduce waste, it minimizes the need for costly separation steps. For climate-critical processes such as electrochemical CO2 reduction, selectivity is an unresolved challenge that can only be addressed with a new generation of breakthrough catalysts.
Scalability
Critical minerals are in rare supply and often very expensive. Large scale adoption of green processes is dependant on scalable manufacturing processes and a robust supply chain for the active materials. Dunia’s catalysts are optimized for scalability by keeping track of precursor abundance, cost and processing requirements.
Critical minerals are in rare supply and often very expensive. Large scale adoption of green processes is dependant on scalable manufacturing processes and a robust supply chain for the active materials. Dunia’s catalysts are optimized for scalability by keeping track of precursor abundance, cost and processing requirements.
Case studies
Case studies
CASE STUDY
End-to-end discovery campaign
Identifying high-performing catalyst inks within months.
Problem
AEM water electrolysis is an emerging technology that has the potential to unlock the hydrogen economy. Trial-and-error experimentation leads to inefficient experimentation which ultimately hinder commercialization.
Solution
Our autonomous platform used ML-guided design with automated experimentations to quickly identify high performing catalyst ink formulations, outperforming human researchers.
Impact
Our platform outperformed human researchers in increasing energy efficiency while simultaneously minimizing cost. Within just few months, high-performing formulations were identified and validated under industrially relevant conditions.
CASE STUDY
ML-driven Design
Efficient navigation of chemical search spaces.
Problem
Finding the right starting point for a catalyst discovery campaign is difficult as human researchers struggle to screen through the vast amount of scientific data.
Solution
We leveraged unsupervised machine learning techniques in conjunction with domain-specific materials representations to map out chemical search spaces.
Impact
Our data-driven tools enable the efficient navigation of chemical search spaces by exploiting existing knowledge as well as highlighting underexplored areas to search for novel solutions.
CASE STUDY
Automated synthesis
Accessing advanced nanomaterials by tuning processing conditions.
Problem
Particle sizes, distributions and morphology affect the catalytic activity performance of nanostructured materials. Yet, precise control of these properties is difficult to achieve, creating a synthesis bottleneck.
Solution
By tuning the processing conditions of modern synthetic protocols, multi-metallic nanoparticle catalysts were prepared that exhibited high catalytic activity.
Impact
Our platform has access to a large variety of nanostructured materials and can directly map the influence of processing conditions to catalytic function.
CASE STUDY
High-throughput testing
40% increased performance for supercapacitor additives.
Problem
Identifying the right additives is crucial for enhancing the performance and longevity of supercapacitors, but the extensive range of possibilities makes the optimization process difficult and labour-intensive.
Solution
A high-throughput robotic platform was employed to perform electrochemical characterisation and probe the redox behaviour of additives for supercapacitors.
Impact
During an extensive search campaign more than 2500 electrochemical tests were performed, resulting in 45 % increase in capacitance for the champion formulation.
CASE STUDY
Data capture and analysis
100% traceability of research data.
Problem
Machine learning system trained on literature data often yield unsatisfactory performance due to publication biases and missing details in experimental procedures.
Solution
Dunia built a proprietary electrolysis dataset in which meta data are meticulously captured throughout the full sample history. All data is mapped on a domain-specific schema and managed in a cloud database.
Impact
All data are 100% traceable to the sample origin, enabling us to build more powerful machine learning models and immediately validate promising material candidates.
CASE STUDY
End-to-end discovery campaign
Identifying high-performing catalyst inks within months
Problem
AEM water electrolysis is an emerging technology that has the potential to unlock the hydrogen economy. Trial-and-error experimentation leads to inefficient experimentation which ultimately hinder commercialization.
Solution
Our autonomous platform used ML-guided design with automated experimentations to quickly identify high performing catalyst ink formulations, outperforming human researchers.
Impact
Our platform outperformed human researchers in increasing energy efficiency while simultaneously minimizing cost. Within just few months, high-performing formulations were identified and validated under industrially relevant conditions.
CASE STUDY
ML-driven Design
Efficient navigation of chemical search spaces
Problem
Finding the right starting point for a catalyst discovery campaign is difficult as human researchers struggle to screen through the vast amount of scientific data.
Solution
We leveraged unsupervised machine learning techniques in conjunction with domain-specific materials representations to map out chemical search spaces.
Impact
Our data-driven tools enable the efficient navigation of chemical search spaces by exploiting existing knowledge as well as highlighting underexplored areas to search for novel solutions.
CASE STUDY
Automated synthesis
Accessing advanced nanomaterials by tuning processing conditions
Problem
Particle sizes, distributions and morphology affect the catalytic activity performance of nanostructured materials. Yet, precise control of these properties is difficult to achieve, creating a synthesis bottleneck.
Solution
By tuning the processing conditions of modern synthetic protocols, multi-metallic nanoparticle catalysts were prepared that exhibited high catalytic activity.
Impact
Our platform has access to a large variety of nanostructured materials and can directly map the influence of processing conditions to catalytic function.
