Please describe your proposed solution.

We are continuing the creation of our decentralized protocol for physics simulations and inference used for discovery of therapeutics, drugs, and other industry relevent materials (even Quantum materials) using Machine learning while leveraging Nunet for decentralized computational resources. We aim to make our protocol open ended such that enhanced physics simulations can leverage knowledge from Deep Learning [1], to neuro-symbolic AI [2], quantum chemistry [4], cognitive architectures[5], etc. Additionally, we are building a tokemonics system to incentive computation, data, algorithm development, mining, and community rewards for collaborations and support from individual community members, academics, and even corporations. One of our driving principles is the coupling of advancements in artificial intelligence to advancements in functional near-term technologies.

Our solutions will be useful in markets like Biotechnology, Artificial Intelligence, Chemical Synthesis, and many more. These are quickly growing markets, and would be absolutely amazing for the health of the cardano ecosystem to bridge the market demand home. Take for instance just the Biotechnology market; it is expected to surpass 1.5 Trillion by 2030 and growing at nearly ten percent per year [6].

We're aiming to engineer a substantial shift in the way we compute with Nunet. Essentially, we're building a computational environment designed for smooth integration of multi-scale simulations. This system will use both theoretical and AI-driven algorithms, developed from various data sources. The idea is to improve knowledge extraction and incorporate cognitive principles, establishing a highly interconnected computational network within the physical sciences.

The goal is to match and eventually exceed the capabilities of current High-Performance Computing (HPC) infrastructures. In an ideal scenario, once Nunet is fully developed and backed by a substantial ecosystem, we anticipate the ability to simulate molecular systems faster than most leading supercomputers today.

All of our code will be developed with parallel processing in mind, targeting multi-virtual node CPUs and GPUs. By integrating AI into our approach, we aim to overcome many traditional limitations encountered in high-level computations. Our method promises not only to optimize resource usage, but also to reduce the time required to reach solutions, providing a unique, powerful toolset for innovation in the sciences. We hope these tech-focused details provide a compelling reason to cast your vote in our favor.

Find more information in our LitePaper: https://www.hetzerk.com/litepaper

References:

[1] https://pubs.acs.org/doi/10.1021/acs.accounts.0c00472

[2] https://arxiv.org/abs/2006.11287

[3] https://pubs.acs.org/doi/10.1021/acscentsci.0c01236

[4] http://quantum-machine.org/gdml/

[5] https://arxiv.org/abs/1410.5401

[6] https://www.globenewswire.com//news-release/2022/01/18/2368681/0/en/Biotechnology-Market-Size-to-Surpass-US-1-683-52-Bn-by-2030.html

Please define the positive impact your project will have on the wider Cardano community.

Our proposed solution aims to directly address key areas of the challenge, specifically focusing on computational services and smart contract automation for industrial and research entities.

From an industrial viewpoint, our computational protocol enables users to exchange tokens for precise computational modeling of specific systems or private/public algorithms curated by a wide variety of entities, which may include individuals, research labs, corporations, or community contributors. For the Cardano community, this proposal envisages a reward system, remunerating contributions in areas such as data provision, computational resources, algorithmic development, mining, and technological innovation.

Rewards are primarily derived from the following processes: physics data (incorporating experimental data, simulation data, and theoretical data), computational resources and storage provision, algorithm advancements (including the creation of new algorithms, neural network training, performance enhancement of existing networks), mining operations, and broader technological development. Mining, in this context, refers to the ultimately automated procedure of executing specific computations proposed by community members or recommended by an AI agent. Anyone can participate in this process through staking or resource allocation. Furthermore, entities that contribute to the protocol's development through any of the aforementioned avenues, including mining, can gain rewards through a prearranged distribution of tokens paid by industrial users using smart contracts.

Our immediate objective within this proposal is to develop a minimum viable product (MVP) on the Cardano platform. This MVP aims to initiate collaborative efforts with the pharmaceutical and biotechnology sectors, with a longer-term ambition to serve as the decentralized cloud solution for AI-driven drug, therapeutic, and materials (batteries, quantum devices, LED, glass…) development.

Benefits to the Cardano Ecosystem:

1. User Expansion: The solution attracts an array of users from various backgrounds, thus expanding the user base within the Cardano ecosystem.
2. Ecosystem Enrichment: An influx of diverse users and increased transactional activity will reinforce the robustness of the Cardano ecosystem, facilitating the exchange of innovative ideas and novel solutions.
3. Resource Utilization: By facilitating users to offer their computational resources and storage, the proposal provides a platform to utilize unused resources effectively and reward their contribution.
4. Decentralized Solution: The proposal aids in furthering the cause of decentralized science and research. By providing a platform for a wide range of computational problems, it democratizes access to computational resources and promotes collaboration among researchers.

