{"total":15,"items":[{"citing_arxiv_id":"2606.23214","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-22T12:01:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Universal MLIPs serve as configuration generators whose DFT-relabeled subsamples enable one-shot or iterative training of material-specific MLIPs that recover accurate reactive energy profiles with 600-2000 DFT calculations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19939","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Uncertainty-aware Machine Learning Interatomic Potentials via Learned Functional Perturbations","primary_cat":"cs.CE","submitted_at":"2026-05-19T15:00:06+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":"2605.16612","ref_index":31,"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":"2605.15630","ref_index":64,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building","primary_cat":"physics.chem-ph","submitted_at":"2026-05-15T05:30:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A reweighting method with mean energy-gap approximation transfers PMFs between MLIPs to recover target reaction and activation free energies at low cost for a 601-atom Li+ transport system across DFT levels.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14527","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows","primary_cat":"cs.LG","submitted_at":"2026-05-14T08:10:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Lang2MLIP is an LLM multi-agent framework that automates end-to-end development of machine learning interatomic potentials from natural language input for heterogeneous materials systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13788","ref_index":36,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs","primary_cat":"cs.LG","submitted_at":"2026-05-13T17:08:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.11610","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-12T06:42:00+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"Chen J, Bystrom K, Dylla M, Chard K, Asta M, Persson K A, Snyder G J, Foster I and Jain A 2018 Computational Materials Science15260-69 ISSN 0927-0256 URL http://dx.doi.org/10.1016/j.commatsci.2018.05.018 [38] Neumann M, Gin J, Rhodes B, Bennett S, Li Z, Choubisa H, Hussey A and Godwin J 2024 Orb: A fast, scalable neural network potential (Preprint2410.22570) URL https://arxiv.org/abs/2410.22570 [39] Rhodes B, Vandenhaute S, ˇSimkus V, Gin J, Godwin J, Duignan T and Neumann M 2025 Orb-v3: atomistic simulation at scale (Preprint2504.06231) URLhttps: //arxiv.org/abs/2504.06231 [40] Kim S Y, Park Y J and Li J 2025 Leveraging neural network interatomic potentials for a foundation model of chemistry URLhttps://arxiv.org/abs/2506.18497 [41] Zhang C, Zhang D, Peng A, Guo M, Zhang Y, Wang L, Ke G, Zhang L, Li"},{"citing_arxiv_id":"2605.08960","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CrystalREPA: Transferring Physical Priors from Universal MLIPs to Crystal Generative Models","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-09T13:56:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CrystalREPA closes the representation gap between crystal generators and universal MLIPs via contrastive alignment, yielding more stable and valid generated crystals while revealing that MLIP teacher quality is better predicted by representation distinguishability than by leaderboard accuracy.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Kavanagh, Laura Zichi, Menghang Wang, Aadit Saluja, Yizhong R Hu, Tess Smidt, et al. High-performance training and inference for deep equivariant interatomic potentials.Digital Discovery, 2026. [34] Yury Lysogorskiy, Anton Bochkarev, and Ralf Drautz. Graph atomic cluster expansion for foundational machine learning interatomic potentials.npj Computational Materials, 2026. [35] Benjamin Rhodes, Sander Vandenhaute, Vaidotas Šimkus, James Gin, Jonathan Godwin, Tim Duignan, and Mark Neumann. Orb-v3: atomistic simulation at scale, 2025. URL https: //arxiv.org/abs/2504.06231. [36] Anton Bochkarev, Yury Lysogorskiy, and Ralf Drautz. Graph atomic cluster expansion for semilocal interactions beyond equivariant message passing.Physical Review X, 14(2):021036,"},{"citing_arxiv_id":"2605.08885","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning","primary_cat":"cs.LG","submitted_at":"2026-05-09T11:07:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Structural pruning of SO(3) equivariant atomistic models from large checkpoints yields 1.5-4x fewer parameters and 2.5-4x less pre-training compute than small models trained from scratch, while outperforming them on most Matbench Discovery metrics and downstream tasks.