CASE STUDY
High-throughput testing
40% increased performance for supercapacitor additives
Problem
Identifying the right additives is crucial for enhancing the performance and longevity of supercapacitors, but the extensive range of possibilities makes the optimization process difficult and labour-intensive.
Solution
A high-throughput robotic platform was employed to perform electrochemical characterisation and probe the redox behaviour of additives for supercapacitors.
Impact
During an extensive search campaign more than 2500 electrochemical tests were performed, resulting in 45 % increase in capacitance for the champion formulation.
CASE STUDY
Data capture and analysis
100% traceability of research data
Problem
Machine learning system trained on literature data often yield unsatisfactory performance due to publication biases and missing details in experimental procedures.
Solution
Dunia built a proprietary electrolysis dataset in which meta data are meticulously captured throughout the full sample history. All data is mapped on a domain-specific schema and managed in a cloud database.
Impact
All data are 100% traceable to the sample origin, enabling us to build more powerful machine learning models and immediately validate promising material candidates.
CASE STUDY
End-to-end discovery campaign
Identifying high-performing catalyst inks within months
Problem
AEM water electrolysis is an emerging technology that has the potential to unlock the hydrogen economy. Trial-and-error experimentation leads to inefficient experimentation which ultimately hinder commercialization.
Solution
Our autonomous platform used ML-guided design with automated experimentations to quickly identify high performing catalyst ink formulations, outperforming human researchers.
Impact
Our platform outperformed human researchers in increasing energy efficiency while simultaneously minimizing cost. Within just few months, high-performing formulations were identified and validated under industrially relevant conditions.
CASE STUDY
ML-driven Design
Efficient navigation of chemical search spaces
Problem
Finding the right starting point for a catalyst discovery campaign is difficult as human researchers struggle to screen through the vast amount of scientific data.
Solution
We leveraged unsupervised machine learning techniques in conjunction with domain-specific materials representations to map out chemical search spaces.
Impact
Our data-driven tools enable the efficient navigation of chemical search spaces by exploiting existing knowledge as well as highlighting underexplored areas to search for novel solutions.
CASE STUDY
Automated synthesis
Accessing advanced nanomaterials by tuning processing conditions
Problem
Particle sizes, distributions and morphology affect the catalytic activity performance of nanostructured materials. Yet, precise control of these properties is difficult to achieve, creating a synthesis bottleneck.
Solution
By tuning the processing conditions of modern synthetic protocols, multi-metallic nanoparticle catalysts were prepared that exhibited high catalytic activity.
Impact
Our platform has access to a large variety of nanostructured materials and can directly map the influence of processing conditions to catalytic function.
CASE STUDY
High-throughput testing
40% increased performance for supercapacitor additives
Problem
Identifying the right additives is crucial for enhancing the performance and longevity of supercapacitors, but the extensive range of possibilities makes the optimization process difficult and labour-intensive.
Solution
A high-throughput robotic platform was employed to perform electrochemical characterisation and probe the redox behaviour of additives for supercapacitors.
Impact
During an extensive search campaign more than 2500 electrochemical tests were performed, resulting in 45 % increase in capacitance for the champion formulation.
CASE STUDY
Data capture and analysis
100% traceability of research data
Problem
Machine learning system trained on literature data often yield unsatisfactory performance due to publication biases and missing details in experimental procedures.
Solution
Dunia built a proprietary electrolysis dataset in which meta data are meticulously captured throughout the full sample history. All data is mapped on a domain-specific schema and managed in a cloud database.
Impact
All data are 100% traceable to the sample origin, enabling us to build more powerful machine learning models and immediately validate promising material candidates.
Our Whitepaper
As our society is becoming more conscious of the detrimental consequences of a fossil-based economy, efforts are being made to shift the current industry to more sustainable processes. However, present-day sustainable processes are too expensive and have been deemed unfeasible for industrial-scale production. Catalysis as a key enabler could help make the transition to renewable energy sources more efficient and cost effective. However, the current paradigm in catalyst innovation is lagging behind the goal of reaching net zero by 2030. By leveraging artificial intelligence and robotic technologies, we can accelerate the research and development of catalyst discovery for sustainable processes such as using hydrogen fuel, upcycling CO2 to carbon-neutral chemicals, and using sustainable fuels. Herein, we discuss the necessities for an operational catalyst acceleration concept and the societal impact of breakthrough catalytic materials.
As our society is becoming more conscious of the detrimental consequences of a fossil-based economy, efforts are being made to shift the current industry to more sustainable processes. However, present-day sustainable processes are too expensive and have been deemed unfeasible for industrial-scale production. Catalysis as a key enabler could help make the transition to renewable energy sources more efficient and cost effective. However, the current paradigm in catalyst innovation is lagging behind the goal of reaching net zero by 2030. By leveraging artificial intelligence and robotic technologies, we can accelerate the research and development of catalyst discovery for sustainable processes such as using hydrogen fuel, upcycling CO2 to carbon-neutral chemicals, and using sustainable fuels. Herein, we discuss the necessities for an operational catalyst acceleration concept and the societal impact of breakthrough catalytic materials.