PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
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2026
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| _version_ | 1866914344554463232 |
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| author | Subramanian, Akshay Pan, Elton Nam, Juno Weiler, Maurice Qu, Shuhui Park, Cheol Woo Jaakkola, Tommi S. Olivetti, Elsa Gomez-Bombarelli, Rafael |
| author_facet | Subramanian, Akshay Pan, Elton Nam, Juno Weiler, Maurice Qu, Shuhui Park, Cheol Woo Jaakkola, Tommi S. Olivetti, Elsa Gomez-Bombarelli, Rafael |
| contents | Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because candidate generation is combinatorial and stability is only resolved after costly energy evaluations. Here we introduce PackFlow, a flow matching framework for molecular crystal structure prediction (CSP) that generates heavy-atom crystal proposals by jointly sampling Cartesian coordinates and unit-cell lattice parameters given a molecular graph. This lattice-aware generation interfaces directly with downstream relaxation and lattice-energy ranking, positioning PackFlow as a scalable proposal engine within standard CSP pipelines. To explicitly steer generation toward physically favourable regions, we propose physics alignment, a reinforcement learning post-training stage that uses machine-learned interatomic potential energies and forces as stability proxies. Physics alignment improves physical validity without altering inference-time sampling. We validate PackFlow's performance against heuristic baselines through two distinct evaluations. First, on a broad unseen set of molecular systems, we demonstrate superior candidate generation capability, with proposals exhibiting greater structural similarity to experimental polymorphs. Second, we assess the full end-to-end workflow on two unseen CSP blind-test case studies, including relaxation and lattice-energy analysis. In both settings, PackFlow outperforms heuristics-based methods by concentrating probability mass in low-energy basins, yielding candidates that relax into lower-energy minima and offering a practical route to amortize the relax-and-rank bottleneck. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20140 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment Subramanian, Akshay Pan, Elton Nam, Juno Weiler, Maurice Qu, Shuhui Park, Cheol Woo Jaakkola, Tommi S. Olivetti, Elsa Gomez-Bombarelli, Rafael Chemical Physics Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because candidate generation is combinatorial and stability is only resolved after costly energy evaluations. Here we introduce PackFlow, a flow matching framework for molecular crystal structure prediction (CSP) that generates heavy-atom crystal proposals by jointly sampling Cartesian coordinates and unit-cell lattice parameters given a molecular graph. This lattice-aware generation interfaces directly with downstream relaxation and lattice-energy ranking, positioning PackFlow as a scalable proposal engine within standard CSP pipelines. To explicitly steer generation toward physically favourable regions, we propose physics alignment, a reinforcement learning post-training stage that uses machine-learned interatomic potential energies and forces as stability proxies. Physics alignment improves physical validity without altering inference-time sampling. We validate PackFlow's performance against heuristic baselines through two distinct evaluations. First, on a broad unseen set of molecular systems, we demonstrate superior candidate generation capability, with proposals exhibiting greater structural similarity to experimental polymorphs. Second, we assess the full end-to-end workflow on two unseen CSP blind-test case studies, including relaxation and lattice-energy analysis. In both settings, PackFlow outperforms heuristics-based methods by concentrating probability mass in low-energy basins, yielding candidates that relax into lower-energy minima and offering a practical route to amortize the relax-and-rank bottleneck. |
| title | PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2602.20140 |