{"total":32,"items":[{"citing_arxiv_id":"2606.23214","ref_index":105,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"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":"2606.21632","ref_index":36,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Fine-Tuning a Universal Machine-Learned Interatomic Potential for Oxygen Plasma Interactions with WS$_2$","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-19T17:36:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Pretrained UMA model reproduces chemisorbed S and O coverage under 15 eV O+ and O2+ bombardment on WS2 without fine-tuning; fine-tuning lowers energy MAE to 4.5e-3 eV/atom and force MAE to 0.076 eV/Å.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07327","ref_index":63,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Six Open Questions in Machine-Learned Interatomic Potential Foundation Models","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-06-05T14:45:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"This perspective article develops a definition of foundational MLIPs and poses six open questions that the authors believe will define future research in machine-learned interatomic potentials.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.28395","ref_index":38,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Can MACE Potentials Accurately Describe Magnetism and Phase Stability in Fe-Ni Alloys? A Systematic Benchmark","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-27T12:32:20+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A system-specific MACE-sqs model trained on spin-polarized PBE DFT data for Fe-Ni SQS structures outperforms foundation models for equations of state, volumes, elastic constants and thermal expansion but all models incorrectly increase bcc-to-hcp transition pressure with Ni content.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19124","ref_index":267,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-18T21:22:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A review of classical and AI-assisted methods for modeling chemical disorder in atomistic simulations of alloys and complex materials.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18404","ref_index":2,"ref_count":2,"confidence":0.98,"is_internal_anchor":true,"paper_title":"JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials","primary_cat":"cs.DC","submitted_at":"2026-05-18T13:45:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"JanusPipe introduces SymFold and WaveK to enable efficient 3D-parallel training for conservative MLIPs, reporting 1.51x and 1.45x average throughput gains over 1F1B and Hanayo baselines on 32 GPUs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18893","ref_index":18,"ref_count":2,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence","primary_cat":"cs.LG","submitted_at":"2026-05-17T07:08:22+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"The paper claims current graph condensation approaches are flawed due to full-dataset training requirements, high overhead, poor generalization, and misleading evaluation metrics, calling for a reset toward lightweight and architecture-agnostic methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14527","ref_index":79,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"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":1,"top_context_role":"background","top_context_polarity":"background","context_text":"EvalReferenceAgent:Completed evaluation on 10 validation structures, all successful. Each struc- ture was simulated with NPT MD at 300 K, generating 500 snapshots. The complete report is available at eval reference/report.txt. Key physical quantity ranges (density, energy per atom, minimum interatomic distance) are summarized below: -graphite xlarge 300K:ρ= [2.23,2.23]g/cm 3,E/at = [−7.79,−7.78]eV ,d min = 1.32 ˚A -Li2CO3 amorphous large 300K:ρ= [1.67,1.77]g/cm 3,E/at = [−5.12,−5.11]eV ,d min = 1.12 ˚A -LEDC amorphous medium low density 300K: ρ= [1.15,1.39] g/cm3, E/at = [−4.90,−4.89] eV , dmin = 0.95 ˚A -EC DMC liquid medium seed2 300K: ρ= [0.56,0.88] g/cm3, E/at = [−4.54,−4.53] eV ,dmin = 0.99 ˚A -LiPF6 EC DMC electrolyte large 300K: ρ= [0."},{"citing_arxiv_id":"2605.13594","ref_index":65,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Assessing foundational atomistic models for iron alloys under Earth's core conditions","primary_cat":"physics.geo-ph","submitted_at":"2026-05-13T14:30:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Foundational atomistic models reproduce some structural and dynamical properties of iron alloys under core conditions but none consistently match first-principles benchmarks due to missing explicit treatment of thermal electronic excitations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09394","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis","primary_cat":"physics.chem-ph","submitted_at":"2026-05-10T07:43:42+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Fine-tuned MACE MLIPs achieve lower mean absolute errors on catalytic reaction energies and barriers than from-scratch models, with a large fine-tuned model performing best on both metallic and oxide systems including out-of-distribution cases.