In terms of quantifying the impact, we anticipate a significant increase in users and transaction volume within a realistic timeframe following project completion. Exact figures will be dependent on various factors such as outreach efforts and adoption rates among potential users. Given the unique value proposition of our platform, we expect a positive response from the scientific and research community.

A handful of markets we may be able to enter, and bring to Cardano, in the coming years are listed below:

Potential Target markets

• Drug Discovery Technologies Market

1. 70 B 2020 -> 110 B 2025 @ 10% CAGR

• AI Market

1. 90 B 2021 -> 1.8 T 2030 @ 38% CAGR

• Global Simulation Software Market

1. 11.5 B 2020 -> 26 B 2027 @ 12.8% CAGR

• Molecular Modeling Market

1. 1.6 B 2027 @ 16% CAGR

• Global Computational Biology

1. 2.7 B 2018 -> 12.4 B by 2027 @ 20.6% CAGR

• Global Computational Fluid dynamics market

1. 2.1 M 2021 -> 3.4 M by 2027 @ 8% CAGR

• Global Semiconductor Modeling

1. 342 M2021 -> 615 M by 2028 @ 8.7% CAGR

• Distributed Cloud Market

1. 3.9 B by 2025 @ 24.1% CAGR

References:

1. <https://www.marketsandmarkets.com/Market-Reports/simulation-software-market-263646018.html>
2. <https://www.prnewswire.com/news-releases/computational-biology-market-size-worth–12-45-billion-globally-by-2028-at-20-64-cagr-verified-market-research-301502624.html#:~:text=According%20to%20Verified%20Market%20Research,20.64%20%25%20from%202019%20to%202026>.
3. <https://www.imarcgroup.com/computational-fluid-dynamics-market>
4. <https://www.globenewswire.com/en/news-release/2021/08/11/2279088/0/en/Molecular-Modeling-Market-Worth-USD-1-617-80-Million-by-2027-at-15-93-CAGR-Report-by-Market-Research-Future-MRFR.html>
5. <https://www.marketwatch.com/press-release/semiconductor-modeling-market-size-share-global-industry-current-trends-top-companies-application-growth-factors-development-and-forecast-to-2028-research-report-2022-05-18-3197059#:~:text=The%20global%20Semiconductor%20Modeling%20market,8.7%25%20during%202022%2D2028>.
6. <https://www.industryarc.com/Report/19198/distributed-cloud-market.html>
7. https://www.bccresearch.com/market-research/biotechnology/drug-discovery-technologies-report.html

What is your capability to deliver your project with high levels of trust and accountability? How do you intend to validate if your approach is feasible?

Capability to Deliver with Trust and Accountability

1. Academic Credentials and Research Experience: As a PhD student in machine learning in Switzerland, you possess a strong academic foundation in a highly relevant field. Your experience and knowledge in machine learning, evidenced by multiple papers published in prestigious venues like ICML, ICLR, and NeurIPS, demonstrate your capability to understand, innovate, and apply complex ML concepts, which are integral to the success of this project.
2. Partnerships with Project Catalysts and SingularityNET: Your active partnerships with Project Catalysts Fund 8 and SingularityNET's Deep Funding Round 1 highlight your ability to collaborate effectively with significant entities in the field. These partnerships not only bring external validation but also offer access to resources, networks, and expertise that are critical for project delivery.

Validating Feasibility

1. Prototype Development and Testing: Leverage your machine learning expertise to develop a working prototype. Test the prototype in real-world scenarios to evaluate performance and identify areas for improvement.
2. Peer Review and Feedback: Submit your methodologies and findings for peer review within your academic and professional networks, including your connections at ICML, ICLR, and NeurIPS. This will provide critical, expert feedback on the feasibility and potential of your approach.
3. Collaborative Pilot Projects: Utilize your partnerships with Project Catalysts and SingularityNET to set up pilot projects. These projects will serve as practical tests of your approach's feasibility in operational environments.

What are the key milestones you need to achieve in order to complete your project successfully?

Initialization and Project Conceptualization: 30000 ADA

Main Tasks / Activities: Assembling the core project team, setting up communication channels, and drafting a comprehensive project blueprint after thorough brainstorming and understanding of Cardano's and Nunet's decentralized and computational landscape and our project's specific requirements.

