Genesis Mission, Part II — The Hardcore Center: The Scientific Foundation Model

Why DOE? What Could Possibly Go Right—or Wrong?

Why the Department of Energy? Why now?

Because everything—everything—hinges on the Scientific Foundation Model.

If you ask me, the single most ambitious and high-risk/high-reward goal inside the Genesis Mission is the establishment of the Scientific Foundation Model (SFM).

This is not an AI project.

This is the blueprint for an entirely new scientific civilization.

Before we can understand DOE’s role, data standardization, or what might go right or catastrophically wrong, we must define—precisely—what a Scientific Foundation Model actually is.

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What is a Scientific Foundation Model (SFM)? — My Definition

A Scientific Foundation Model (SFM) is a large-scale, multi-domain AI model trained on heterogeneous scientific datasets—experimental, observational, simulation-based, instrumental, and mechanistic—underpinned by physical laws, calibrated measurements, and structured scientific semantics, enabling unified representation, cross-domain transfer, predictive reasoning, design generation, optimization, and autonomous scientific workflows across the natural and engineered world.

This is the most complete definition you will find anywhere.

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1. It Is Large-Scale

(Comparable to GPT/Gemini—but trained on the Scientific Data Universe)

• Billions to trillions of parameters

• Multimodal encoders

• Massive cross-domain representation capacity

• But unlike language models, the training corpus is not text

• It is: physics, materials, climate, chemistry, biology, quantum, fusion, manufacturing…

  the entire scientific world digitized.

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2. It Is Multi-Domain

Spanning:

• materials

• energy

• physics

• quantum systems

• biology & biotechnology

• chemistry

• climate & earth systems

• engineering & manufacturing

• HPC simulation domains

And all of these map into a Unified Representation Space.

This is the first time in history that such a space is even conceivable.

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3. It Is Trained on Heterogeneous Scientific Data

Including:

• Experimental data (X-ray, neutron scattering, TEM, AFM, spectroscopy…)

• Simulation data (PDE solvers, MD, QM, CFD, plasma MHD, quantum circuits…)

• Observational data (climate archives, satellite data, sensor logs)

• Spatiotemporal fields (flows, electromagnetic fields, trajectories, dynamics)

• Structured graph-based data (molecules, lattices, reaction networks)

• Instrument-level raw signals (detector data, calibration curves, error bounds)

This is data with far higher signal density than any text corpus on the planet.

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4. The Data Is Constrained by Physics

Unlike human text, scientific data is:

• governed by conservation laws

• structured by PDEs

• consistent with energy landscapes

• symmetric under group-theoretic constraints

• calibrated against physical instruments

• bounded by measurable error

This means:

SFM does not learn the artifacts of human language.It learns the structure of the physical world.

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5. It Learns a Unified Scientific Representation Space

Built from:

• tensor representations

• manifold embeddings

• graph / hypergraph structures

• PDE-aware encodings

• Scientific Primitives (your Primitive IR layer)

This is the actual “shared language of science” that has never existed before.

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6. It Performs the Entire Scientific Cycle, Not Just Prediction

An SFM can:

1. Understand scientific data and underlying structure

2. Predict physical behavior, material properties, reaction pathways

3. Invert from effects back to causes (inverse problems)

4. Generate and design molecules, materials, systems, devices

5. Optimize parameters, processes, experimental settings

6. Automate reasoning (hypothesis generation, experiment planning)

7. Drive autonomous labs (through AI agents)

8. Transfer across domains (one model → many scientific fields)

This is why SFM is not “AI for science.”

It is AI becoming a scientific agent.

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Engineering Definition

From an engineering perspective, an SFM is:

A unified, scalable AI runtime that consumes standardized scientific data schemas and produces cross-domain predictions, simulations, designs, and decisions through a common model interface.

It includes:

• multimodal encoders (graph, tensor, sequence, PDE)

• physics-aware architectures (PINNs, neural operators, GNNs, transformers)

• domain adapters

• a unified Scientific IR layer

• task heads (predict, invert, design, optimize)

• agent interfaces for autonomous science

This is the scientific equivalent of an operating system kernel.

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Philosophical Definition

The SFM is humanity’s first attempt to compile the scientific world into a computable, negotiable, scheduleable model-universe.

Language models learn human speech.

SFM learns nature itself.

This is the real paradigm shift.

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Why SFM Is the Absolute Core of Genesis Mission

Every line of the Executive Order points to this:

• Data standardization → the fuel

• National labs → the engine bay

• HPC → the engine

• AI agents → the transmission

• The unified platform → the chassis

• Scientific breakthroughs → the output

Without SFM, Genesis Mission is just “better scientific IT.”

With SFM, it becomes:

‼️ a new scientific civilization infrastructure

‼️ the first true cross-disciplinary scientific operating system

‼️ the first attempt to compile physical reality into a model

This is why SFM is the hardcore center.

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A Scientific Foundation Model (SFM) is a physics-structured, multi-domain, cross-scale AI model that unifies prediction, inversion, design, and optimization across natural and engineered systems, enabling transferable scientific intelligence.

Or the ultrahardcore version:

SFM = a unified AI model that learns the structure of the physical world.

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It Sounds Unreal. Why DOE? What Could Possibly Go Right—or Wrong?

Here’s my projection.

1. DOE has the right data — accumulated for decades, massive in scale, and scientifically correct.

No other institution on Earth holds anything comparable: climate archives, fusion plasma logs, X-ray scattering databases, particle detector outputs, superconductivity datasets, multi-decade HPC simulations, quantum noise spectra, materials phase transitions, and every form of structured scientific signal you can imagine.

This is the scientific treasury of the United States.

2. But will non-language data actually work for foundation models?

That’s the billion-dollar question.

Scientific data aren’t text; they are fields, tensors, manifolds, PDE trajectories, instrument signals.

If Scaling Laws apply more cleanly to physics-structured, low-noise data than to messy human text, the SFM could unlock a new scientific paradigm.

If not, the entire mission collapses under its own ambition.

3. And are these datasets even in the right format? Can we make them compatible?

DOE data are rich—but fragmented.

Different labs, different instruments, different file formats, different metadata conventions, and overwhelming amounts of semi-classified measurements.

Before an SFM can exist, these datasets must be standardized, schema-aligned, calibrated, provenance-tracked, and transformed into a unified scientific IR.

If that works, the SFM becomes inevitable.

If it fails, the entire platform becomes an impossible integration problem.

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最硬核的核心：科学基础模型（SFM）

因为一切——所有的一切——都取决于“科学基础模型”（Scientific Foundation Model, SFM）。

如果你问我，Genesis Mission 中最雄心最大、风险最高但潜在回报也最高的目标，就是建立一个 科学基础模型（SFM）。

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什么是科学基础模型（SFM）？——我的定义

科学基础模型（SFM）是一类大规模、跨领域的 AI 模型，训练于多源异构科学数据——包括实验、观测、仿真、仪器、以及机制性数据——并由物理定律、校准测量与科学语义所约束。它能够学习统一表示，实现跨领域迁移、预测推理、设计生成、优化决策，并推动自然与工程系统中的自主科学工作流。

这是你能找到的最完整、最高精度的定义。

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