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"and quality, which may restrict their generalizability when determining bothE r and Ea Wood, et al. [15] have used a mixture of experts model to train a family of universal models (UMA) on a combined dataset comprised of OC22 (oxide electrocatalysts) [16], OC25 (solid-liquid interfaces) [17], OMOL (molecules) [18], OpenDac [19] and OMAT (bulk inorganic materials)[20]and is able to estimateE r within 0.1 eV of DFT, for 74 % ofE r reported in AdsorbML task. In another effort, Batatia, et al. pretrained a base model to OMAT dataset [20], then fine-tuned it using a multi-head replay with 10% of the OMAT to avoid catastrophic forgetting, and 6 other heads that consists of SPICE [21], RGD1 [22], Material project [23], OC20 [14], OMOL [18], and MATPES"},{"citing_arxiv_id":"2605.08960","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"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":"dataset","top_context_polarity":"use_dataset","context_text":"advances, most crystal generative models learn the geometry of stable crystals without supervision from the potential energy surface that governs stability. Universal MLIPs.Universal MLIPs have recently achieved robust generalization across broad chemical spaces by training on large-scale atomistic datasets such as MPtrj [23], Alexandria [24, 25], and OMat24 [26] with explicit energy and force supervision [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43]. Beyond their use for accurate energy prediction and molecular dynamics, pretrained MLIPs have been shown to encode atomistic representations sensitive to variations in the potential energy surface [42, 44]. However, how to exploit these pretrained representations as"},{"citing_arxiv_id":"2605.08885","ref_index":32,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"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":"Nature Machine Intelligence, 5(9):1031-1041, 2023. [31] Peter Eastman, Pavan Kumar Behara, David L Dotson, Raimondas Galvelis, John E Herr, Josh T Horton, Yuezhi Mao, John D Chodera, Benjamin P Pritchard, Yuanqing Wang, et al. Spice, a dataset of drug-like molecules and peptides for training machine learning potentials.Scientific Data, 10(1):11, 2023. [32] Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C Lawrence Zitnick, and Zachary W Ulissi. Open materials 2024 (omat24) inorganic materials dataset and models.arXiv preprint arXiv:2410.12771, 2024. [33] Daniel S Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G Taylor, Muhammad R"},{"citing_arxiv_id":"2605.07927","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"MatterSim-MT: A multi-task foundation model for in silico materials characterization","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-08T16:01:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MatterSim-MT is a multi-task ML foundation model pretrained on 35M+ structures for in silico materials property prediction and complex simulations.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Nature Materials24, 1522-1532 (2025). https://doi.org/10.1038/ s41563-025-02272-0 [18] T. Cavignac, J. Schmidt, P.P. De Breuck, A. Loew, T.F.T. Cerqueira, H.C. Wang, A. Bochkarev, Y. Lysogorskiy, A.H. Romero, R. Drautz, S. Botti, M.A.L. Marques, Ai-driven expansion and application of the alexandria database. arXiv preprint arXiv:2512.09169 (2025) [19] C. Zeni, R. Pinsler, D. Zügner, A. Fowler, M. Horton, X. Fu, S. Shysheya, J. Crabbé, L. Sun, J. Smith, et al., A generative model for inorganic materials design. Nature (2025). https: //doi.org/10.1038/s41586-025-08628-5 [20] S. Yang, K. Cho, A. Merchant, P. Abbeel, D. Schuurmans, I. Mordatch, E.D. Cubuk, Scalable diffusion for materials generation."},{"citing_arxiv_id":"2605.08262","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"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":"other","top_context_polarity":"unclear","context_text":"Advanced Materials, 30(24):1705963, 2018. doi: https://doi.org/10.1002/adma.201705963. URL https://advanced.onlinelibrary. wiley.com/doi/abs/10.1002/adma.201705963. [5] Woohyeon Baek, Sambit Das, Shibo Tan, Vikram Gavini, and Wenhao Sun. Quasicrystal stability and nucleation kinetics from density functional theory.Nature Physics, 21(6):980-987, June 2025. [6] Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M. Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C. Lawrence Zitnick, and Zachary W. Ulissi. Open materials 2024 (omat24) inorganic materials dataset and models, 2024. URLhttps://arxiv.org/abs/2410.12771. [7] Erik Bitzek, Pekka Koskinen, Franz Gähler, Michael Moseler, and Peter Gumbsch. Structural"},{"citing_arxiv_id":"2605.05733","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Density diversity in training data governs thermodynamic transferability of machine learning interatomic potentials","primary_cat":"physics.chem-ph","submitted_at":"2026-05-07T06:24:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Density diversity in training data is the key factor for making machine learning interatomic potentials transferable across thermodynamic states, outperforming temperature diversity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27685","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-04-30T10:20:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VibroML automates remediation of dynamic instabilities in crystalline materials by combining MLIPs with genetic algorithms for polymorph search, finite-temperature MD validation, and compositional alloying to yield stable structures from databases like Alexandria.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.24607","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments","primary_cat":"physics.chem-ph","submitted_at":"2026-04-27T15:32:37+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PET-UAFD ensemble of ML potentials, calibrated on experimental cohesive energies and moduli, matches experimental accuracy on liquid properties and supplies uncertainty estimates via the PET-EXP protocol.