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...

情報理論的統一プログラムによるエネルギー・材料情報理論：K_energy = -log ρ_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則、各種技術動向 (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.20271536

原典: https://doi.org/10.5281/zenodo.20271536

🤖 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部で、エネルギーシステムを作用素代数的にモデル化し、K_energy = -log ρ_energy を定義する。バッテリー価格のライト則をBNEFデータ（2010-2024）で検証し、学習率25.3%を導出。太陽光、風力、原子力、核融合、水素、材料ゲノミクス、炭素回収などを統一的な枠組みで論じ、10の予測を示す。

English

This paper from the Information-Theoretic Unification programme models the global energy system using operator algebra, defining K_energy = -log ρ_energy as a modular Hamiltonian. It validates Wright's Law on battery pack prices using BNEF data (2010-2024), finding a 25.3% learning rate. It covers solar, wind, nuclear, fusion, hydrogen, materials genomics, and carbon capture, offering 10 predictions. The framework aims to unify diverse energy technologies under an information-theoretic lens.

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

本論文は日本のGX戦略と直接の関連は薄いが、バッテリー学習率や太陽光コスト低減の予測は日本企業の事業計画に示唆を与える。特に固体電池（トヨタ）や水素（$1/kg目標）の言及は日本の技術ロードマップと整合する。

In the global GX context

This paper offers a novel information-theoretic perspective on energy transition, synthesizing multiple technology learning curves. While abstract, its predictions (e.g., battery $42/kWh by 2035) could inform global investment and policy planning. The Wright's Law analysis on a 15-year dataset provides a robust empirical benchmark for technology cost forecasting.

👥 読者別の含意

🔬研究者:Provides a unifying theoretical framework for energy system modeling, with a testable learning-rate hypothesis and falsifiable predictions.

🏢実務担当者:The cost projections (e.g., battery $65/kWh by 2030) may inform strategic R&D and procurement decisions, but the theoretical formalism is not directly applicable.

🏛政策担当者:The paper's broad scope and predictions can support long-term energy planning, though the theoretical nature limits immediate policy applicability.

📄 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.20271536first seen 2026-05-20 05:15:26

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