Expected Delivery Time: (6-12 Weeks)

Success Criteria: Creation of a detailed project blueprint with clear steps and deliverables

>Integration and Testing of Computational Framework: 30000 ADA

Main Tasks / Activities: Integration of our computational framework as defined in the project blueprint, and testing the system's capacity to handle complex computational tasks and various algorithms.

Expected Delivery Time: (12 - 20 Weeks)

Success Criteria: Successful integration of the computational framework with the platform and demonstrated platform reliability against an array of computational tasks.

>Engagement Strategy with Industrial Partners : 10000 ADA

Main Tasks / Activities: Development of a comprehensive strategy to attract and engage with industrial entities, particularly from the pharmaceutical sector, focusing on the unique benefits and potential of our platform to cater to their computational needs.

Expected Delivery Time: (4-8 Weeks)

Success Criteria: Development of a robust industrial engagement strategy and preparation for implementation.

>Deployment and Validation of MVP for Industrial-Scale Molecular Simulation and Modeling : 30000 ADA

Main Tasks / Activities:

• Finalize and deploy the Minimum Viable Product (MVP) of the computational platform, tailored specifically for simulation and modeling of molecular systems in a decentralized environment.
• Conduct extensive validation tests to ensure that the MVP meets industry standards and requirements for accuracy, efficiency, and scalability in molecular simulations and modeling.
• Engage with initial industrial partners to pilot the MVP in real-world scenarios, focusing on complex molecular systems relevant to their research and development needs.
• Collect and analyze performance data, user feedback, and system metrics to assess the MVP’s effectiveness and identify areas for further optimization.

Expected Delivery Time: (10 - 14 Weeks)

Success Criteria:

• Successful deployment of the MVP that demonstrates its capability to efficiently and accurately handle industrial-scale molecular simulations and modeling tasks.
• Positive feedback and validation from industrial partners regarding the MVP’s performance and its alignment with their computational needs.
• Collection of comprehensive performance data and user feedback, leading to a detailed analysis report that highlights the MVP’s strengths and areas for improvement.
• Establishment of a baseline for continuous development and enhancement of the platform, based on real-world usage data and industry feedback.

Who is in the project team and what are their roles?

Justin Diamond, PhD Student in Machine Learning. Codeveloping Hetzerk, a protocol for decentralized physics simulations on Cardano (Fund8)

https://www.linkedin.com/in/justin-sidney-diamond-881798193/

In the project focusing on a decentralized computational platform, Justin Diamond's expertise and role are crucial across various milestones:

1. Milestone 1: Initialization and Project Conceptualization

• Justin Diamond's Contribution:Leading the assembly of the core project team, utilizing his network from his academic and research experiences.
• Contributing to the drafting of a comprehensive project blueprint, drawing upon his deep understanding of computational landscapes from his PhD studies.
• Facilitating brainstorming sessions to align the project with Cardano's and Nunet's decentralized structures.

1. Milestone 2: Integration and Testing of Computational Framework

• Justin Diamond's Contribution:Overseeing the integration of the computational framework, applying his technical expertise from his work in machine learning and data fusion.
• Leading the testing process to ensure the system's capacity to handle complex computational tasks, leveraging his experience in physics and machine learning research.
• Ensuring the platform's reliability and efficiency in processing various algorithms.

1. Milestone 3: Engagement Strategy with Industrial Partners

• Justin Diamond's Contribution:Developing a strategy to engage industrial entities, particularly in the pharmaceutical sector, by highlighting the platform's unique computational capabilities.
• Leveraging his background in bioinformatics and machine learning to communicate the platform's potential to prospective partners.

1. Final Milestone: Deployment and Validation of MVP for Industrial-Scale Molecular Simulation and Modeling

• Justin Diamond's Contribution:Guiding the finalization and deployment of the Minimum Viable Product (MVP), ensuring it meets the specific needs for molecular simulation and modeling in a decentralized environment.
• Leading extensive validation tests, drawing on his research experience at the University of Michigan and TTIC, to verify the MVP’s accuracy, efficiency, and scalability.
• Collaborating with industrial partners to pilot and refine the MVP, utilizing his understanding of complex molecular systems and machine learning applications.

Justin Diamond's robust academic background and diverse research experience in machine learning, bioinformatics, and computational modeling make him an invaluable asset in driving the project’s milestones to success, particularly in developing a platform capable of handling industrial-scale molecular simulations and modeling tasks.

Please provide a cost breakdown of the proposed work and resources.

