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NeurIPS 2026ShanghaiTechCATLsmartlogictechContemporary Amperex Technology East China NormalMaterials & chemistry

SolvMix: Learning Formulation-State Landscapes for Liquid Electrolyte Conductivity Prediction

Kexin Zhang, Minzhang Li, Guotao Qiu, Tianqi Zhao and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026StanfordU California, BerkeleyUC BerkeleyMaterials & chemistry

CME–SpectrumBench: Can LLMs Analyze Condensed Matter Spectral Data?

Jin Gene Wong, Anjney Midha, Joseph Tennyson, Wei-Lin Chiang and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Mohamed bin Zayed University of The University of SydneyHKBU / RIKENJilinHong Kong PolytechnicMaterials & chemistry

MaterialsPilot: An Execution-Feedback Framework for Generative Design of Complex Atomistic Architectures

Qiaolin Lu, Tongliang Liu, Qiang Qu, Bo Han and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026The University of SydneyHarbin Institute of Technology SU Sydney, University of SydneyHKBU / RIKENMohamed bin Zayed University of Materials & chemistry

Symmetry-Guaranteed Prediction of High-Order Tensor Properties for Crystalline Materials via Irreducible Decomposition

Qiaolin Lu, Qiang Qu, Hao Jiang, Aoni Xu and 5 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Universidad de Los AndesUniversidad de los AndesMaterials & chemistry

Quantitative Assessment of Crystal Structure Prediction

Sergio Rincón, Gabriel González, Nicolás Andrade, Rafael Velasquez and 5 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026中国科学院大学Renmin University of ChinaU the Chinese Academy of ScienceTsinghuaChinese Academy of SciencesMaterials & chemistry

EP-Flow: Disordered Crystal Structure Prediction without Site-level Annotation

liu qiuliang, Liming Wu, Qi Li, Zhonglong Peng and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026U Texas at AustinThe University of Texas at AustiMaterials & chemistry

MaterialsSaddles: 34 Million Transition States and a Flow-Matching Saddle-Point Predictor for Materials

Ilgar Baghishov, Sung Hoon Jung, Graeme Henkelman

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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NeurIPS 2026KAISTKorea Research Institute of ChemGenentechKorea Advanced Institute of ScieMaterials & chemistry

A Structured LLM Framework for Inorganic Material Synthesis Planning

Heewoong Noh, Gyoung S. Na, Namkyeong Lee, Chanyoung Park

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Kepler ComputingKEPLER COMPUTINGKepler Computing, Inc.Kepler computingKepler Computing Inc.Materials & chemistry

Ferrogen: Generative Pipeline for Guided Search of Novel Ferroelectric Material for Logic and Memory

Yuan Sheng Fang, Dmitri E Nikonov, Ikenna Odinaka, Alan Kalitsov and 12 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026BeihangTianjinZhejiangBeihangMaterials & chemistry

AET-Bench: Pixel-Accurate Atomic Tomography Is Not Atom-Accurate

Zicheng Liu, Wenzhuo Ma, Jintao Chen, Di Huang

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026, Chinese Academy of SciencesInstitute of Computing TechnologU Chinese Academy of ScienceInsititute of Computing TechnoloInstitute of Computing, Chinese Materials & chemistry

Flux: Online, Fine-Grained Data Scheduling for Training Machine Learning Interatomic Potentials

Yuanchang Zhou, Chen Wang, Hongtao Xu, Mingzhen Li and 2 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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NeurIPS 2026Zhongguancun AcademyShanghai JiaotongShanghai Jiao TongMaterials & chemistry

RECIPE: Learning to Rank Complete Precursor Sets for Inorganic Retrosynthesis

Jing Gao, Kaipeng Zeng, Fuyuan Xia, Jian Ma and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026KAISTKorea Advanced Institute of ScieKorea Advanced Institute of ScieMaterials & chemistry

AtomMOF: All-Atom Flow Matching for MOF-Adsorbate Structure Prediction

AtomMOF uses all-atom flow matching to predict MOF and adsorbate structures directly from 2D graphs, improving match rates and sampling efficiency.

Nayoung Kim, Honghui Kim, Sihyun Yu, Minkyu Kim and 2 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · Code ★ 19

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AI panel: 14 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 1/5
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NeurIPS 2026Massachusetts Institute of TechnU TorontoMITMaterials & chemistry

Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching

Clari predicts organic crystal structures via unit-cell flow matching with pure pair-bias attention, cutting generation to seconds while surpassing OXtal solve rates and supporting non-sanitizable inputs.

Alston Lo, Luka Mucko, Austin Cheng, Andy Cai and 3 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 2 on Hugging Face

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5
89%Must read
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NeurIPS 2026U AugsburgThe University of WarwickUniversität AugsburgMaterials & chemistry

Beyond Drug Discovery: The Nanotechnology Molecular Optimization (NMO) Benchmark

The Nanotechnology Molecular Optimization benchmark replaces proxy drug metrics with quantum simulations for nanomaterials, showing simple methods outperform advanced ones and revealing new structural motifs.

Matthias Blaschke, Daniel Kienzle, Zsuzsanna Koczor-Benda, Julian Lorenz and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 5 on Hugging Face · Code ★ 2

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 3/5
71%Highly rated
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NeurIPS 2026UC BerkeleyMetaUC Berkeley & AmazonMaterials & chemistry

Offline Materials Optimization with CliqueFlowmer

CliqueFlowmer fuses clique-based offline model-based optimization into flow transformers for materials discovery, generating materials that strongly outperform generative baselines.

Jakub Grudzien Kuba, Benjamin K Miller, Sergey Levine, Pieter Abbeel

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 1 on Hugging Face · Code ★ 17

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 4/5
medium 3/10
strict 0/5
74%Highly rated
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NeurIPS 2026Institute of automation, ChineseThe Hong Kong University of ScieSaw Swee Hock School of Public HInstitute of Automation, ChineseMaterials & chemistry

XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction

XDecomposer learns prior-free multiphase X-ray diffraction decomposition as set prediction to identify constituent phases and proportions without candidate lists. It improves reconstruction accuracy and phase identification across simulated and experimental datasets.

Hanyu Gao, Bin Cao, YUNYUE SU, Tong-yi Zhang and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 9 of 20 reviewers recommend it
lenient 4/5
medium 5/10
strict 0/5
72%Highly rated
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NeurIPS 2026SichuanU the Chinese Academy of SciencePeking, Chinese Academy of SciencesInstitute for AI Industry ResearMaterials & chemistry

AtomWorld-Mem: Memory-Restored World States for Long-Horizon Atomistic Evolution

AtomWorld-Mem restores latent hidden dynamical states from atomistic snapshots via multi-scale memory to improve long-horizon kinetic Monte Carlo evolution and transfer across unseen alloys.

Tian Luo, Ruge Zhang, Haozhi Han, Yifeng Chen and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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AI panel: 8 of 20 reviewers recommend it
lenient 3/5
medium 5/10
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71%Highly rated
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NeurIPS 2026Helmholtz Centre HereonHelmholtz Zentrum HereonHelmholtz-Zentrum HereonUniversität des SaarlandesMaterials & chemistry

Co-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design

Co-PiLOT combines generative latent optimization with physics-informed black-box search to inverse-design magnesium alloy microstructures, cutting relative target error by 3, 22% over seven baselines within 160 simulations.

Mahish Kumar Guru, Mayank Nagar, Ayush vyas, Jan Bohlen and 2 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 6 of 20 reviewers recommend it
lenient 4/5
medium 2/10
strict 0/5