HiPoly is a polymer-native AI framework that uses a three-level hierarchical graph architecture built on the G2RINS representation to process complete polymer descriptions. It encodes stochastic inter-monomer connectivity, composition, and molecular weight directly within its architecture, enabling end-to-end workflows from experimental data to property prediction, generative design, and physics-based validation. The framework achieves state-of-the-art accuracy for thermophysical properties of multi-component polymer systems and demonstrates generative design by discovering sustainable, PFAS-free alternatives with target surface-energy properties.
By Ge Sun, Gervasio Zaldivar, Yuan Tian, Gustavo Perez Lemus, Juhae Park, Dasha Safarian, Ming Han, Juan J. de Pablo
The paper introduces PolyLatentFlow, a continuous‑time flow‑matching framework for polymer generation, and LlamaUni, a multimodal representation that fuses polymer sequences with 3D structural data. In unconditional generation, the combination yields the highest number of valid, novel candidates while preserving diversity, and in conditional settings it systematically shifts property distributions across a 200 °C target range. Across multi‑property tasks, the representation choice affects validity, training‑set replay, and structural proximity, with PolyLatentFlow + LlamaUni achieving the best balance of high validity, low replay, and high target hit yield.
By Tianren Zhang
The paper introduces a chemical language foundation model that uses a SMILES‑based polymer graph representation (CPG) to encode polymer architecture and connectivity, addressing gaps in existing line notations. The model achieves strong performance across 30 polymer property benchmarks and demonstrates robustness to structural representation perturbations, with even chemically invalid SMILES sometimes matching state‑of‑the‑art results. Control experiments and attention analyses confirm that CPG offers meaningful advantages while highlighting the model’s ability to interpolate SMILES sequence space in a way that aligns loosely with chemical and architectural space.
By Nathaniel H. Park, Eduardo Soares, Victor Y. Shirasuna, Tiffany J. Callahan, Sara Capponi, Emilio Vital Brazil
arXiv:2608. 01431v1 Announce Type: cross Abstract: Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer design.
By Charlie Pyle, Adarsh Gadari, C. Adrian Figg, Zhenquan Jia, Yaohang Li, Chunjiang Zhu
Polymer property prediction and inverse generative design targeting desired properties are two crucial tasks in machine learning-assisted polymer design. While the former has received considerable attention, there have been limited methods developed for the latter.
arXiv:2606. 05693v1 Announce Type: new Abstract: Large language models (LLMs) have shown promise for molecular property prediction, but their ability to reason over chemical structures remains limited, as molecular representations such as SMILES differ substantially from the natural language on which LLMs are primarily trained.
By Joey Chan, Wonbin Kweon, Ashley Shin, Niharika Bhattacharjee, Pengcheng Jiang, Yue Guo, Jiawei Han
PolyJarvis is an LLM‑orchestrated platform that automates all‑atom molecular dynamics simulations of amorphous homopolymers. It generates validated run plans, constructs systems with Enhanced Monte Carlo, runs dynamics in LAMMPS, and extracts target properties, all while handling failures within a fixed decision budget. Validation on seven polymers—PE, aPS, sPVC, PLLA, PEG, PEEK, and PSU—showed that 13 of 19 property comparisons met experimental acceptance criteria, though some PCFF‑based systems exhibited density and glass‑transition discrepancies.
By Alexander Zhao, Achuth Chandrasekhar, Amir Barati Farimani
arXiv:2609.38744v1 Announce Type: new
Abstract: Predicting molecular properties for compounds that differ structurally from labeled training molecules is important for drug discovery and materials de...
By Jinmo Lee, Dooho Lee, Minho Jeong, Jaemin Yoo
The study benchmarks 12 deep generative crystal structure prediction models against the template-based TCSP 2.0 on 180 test structures, finding that template retrieval achieves the highest top‑1 success (68.3%). Most generative predictions overlap with template substitutions, and removing entire stoichiometric prototype families from training reduces accuracy by 50‑78%, indicating strong prototype dependence. Only a small subset of predictions remain after such removal, suggesting limited genuine de‑novo capability.
By Lai Wei, Rongzhi Dong, Ying Feng, Madeline Miklos, Jianjun Hu
arXiv:2508. 02641v2 Announce Type: replace-cross Abstract: Molecular crystal structure prediction (CSP) is essential for applications in pharmaceuticals and organic electronics.
By Vahe Gharakhanyan, Yi Yang, Luis Barroso-Luque, Daniel S. Levine, Sushree Jagriti Sahoo, Brandon M. Wood, Kyle Michel, Muhammed Shuaibi, Gregory J. O. Beran, Viachaslau Bernat, Misko Dzamba, Xiang Fu, Meng Gao, Xingyu Liu, Benjamin K. Miller, Keian Noori, Lafe J. Purvis, Tingling Rao, Ammar Rizvi, Matt Uyttendaele, Andrew J. Ouderkirk, Chiara Daraio, C. Lawrence Zitnick, Arman Boromand, Noa Marom, Zachary W. Ulissi, Anuroop Sriram
The article introduces PolyBench26, an open benchmark dataset for polymer property prediction that contains nearly 250,000 datapoints covering eight physical properties from experimental, DFT, and MD sources. It supports four evaluation tasks—property prediction, dataset-size scaling, repeat‑unit complexity, and transfer learning—across homopolymers and various copolymer architectures. The study compares language, graph, and descriptor models, finding graph-based approaches achieve the lowest errors and maintain robustness across training sizes and repeat‑unit complexity.
By Robert W. Learsch, Nicholas Liesen, Daniel S. Levine, Anna M. Hiszpanski, Evan R. Antoniuk
arXiv:2605. 26833v2 Announce Type: replace-cross Abstract: Polymers underpin applications across energy, healthcare, and materials science, yet their vast chemical space makes systematic discovery challenging.
By Yasharth Yadav, Tze Kwang Gerald Er, Atsushi Goto, Kelin Xia