Milestone 1: Blueprint Development for Protocol and System Architecture

Budget: 30,000 ADA

• Development of Technical Architecture:Designing a detailed technical architecture for the decentralized protocol.
• Resources: Advanced modeling tools, technical expertise.
• Cost: Approx. 15,000 ADA.
• Blueprint Drafting for Data Mechanics:Creating in-depth blueprints for data storage, retrieval, and sharing.
• Resources: Data architecture specialists, documentation tools.
• Cost: Approx. 10,000 ADA.
• Data Security Protocol Design:Establishing robust data security and privacy protocols.
• Resources: Cybersecurity tools, expert consultations.
• Cost: Approx. 3,000 ADA.
• Initial Whitepaper Development:Drafting a comprehensive whitepaper detailing technical and innovative aspects.
• Resources: Research materials, professional writing assistance.
• Cost: Approx. 2,000 ADA.

Milestone 2: Integration and Testing of Computational Framework

Budget: 30,000 ADA

• Framework Integration:Implementing the computational framework according to the blueprint.
• Resources: Software integration tools, IT specialists.
• Cost: Approx. 15,000 ADA.
• System Testing and Validation:Rigorous testing for system capability and performance.
• Resources: Testing software, technical personnel.
• Cost: Approx. 10,000 ADA.
• Technical Documentation:Detailed documentation of the integration and testing processes.
• Resources: Technical writing tools, documentation experts.
• Cost: Approx. 5,000 ADA.

Milestone 3: Development of Industrial Engagement Strategy

Budget: 10,000 ADA

• Engagement Strategy Formulation:Crafting a strategy for future industrial partner engagement.
• Resources: Strategy development tools, consultancy.
• Cost: Approx. 6,000 ADA.
• Preparation for Future Collaborations:Laying the groundwork for potential industrial collaborations.
• Resources: Presentation tools, preliminary outreach efforts.
• Cost: Approx. 4,000 ADA.

Final Milestone: MVP Development and Internal Validation

Budget: 30,000 ADA

• MVP Development and Deployment:Finalizing and deploying the MVP, focusing on internal specifications.
• Resources: Development team, deployment tools.
• Cost: Approx. 15,000 ADA.
• Internal Validation Tests:Conducting extensive internal tests for accuracy and efficiency.
• Resources: Validation tools, technical staff.
• Cost: Approx. 10,000 ADA.
• Performance Analysis and Optimization:Analyzing performance data and optimizing the MVP.
• Resources: Data analysis software, internal feedback mechanisms.
• Cost: Approx. 5,000 ADA.

Total Project Budget: 100,000 ADA

Please note that this budget is subject to modifications as the project progresses. It's designed to focus on our internal work and development, with intentions for future external collaborations.

How does the cost of the project represent value for money for the Cardano ecosystem?

1. Attracting Industrial Players: The project's focus on computational services and smart contract automation is highly relevant for industries like pharmaceuticals, biotechnology, and semiconductor modeling. By offering a decentralized platform for complex computational tasks such as molecular simulations and modeling, Cardano can attract major industrial players. This not only boosts the transaction volume within the ecosystem but also raises the profile of Cardano as a go-to blockchain for industrial computational needs. The entry of these large entities into the Cardano ecosystem could lead to increased adoption of ADA, enhancing its market position.
2. Collaboration with Academic Institutions: The project’s emphasis on decentralized science and research makes it appealing for academic institutions. By facilitating access to high-level computational resources and promoting collaboration, Cardano can become a hub for academic research in computational biology, fluid dynamics, and more. This enhances the ecosystem's reputation as a leader in supporting innovative scientific research.
3. Strengthening Ecosystem Through Diverse User Base: The introduction of industrial and academic players brings a variety of users into the Cardano ecosystem. This diversity enriches the ecosystem with a wide range of perspectives and expertise, fostering a community that's innovative and forward-thinking.
4. Resource Optimization and Reward System: By allowing users to offer computational resources and storage, and rewarding contributions in areas like algorithmic development and data provision, the project optimizes the use of available resources. This not only benefits participants through rewards but also ensures that the Cardano ecosystem is being utilized to its full potential.
5. Demonstrating Real-World Applications: By showing how Cardano can be used for real-world industrial and academic applications, the project significantly enhances the perceived value of the ecosystem. Demonstrating these practical applications can attract further investment and interest in Cardano.
6. Long-Term Growth and Sustainability: The strategic focus on high-growth markets ensures that the project is not only beneficial in the short term but also contributes to the long-term growth and sustainability of the Cardano ecosystem. By establishing itself in markets with high CAGRs, Cardano positions itself for future success and continued relevance.