{"total":18,"items":[{"citing_arxiv_id":"2606.29220","ref_index":62,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Latent Genetic Algorithm for Crystal Structure Prediction","primary_cat":"physics.comp-ph","submitted_at":"2026-06-28T06:07:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"LGA employs latent-space interpolation from universal interatomic potentials for crossover in crystal structure prediction, raising HfO2 ground-state recovery to 60-95% and identifying unreported periodic structures in perovskite superlattices.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.28578","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-26T20:10:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A Gaussian process surrogate gate inserted between generative crystal models and property oracles matches or exceeds ungated fine-tuning while using roughly one-fifth the oracle calls for heat capacity and bulk modulus.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07712","ref_index":47,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-05T14:11:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MatMind is a unified LLM-based generative model for crystals that reports lowest MAE on energy above hull, bulk modulus and band gap while achieving 65.3% S.U.N. rate on unconditional generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02507","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-01T17:20:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":1.0,"formal_verification":"none","one_line_summary":"A survey of generative crystal modeling, multimodal learning, and closed-loop inverse design pipelines for crystalline solids, including failure modes and evaluation practices.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00776","ref_index":86,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Latent Diffusion Pretraining for Crystal Property Prediction","primary_cat":"cs.LG","submitted_at":"2026-05-30T15:44:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CrysLDNet combines VAE and latent diffusion pretraining on unlabeled crystals to improve graph encoder performance on property prediction by about 4-5% on JARVIS and MP datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22141","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Theory-Guided, Machine-Learning-Accelerated Discovery of a 3D Carbon Nested Nodal-Surface Semimetal","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-21T08:14:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Discovery via symmetry-guided ML of Netsene (bct-C24), a dynamically stable carbon allotrope exhibiting nested nodal-surface semimetal behavior with Dirac-like crossings and drumhead surface states.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16612","ref_index":41,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation","primary_cat":"cs.AI","submitted_at":"2026-05-15T20:27:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"PRISMat generates crystal slabs with mean absolute errors of 0.188 eV/A² for cleavage energy and 2.79 eV for work function, reducing error by 4× versus the next best model while using less inference time.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16214","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bridging Atomistic Simulation and Experimental Processing Timescales with Goal-Directed Deep Reinforcement Learning","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-15T17:28:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"An E(3)-equivariant deep RL framework lets an O2 agent discover kinetically plausible diffusion and dissociation pathways in disordered Si/a-SiO2 without hand-crafted reaction coordinates or collective variables.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14769","ref_index":36,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Composable Crystals: Controllable Materials Discovery via Concept Learning","primary_cat":"cs.LG","submitted_at":"2026-05-14T12:36:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"VQ-VAE concept learning enables controllable recombination of crystal motifs to generate structures with reported gains in validity-stability-uniqueness-novelty metrics on MP-20 and Alex-MP-20.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14759","ref_index":51,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement","primary_cat":"cs.LG","submitted_at":"2026-05-14T12:23:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Crys-JEPA introduces a joint embedding predictive architecture that creates an energy-aware latent space, enabling embedding-based stability screening and a refinement pipeline that yields up to 72.7% gains on the V.S.U.N. metric for crystal generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14344","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CrystalReasoner: Reasoning and RL for Property-Conditioned Crystal Structure Generation","primary_cat":"cs.AI","submitted_at":"2026-05-14T04:08:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CrystalReasoner combines LLM reasoning traces with physical priors and multi-objective RL to generate valid, stable, and property-conditioned crystal structures.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27879","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Generation of magnetic metal-organic frameworks","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-04-30T13:56:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Fine-tuning CHGNet on OMDB data and performing site substitution on QMOF prototypes yields novel highly magnetic MOFs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13520","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LEGO-MOF: Equivariant Latent Manipulation for Editable, Generative, and Optimizable MOF Design","primary_cat":"cs.LG","submitted_at":"2026-04-15T06:06:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LEGO-MOF maps MOF linkers to an equivariant latent space for continuous editing and uses test-time optimization to achieve a 147.5% average boost in pure CO2 uptake while preserving structural validity.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"generative models have recently emerged as powerful tools. For small molecules, diffu- sion models have achieved remarkable success in capturing both spatial topologies and chemical information [15-17]. Extending these methods to periodic crystals introduces additional complexities, pioneered by models integrating Variational Autoencoders (VAEs) with diffusion [18] and large-scale property-guided frameworks [19]. To address the uniquely large hierarchical architectures of MOFs, recent models have adopted coarse-grained or fragment-based strategies to generate skeletal arrangements [20, 21], utilized Riemannian flow matching for rigid-body assembly [22], or developed fully all-atom frameworks [23, 24] and agentic LLM-driven pipelines [25, 26]."},{"citing_arxiv_id":"2603.06082","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Offline Materials Optimization with CliqueFlowmer","primary_cat":"cs.AI","submitted_at":"2026-03-06T09:33:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CliqueFlowmer combines clique-based model-based optimization with transformer and flow models to generate materials that optimize target properties better than generative baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.20210","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling","primary_cat":"cs.LG","submitted_at":"2026-02-23T03:59:47+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.16307","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design","primary_cat":"cs.LG","submitted_at":"2025-07-22T07:48:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A fine-tuned LLM called Perovskite-R1, built from curated perovskite literature and material libraries, proposes precursor additives and designs with some experimental validation showing improved stability and performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.13048","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Design Topological Materials by Reinforcement Fine-Tuned Generative Model","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-04-17T16:05:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Reinforcement fine-tuning of a generative model produces new topological insulators and crystalline insulators, exemplified by Ge2Bi2O6 with a 0.26 eV full band gap.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2405.04967","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2024-05-08T11:13:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"com/janosh/pymatviz [38] M. Horton. Add strict anions option to MaterialsProject2020Compatibility by mkhorton (2024). Accessed May 07, 2024 [39] C. Zeni, R. Pinsler, D. Z¨ ugner, A. Fowler, M. Horton, X. Fu, S. Shysheya, J. Crabb' e, L. Sun, J. Smith, et al., Mattergen: a generative model for inorganic materials design. arXiv preprint arXiv:2312.03687 (2023) [40] T. Xie, X. Fu, O.E. Ganea, R. Barzilay, T. Jaakkola, Crystal diffusion variational autoencoder for periodic material generation. arXiv preprint arXiv:2110.06197 (2021) [41] C.J. Pickard, R. Needs, Ab initio random structure searching. Journal of Physics: Condensed Matter 23(5), 053201 (2011) [42] J. Schmidt, H.C. Wang, T.F. Cerqueira, S. Botti, M."}],"limit":50,"offset":0}