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はじめに
近年、材料研究の現場では 「マテリアルズ・インフォマティクス（MI）をどう導入し、成功させるか」 が大きなテーマとなっています。Polymerizeは、日本の熱可塑性ポリウレタンメーカーをはじめとする多くのお客様とともに、データ駆動型アプローチを実現し、研究効率化と新材料探索の両立を支援してきました。
本記事では、複数の導入事例をもとに、MI成功の共通要因 を整理し、研究者の皆様に役立つ示唆をご紹介します。
共通する研究課題
各事例に共通して見られた課題は以下の通りです。
- データの不整合：PHR（樹脂100部あたり）、wt%（重量％）といった異なる表記が混在
- 知識変数の扱い：NCO（イソシアネート）指数をモデルにどのように組み込み、知識化するか
- 未知成分の予測：既知データを活用して未知の成分を推定し、新規探索に展開する必要性
- 多目的最適化の困難さ：10種類以上の特性を同時に制御するのは従来手法では非効率
成功に導いたアプローチ
MIを支えるPolymerizeのプラットフォームを導入したことで、各社は以下のアプローチを実現しました。
- データ統合と標準化
単位やフォーマットが異なるデータを整理し、解析基盤を整備。
- 知識変数の導入
NCO指数を説明変数として組み込み、化学的知見をモデル化。
- 順解析と逆解析の両立
配合条件から特性値を予測するだけでなく、目標特性から条件を逆算。
- 既知から未知への拡張
既知材料のデータをもとに未知成分を推定し、新規材料探索を効率化。
成功事例に共通する成果
これらのアプローチを通じて、以下の成果が得られました。
- 高精度の予測モデル：95%以上の精度、MAPEは6%未満、R²は0.9以上
- 研究効率化：平均誤差率を削減し、開発サイクルを従来比で大幅に短縮
- 新材料探索の推進：未知成分の予測が可能となり、研究の幅を拡大
- 信頼性の実証：厳格な評価指標をクリアし、再現性と堅牢性を確認
考察と展望
これらの事例に共通する「マテリアルズ・インフォマティクス成功の鍵」は、研究効率化と新材料探索を同時に実現できた点 にあります。
特に、
- データ標準化と知識パラメータ（NCO指数）の導入
- 順解析と逆解析を組み合わせた設計空間探索
- 既知材料から未知材料への知見拡張
が成果の核心でした。
今後は、さらに大規模なデータ連携や自律実験システムとの統合により、MIの成功事例は加速度的に増えていくと考えられます。Polymerizeはその基盤を提供し、材料研究の効率化とイノベーション を同時に支える存在として期待されています。
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