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"neural network interatomic potentials in molecular dynamics simulations.J. Chem. Theory Comput., 20 (11):4857-4868, 2024. doi: 10.1021/acs.jctc.4c00190. [4] Brandon M Wood, Misko Dzamba, Xiang Fu, Meng Gao, Muhammed Shuaibi, Luis Barroso-Luque, Kareem Abdelmaqsoud, Vahe Gharakhanyan, John R Kitchin, Daniel S Levine, et al. Uma: A family of universal models for atoms.arXiv preprint arXiv:2506.23971, 2025. [5] Benjamin Rhodes, Sander Vandenhaute, Vaidotas Šimkus, James Gin, Jonathan Godwin, Tim Duignan, and Mark Neumann. Orb-v3: atomistic simulation at scale.arXiv preprint arXiv:2504.06231, 2025. [6] Arslan Mazitov, Filippo Bigi, Matthias Kellner, Paolo Pegolo, Davide Tisi, Guillaume Fraux, Sergey Pozdnyakov, Philip Loche, and Michele Ceriotti. Pet-mad as a lightweight universal interatomic potential"},{"citing_arxiv_id":"2605.08262","ref_index":86,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SLayerGen: a Crystal Generative Model for all Space and Layer Groups","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-07T23:30:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"SLayerGen generates crystals invariant to any space or layer group via autoregressive lattice and Wyckoff sampling plus equivariant diffusion, achieving gains over bulk models on diperiodic materials after correcting a prior loss inconsistency for hexagonal groups.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"diperiodic crystals are invariant to one of 80 discretelayer groups(see Sec. 2 for definitions). Diperiodic systems offer large surface to mass ratios, frequently yield exotic physics, and are highly tunable via interlayer stacking, twisting, and sliding. Notably, they have been shown to exhibit symmetry-dependent behaviors such as valley-contrasting physics [86], nonlinear optics [4], superconductivity [84], and topological properties [23], offering potential to unlock new paradigms for flexible electronics, quantum computing, batteries, and more [73]. The layer group symmetries ∗Correspondence toreeswc2@illinois.edu Preprint. arXiv:2605.08262v1 [cond-mat.mtrl-sci] 7 May 2026 inherent in these systems have been used to theoretically identify bilayer ferroelectrics [46], detect"},{"citing_arxiv_id":"2605.03964","ref_index":32,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs","primary_cat":"cs.LG","submitted_at":"2026-05-05T16:48:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03205","ref_index":118,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-04T22:48:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"Hackathon submissions indicate LLMs are moving from general assistants toward composable multi-agent systems for structuring scientific knowledge and automating tasks in materials science and chemistry.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"More extensive benchmarking is required to fully evaluate the approach, but the initial results presented here are promising. Future Work Future work includes exploring larger LLMs, expanding the training data with additional datasets such as NOMAD [116], and performing systematic comparisons with state-of-the-art graph neural network methods used in materials science [117, 118]. 41 Tokenizer Transformer Modules T5 - text to text transformer Input CIF file Output CIF file Figure 26: Overview of the relaxation training pipeline. The input structure from the LeMatTraj dataset is transformed to a CIF file using ASE. It is then fed into the T5 tokenizer and the fine-tuned transformer block layers to output a relaxed structure CIF file."},{"citing_arxiv_id":"2604.10887","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-04-13T01:32:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SevenNet-Nano is a lightweight universal ML interatomic potential distilled from a larger multi-task foundation model, delivering high accuracy, transferability, and over 10x computational speedup for scalable atomistic simulations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.16331","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations","primary_cat":"physics.chem-ph","submitted_at":"2026-01-22T21:32:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Benchmarks of 15 MLIPs show parameter count and training set size correlate with accuracy, architecture drives speed and memory, and explicit Coulomb terms provide no benefit.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.05717","ref_index":65,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Comparing the latent features of universal machine-learning interatomic potentials","primary_cat":"physics.chem-ph","submitted_at":"2025-12-05T13:45:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Different uMLIPs encode chemical space in distinct ways, with high cross-model feature reconstruction errors, and fine-tuning preserves strong pre-training bias in the latent features.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}