Traditional molecular generation benchmarks like PMO include unrealistic proxy oracles that are frequently exploited by generative models. In contrast, the NMO Benchmark replaces arbitrary proxy oracles with rigorous quantum simulations to calculate real molecular properties. This ensures that models have to learn actual physics and can't simply exploit the benchmark's rules (as is common in popular generative methods).</p>\n<p>Because the NMO oracles represent real scientific tasks, it bridges the gap between Machine Learning and Materials Science, enabling ML researchers to enter the field and have real-world impact.</p>\n","updatedAt":"2026-06-30T08:25:39.904Z","author":{"_id":"6888b31e795863924e46d249","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jpCbNuOAKsXjlT5ZpoNIY.png","fullname":"Daniel Kienzle","name":"KieDani","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":1,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8942403793334961},"editors":["KieDani"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jpCbNuOAKsXjlT5ZpoNIY.png"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.30170","authors":[{"_id":"6a437702763f63ca3757eb63","name":"Matthias Blaschke","hidden":false},{"_id":"6a437702763f63ca3757eb64","name":"Daniel Kienzle","hidden":false},{"_id":"6a437702763f63ca3757eb65","name":"Zsuzsanna Koczor-Benda","hidden":false},{"_id":"6a437702763f63ca3757eb66","name":"Julian Lorenz","hidden":false},{"_id":"6a437702763f63ca3757eb67","name":"Rainer Lienhart","hidden":false},{"_id":"6a437702763f63ca3757eb68","name":"Fabian Pauly","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/6888b31e795863924e46d249/aItSM-sX5gEyNHoAkDbMt.png","https://cdn-uploads.huggingface.co/production/uploads/6888b31e795863924e46d249/dMXh828mKw1Yzkb0PNHJW.png","https://cdn-uploads.huggingface.co/production/uploads/6888b31e795863924e46d249/HmjNPxRlsbokFbDBMfZMy.png"],"publishedAt":"2026-06-29T00:00:00.000Z","submittedOnDailyAt":"2026-06-30T00:00:00.000Z","title":"Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark","submittedOnDailyBy":{"_id":"6888b31e795863924e46d249","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jpCbNuOAKsXjlT5ZpoNIY.png","isPro":false,"fullname":"Daniel Kienzle","user":"KieDani","type":"user","name":"KieDani"},"summary":"Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets. This combination yields strong benchmark metrics but limits transferability to domains structurally distinct from drug discovery. To overcome this limitation and drive discovery toward real, scientifically grounded targets, we introduce the Nanotechnology Molecular Optimization (NMO) Benchmark, which bridges machine learning (ML) and quantum materials science. NMO acts simultaneously as a rigorous testbed for the ML community and a discovery engine for nanotechnology research. The suite replaces proxy oracles with quantum simulations and introduces strict protocols that prioritize scientific utility over leaderboard-oriented overfitting. The physics-based NMO tasks impose hard structural constraints and rugged fitness landscapes, posing fundamentally new requirements on generative models. Notably, advanced molecular optimization methods underperform much simpler approaches on the NMO tasks. We develop a new baseline method identifying the critical components to solve the NMO tasks, including a novel representation for modeling structural constraints and a domain-agnostic pretraining strategy to eliminate pharmaceutical dataset bias. Our results surpass state-of-the-art physical properties and reveal previously unknown structural motifs, offering new insights for the nanotechnology community and demonstrating that ML can drive genuine scientific discovery.","upvotes":3,"discussionId":"6a437702763f63ca3757eb69","githubRepo":"https://github.com/blaschma/TheNanotechnologyMolecularOptimizationBenchmark","githubRepoAddedBy":"user","ai_summary":"The Nanotechnology Molecular Optimization (NMO) Benchmark introduces physics-based molecular design challenges that require new generative model approaches, moving beyond drug-discovery-focused metrics to enable scientific discovery in nanotechnology.","ai_keywords":["Nanotechnology Molecular Optimization","generative models","molecular optimization","quantum simulations","structural constraints","fitness landscapes","domain-agnostic pretraining","pharmaceutical dataset bias"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":2,"organization":{"_id":"68a43e693f883647998a0a57","name":"MLCVLab","fullname":"Chair for Machine Learning & Computer Vision ","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/6888b31e795863924e46d249/N5eV3SbCRFQMfH4PpPZ7E.png"}},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"6888b31e795863924e46d249","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/jpCbNuOAKsXjlT5ZpoNIY.png","isPro":false,"fullname":"Daniel Kienzle","user":"KieDani","type":"user"},{"_id":"6a32971a65203da85439c4b7","avatarUrl":"/avatars/cc7a25c84be7cd08859a3dd74695e42a.svg","isPro":false,"fullname":"Matthias Blaschke","user":"blaschma","type":"user"},{"_id":"6270324ebecab9e2dcf245de","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/6270324ebecab9e2dcf245de/cMbtWSasyNlYc9hvsEEzt.jpeg","isPro":false,"fullname":"Kye Gomez","user":"kye","type":"user"}],"acceptLanguages":["en"],"dailyPaperRank":0,"organization":{"_id":"68a43e693f883647998a0a57","name":"MLCVLab","fullname":"Chair for Machine Learning & Computer Vision ","avatar":"https://cdn-avatars.huggingface.co/v1/production/uploads/6888b31e795863924e46d249/N5eV3SbCRFQMfH4PpPZ7E.png"},"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2606/2606.30170.md","query":{}}">
Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark
Abstract
The Nanotechnology Molecular Optimization (NMO) Benchmark introduces physics-based molecular design challenges that require new generative model approaches, moving beyond drug-discovery-focused metrics to enable scientific discovery in nanotechnology.
Generative molecular design is shaped by simple proxy benchmarks for drug-like properties and models pretrained on large pharmaceutical datasets. This combination yields strong benchmark metrics but limits transferability to domains structurally distinct from drug discovery. To overcome this limitation and drive discovery toward real, scientifically grounded targets, we introduce the Nanotechnology Molecular Optimization (NMO) Benchmark, which bridges machine learning (ML) and quantum materials science. NMO acts simultaneously as a rigorous testbed for the ML community and a discovery engine for nanotechnology research. The suite replaces proxy oracles with quantum simulations and introduces strict protocols that prioritize scientific utility over leaderboard-oriented overfitting. The physics-based NMO tasks impose hard structural constraints and rugged fitness landscapes, posing fundamentally new requirements on generative models. Notably, advanced molecular optimization methods underperform much simpler approaches on the NMO tasks. We develop a new baseline method identifying the critical components to solve the NMO tasks, including a novel representation for modeling structural constraints and a domain-agnostic pretraining strategy to eliminate pharmaceutical dataset bias. Our results surpass state-of-the-art physical properties and reveal previously unknown structural motifs, offering new insights for the nanotechnology community and demonstrating that ML can drive genuine scientific discovery.
Community
Traditional molecular generation benchmarks like PMO include unrealistic proxy oracles that are frequently exploited by generative models. In contrast, the NMO Benchmark replaces arbitrary proxy oracles with rigorous quantum simulations to calculate real molecular properties. This ensures that models have to learn actual physics and can't simply exploit the benchmark's rules (as is common in popular generative methods).
Because the NMO oracles represent real scientific tasks, it bridges the gap between Machine Learning and Materials Science, enabling ML researchers to enter the field and have real-world impact.
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Cite arxiv.org/abs/2606.30170 in a model README.md to link it from this page.
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