ITU-Derived Energy & Materials Information Theory: K_energy = -log rho_energy — Operator-Algebraic Energy System Modular Hamiltonian, Battery Wright's Law (BNEF 2010-2024, $1100 -> $115/kWh, 25.3% Learning Rate), Goodenough Nobel 2019, Perovskite Tandem 33.9% (Oxford PV 2024.6), NIF Fusion Ignition 2022.12.5 + 5.2 MJ Enhanced 2024, ITER 2035, SMR Renaissance (TerraPower, X-energy DOE $1.2B), Green Hydrogen, DeepMind GNoME 2023 Nature (2.2M Crystals, 380K Stable), Carbon Capture (Climeworks Ma...

ITU由来のエネルギー・材料情報理論: K_energy = -log rho_energy — 作用素代数的エネルギーシステムのモジュラーハミルトニアン、バッテリーのライトの法則（BNEF 2010-2024、$1100→$115/kWh、25.3%学習率）、グッドイナフノーベル賞2019、ペロブスカイトタンデム33.9%（オックスフォードPV 2024年6月）、NIF核融合点火2022年12月5日＋5.2MJ増強2024年、ITER2035年、SMRルネサンス（テラパワー、X-energy DOE12億ドル）、グリーン水素、DeepMind GNoME 2023 Nature（220万結晶、38万安定）、炭素回収（クライムワークス…） (AI 翻訳)

Munehiro Terada

Zenodo (CERN European Organization for Nuclear Research)プレプリント2026-05-18#エネルギー転換

DOI: 10.5281/zenodo.20271535

原典: https://doi.org/10.5281/zenodo.20271535

🤖 gxceed AI 要約

日本語

本論文は情報理論的統一プログラムの一環として、エネルギーシステムの作用素代数的モジュラーハミルトニアンK_energyを定義。BNEFデータを用いたバッテリーライトの法則分析（学習率25.3%、2028年$82/kWhと予測）、太陽光・風力・核融合・水素・CCUSなど16分野の定量的深堀りと10の反証可能な予測を提供する。エネルギー転換の統一理論枠組みを提案し、材料ゲノミクスやグリッドストレージも包含する。

English

This paper defines the operator-algebraic modular Hamiltonian K_energy for energy systems as part of an information-theoretic unification program. It presents a Wright's Law analysis on battery costs (25.3% learning rate, projecting $82/kWh by 2028), deep dives into 16 energy technologies (solar, wind, fusion, hydrogen, CCUS, etc.), and offers ten falsifiable predictions. The framework unifies energy transition theory with materials genomics and grid storage.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

本論文はエネルギー転換を情報理論で統一的に記述する枠組みを提案。日本のGX政策（洋上風力、水素、蓄電池）にも示唆を与える理論的基盤を提供する。

In the global GX context

This paper proposes a unifying information-theoretic framework for the global energy transition, including quantitative learning curves and falsifiable predictions. Valuable for scholars and practitioners in energy systems modeling.

👥 読者別の含意

🔬研究者:Unified framework for energy transition modeling and falsifiable hypotheses for technology milestones.

🏢実務担当者:Learning rate projections for battery costs and timelines for key technologies (e.g., battery $80/kWh by 2028).

🏛政策担当者:Falsifiable predictions for energy technology milestones that can inform R&D and investment strategies.

📄 Abstract（原文）

Tenth Pass-1.5 paper of the Information-Theoretic Unification (ITU) programme. K_energy = -log rho_energy as the operator-algebraic modular Hamiltonian of the global energy system, defined over (generation source x storage type x conversion efficiency x demand pattern x grid topology). 16-phase deep dive unifying: battery technology (Whittingham 1976 TiS2, Goodenough 1980 LiCoO2 Nobel Chem 2019, Sony 1991 first commercial Li-ion, BNEF $115/kWh 2024 -90% from 2010, solid-state Toyota 2027-28, sodium-ion CATL Naxtra 2023); solar (Shockley-Queisser 1961, Oxford PV 33.9% perovskite-Si tandem 2024.6 commercial-scale record, LCOE $0.04-0.08/kWh cheapest source 2024, 1 TW installed 2024); wind (Hornsea 2 UK 1.4 GW 2022 largest, Hywind Tampen Norway floating 2023, Vineyard Wind 1 US 2024.1); nuclear+SMR (TerraPower Wyoming 2030, X-energy DOE $1.2B 2024.10, NuScale NRC 2023, COP28 tripling pledge); fusion (NIF ignition 2022.12.5 Q=1.5 3.15 MJ output, 2024 enhanced 5.2 MJ, ITER 2035 first plasma, Commonwealth Fusion SPARC, WEST 1337 s plasma 2025.2); hydrogen ($2-7/kg green, target $1/kg by 2030, IRA $3/kg credit); materials genomics (DeepMind GNoME 2023.11 Nature 2.2M crystal candidates 380K stable AI accelerated 800x, MatterGen Microsoft); carbon capture (Climeworks Mammoth 36k tCO2/yr 2024.5, 1PointFive Stratos 500k tCO2/yr, $400-1000/tCO2 -> target $100-200, US 45Q + IRA $180/tCO2); grid storage (Form Energy iron-air 100h, CATL LFP 70% market); critical minerals (Li/Co/Ni/REE China 80% refining, IRA + EU CRMA 2024); EV (14M sales 2023 18%, BYD > Tesla BEV Q4 2023, Tesla Cybercab 2024.10, NACS standard). Numerical Phase 505: Wright's Law fit on BNEF Li-ion pack price 2010-2024 ($1100 -> $115/kWh, 9.6x drop in 14 years). P = a*Q^b regression on cumulative TWh production yields a=196.7, b=-0.4199, learning rate = 1 - 2^b = 25.3% per cumulative-production doubling, R^2 = 0.954. ITU interpretation: K_cost reduction per doubling = -b*ln(2) = 0.291 nats. Wright's Law (1936) recast as ITU modular flow on rho_market. Forward projection: $82/kWh 2028, $65/kWh 2030, $42/kWh 2035. Four hypotheses (H_EN1-H_EN4): operator-algebraic energy state; transition entropy (diversification increases K_energy); Wright's Law = ITU learning curve (constant dK_cost per doubling); materials genomics = K_material exploration acceleration. Ten falsifiable predictions P_avg=0.60, S/M/W=1/7/2 (highlights: battery $80/kWh by 2028 P=0.75, solid-state 2028 P=0.55, fusion commercial 2032 P=0.40, ITER first plasma 2035 P=0.55, solar 50% world electricity 2050 P=0.65, green H2 $1/kg 2030 P=0.50). New 45-vertex polytope #10 top couplings: #11 Climate (0.95), #4 Semi (0.92), #15 Infrastructure (0.92), #39 Manufacturing (0.92), #41 Agriculture (0.85). Degree (>0.7): 6, total (>0.5): 27. Pass-2 roadmap ~$1.8M: K_energy grid analytics $600K + Lean Mathlib formalization $200K + materials AI partnership $1M. Main new contributions: (1) K_energy operator-algebraic definition; (2) Wright's Law as K_information learning curve with 0.291 nats/doubling on 15-yr BNEF series; (3) materials genomics framed as K_material exploration acceleration. Next: Tier 1+ #11 Climate (K_climate Earth system modular Hamiltonian). Pass-1.5 progress: 10/45 = 22.2%. License: CC-BY-4.0.

🔗 Provenance — このレコードを発見したソース

openalex https://doi.org/10.5281/zenodo.20271535first seen 2026-05-20 05:15:10

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