. Currently, most of this information is dormant, redundant, and inconclusive. The data is frequently dormant as the simulation data is analyzed for publications or industrial applications and then held on local data storage units, redundant as there are often teams around the world doing highly similar simulations, and inconclusive because often single simulations lack enough information to lead to conclusive results. Thus, centralized infrastructures are rather limiting in developing AI-centric frameworks for improving the efficiency and accuracy of physics computation and knowledge extraction. As bad as this is, this is only the surface of the problem. The larger problem is that there is no natural way to incorporate vast and diverse amounts of physics information (experiments, quarks, chemicals, proteins), data, knowledge , and algorithms in a cohesive and synergetic manner.

We are creating a decentralized protocol for the simulation of physical systems while leveraging Nunet for computational resources and SingularityNet for AI enhancements with open ended improvements using anything 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, to computer scientists, and even corporations. One of our driving principles is the coupling of advancements in artificial intelligence to advancements in functional near-term technologies.

The paradigm shift we are creating with SNet and Nunet stems from creating a computational and algorithm environment for end-to-end integration of multi-scale simulations for developing and employing theoretical and AI algorithms built up from heterogeneous data sources, symbolic knowledge extraction, and cognitive principles to lead to the most interconnected framework for self-consistent computations in the physical sciences.

From an industry perspective, users (entities taking advantage of our computational protocol) can exchange tokens for theoretical computations of a particular system of study and/or private/public algorithms developed by various entities (individuals, research labs, corporations, community members). From the community perspective they will get rewarded for the contribution to data, computation, algorithm development (to name a few).

Rewards are mostly obtained from the following procedures: physics data (experiments, simulation data, theory), computational resources and storage, algorithm developments (developing new algorithms, training neural networks, improving existing networks), mining, and technology development. The first two are rather clear. In short, mining is the eventually-automated process of performing specific computations as suggested by community members or recommended by an AI agent that anyone can partake in by staking or resource allocation. As well, entities that develop on the protocol (via any of the above including mining) can obtain rewards via a predetermined ratio of tokens paid by industrial entities using smart contracts.

This is an AI project that wants to show the community what type of technological advancements can be made with decentralized computation and AI and their ability to transform multiple different economic sectors, while also rewarding the community member's support and development.

Hetzerk will directly and indirectly make use of SingularityNET's marketplace AI services by making calls to the AGIX API with Nunet in the backend, which inevitably benefits the Cardano ecosystem as a consequence of SNET's Cardano dependence.

Our budget request is intended to meet our 7-month milestones with additional funding necessary for future development via additional Catalyst funds, other funding avenues, or token generation events.

Budget Allocation:

Miscellaneous Hardware for local testing and development is not needed as we currently have self-owned servers. Any additional resources will be obtained out-of-pocket to improve our chances of obtaining funding.

Function Person/months People Salary Total

Blockchain Development (Plutus) 7 1 $2,500 $17,500

Physics Protocol Engineering 7 1 $2,500 $17,500

Timeline:

At 3 months -> Basic set of theoretical algorithms being run on Nunet and projects/comparisons with centralized servers.

At 6 months -> Demonstration of basic AI incorporated simulations, and white paper including specifics about tokenomics and protocols. Additionally, updated metrics compared to centralized servers and other AI methods leading to at least one publication in a peer-reviewed journal.

At 7-8months -> General prototype for decentralized applications of industrial needs including smart contact design implementation.

After 12 months -> Partnerships and Industry collaboration. Additional funding requests or token generation events to continue developing objectives refined in White Paper. Prototyping mining protocols and tokenomics.

These are slightly difficult to precisely define as we will be developing our protocol as nunet, specifically, matures. Thus, many of our timeline objectives will be dependent on progress with Nunet.

KPI / Metrics / Deliverables:

This Catalyst proposal will aid Hetzerk in prototyping large scale computations of physical systems using SingularityNet and Nunet allowing for the continued growth and progressive development with further funding.

Increased number of transactions on Cardano due to SingularityNET AI service calls.

Prototypes of Traditional Computational Algorithms:

1. Langevin Dynamics Integrator

2. Open Source Molecular Dynamics

3. Density Functional Theory

Prototypes of AI based Simulations:

1. 1-3 open source projects for AI enhanced molecular dynamics, and electron density calculations

2. Functionality to update Neural Network parameterizations

3. Training on heterogeneous data obtained from community members or Traditional Algorithms as testbed for multi-scale approaches

4. Possible collaborations with DeepChainAda in training neural networks

Successful measures include

1. Metric showing scalability of Nunet (and future projections) of theoretical simulations

2. Increased accuracy of AI enhanced simulations due to heterogeneous data collection

3. In due course, we will prototype neuro-symbolic integration using OpenCog 2.0 (among others), and other approaches such as Recommendation Systems to suggest optimal computations to be performed when faced with uncertainty in results or models.

Risk analysis & mitigation:

Risk (1): Immediate hiring need. Our team is actively searching for plutus developers now. Until a plutus developer is found, I will be in close collaboration with the community for difficulties in Cardano or Singularity Net protocols.

Members:

Justin Diamond - PhD Candidate - AI Researcher in Physics, Chemistry, Pharma, Bioinformatics at academic institutions including University of Michigan, Toyota Technological Institute of Chicago, Boston University, University of Luxembourg, and University of Basel.

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

<https://github.com/blindcharzard>

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>

ABOUT HETZERK:

Hetzerk, a neuro-symbolic computational science research and engineering company. We build ab-initio learning algorithms to integrate theoretical, computational, and cognitive science to describe micro and macroscopic properties of matter.

We are also creating infrastructure and partnerships to scale towards a decentralized platform of synergetic AI agents to study matter.

Our company believes there is a principle of computational disconnectedness between theory, computation, and approximations in physics and beyond. The current principle of modeling subatomic and macroscopic systems is built from a graph of interconnected physical theories, but which are quite disconnected computationally. Whereby participating scientists are needed to piece together the overarching themes. Here, at Hetzerk, we are integrating cognitive perspectives with ab initio principles for scaling computational methods to model diverse types of matter from organic therapeutics, semiconductors, and under explored exotic state of quantum systems.

Hetzerk is attempting to position itself as the de facto infrastructure for theoretical and applied material science and discovery. Near term blockchain solutions for decentralized computation and AI will transcend the modern, disconnected, approach to building computational models of atoms, molecules, and cells. This, in addition to advances in symbolic based artificial intelligence has the potential to dramatically outperform data-hungry Deep Learning and computationally demanding Quantum Physics algorithms.

Many future material technologies and therapeutics will be conglomerately developed by independent and spatially separated entities like institutions, startups, and individual community members to take advantage of large scale emergent knowledge and computation of neuro-symbolic AI and blockchain. We are building technologies, new paradigms, and software infrastructure to act as the glue for the future of computational modeling. Our company's objective is firstly to create neuro-symbolic algorithms for the advanced computation of material properties and second to continue building next-gen blockchain solutions for scalable computational and knowledge based development of novel materials with Intellectual Property allocation management.