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.23758","ref_index":89,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Agentic Fusion of Large Atomic and Language Models to Accelerate Superconductor Discovery","primary_cat":"cs.LG","submitted_at":"2026-04-26T15:14:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An agentic framework fusing large atomic and language models rediscovers 66 known superconductors and guides experimental verification of four new ones with transition temperatures from 2.5 K to 6.5 K.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"5PrH 9 [76] 20.72 9P6 3/mmc 38B 3Ru7 [77] 6.82 3.0Cmc2 1 6VRu[78] 19 4.2P m 3m 39Rb 2Cr3As3 [79] 6.8 4.8Amm2 7MgAlB 4 [80] 18.14 3P6/mmm 40TaBRu[81] 6.754 4.0P bam 8MoN[82] 17.5 12.1P3m1 41AlSb[83] 6.69 2.8F mmm 9ZrP[84] 16.19 4.5F m 3m 42Re 2W3C[85] 6.676 2.9P2 13 10BeNb 3 [86] 16.08 10P m 3n 43BaH 12 [87] 6.555 20F m 3m 11Nb 3Si[88] 15.58 8.9P m 3n 44HfAsRu[89] 6.406 4.7P 62m 12Ta 5N6 [90] 14.59 7Cm 45Re 7B3 [91] 6.406 3.3Cm 13NbN[92] 14.57 13.7F m 3m 46Nb 5Re24 [93] 6.23 8.8I 43m 14Cr 3Os[94] 14.03 4.0P m 3n 47ThTc 2 [95] 6.145 5.3Cmcm 15NbC[96] 13.99 11.8F m 3m 48ZrPRu[97] 6.113 3.7P nma 16MoC[98] 12.35 9.3P6 3/mmc 49AlV 2N[99] 6.023 15.7F m 3m 17V 3Ga[100] 11.68 15P m 3n 50Rb 2Mo3As3 [101] 5.99 10.3Amm2"},{"citing_arxiv_id":"2604.21850","ref_index":65,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-04-23T16:40:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"OptiMat Alloys is a conversational AI system that maintains a living FAIR database of multi-principal element alloy calculations and enables natural-language, on-demand computations with built-in uncertainty checks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20515","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Accurate and Efficient Interatomic Potentials for Dislocations in InP","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-04-22T12:58:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"New ACE and MACE potentials for InP achieve at most 4% error on partial dislocation formation energies versus DFT, outperforming literature models by factors of 4-12 while being computationally faster.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15821","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials","primary_cat":"cs.DC","submitted_at":"2026-04-17T08:20:32+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"MatRIS-MoE and Janus enable efficient exascale training of billion-parameter universal interatomic potentials by addressing second-order derivative computation and communication overheads.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.11827","ref_index":85,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Inverse Design of Inorganic Compounds with Generative AI","primary_cat":"physics.chem-ph","submitted_at":"2026-04-11T16:37:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A review of generative AI for inverse design of inorganic compounds, analyzing adaptations for their complexity in composition, geometry, symmetry, and electronic structure, with discussion of future benchmarks and synthesizability metrics.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.01642","ref_index":27,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Benchmarking Chemically Scalable Machine-Learning Interatomic Potentials for Large-Scale Simulations of Multicomponent Alloys","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-04-02T05:41:09+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"complex and multicomponent systems [26]. GRACE ex- tends the ACE formalism by introducing graph-based ba- sis functions that capture semi-local interactions while preserving permutation, translation, and rotation invari- ances. Recent work has trained foundational GRACE models on some of the largest available materials datasets ( MPtrj [15], OMat24 [27] and Alexandria [28]), re- porting a new Pareto frontier between accuracy and per- atom computational cost [29]. In particular, the Finnis- Sinclair-type variant (GRACE-FS) is highlighted for its straightforward parallelization and capability for efficient CPU-only inference, with reported timings indicating favourable scaling in LAMMPS [30]. This context motivates a more practical question for"},{"citing_arxiv_id":"2601.16331","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"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":"2512.09169","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"AI-Driven Expansion and Application of the Alexandria Database","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-12-09T22:31:17+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A combined generative model, ML potential, and graph neural network pipeline expands the Alexandria database by 1.3 million DFT-validated compounds with 99% success near the convex hull and releases training data for universal force fields.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.05717","ref_index":25,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"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},{"citing_arxiv_id":"2512.02309","ref_index":58,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"An experimentally validated end-to-end framework for operando modeling of intrinsically complex metallosilicates","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-12-02T01:09:17+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An end-to-end framework combining domain separation, lightweight ML potentials, and de novo in silico synthesis enables quantitative atomistic modeling of mesoporous metallosilicates that matches experimental densities, pair distribution functions, IR spectra, and hydroxyl densities.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Burov, H. Montigaud, B. Baptiste, and S. Nowak, Comparative structural study of Al2O3-SiO2 glasses and amorphous thin films, Int. J. Appl. Glass Sci.15, 212 (2024). [57] I. Ohira, M. Murakami, S. Kohara, K. Ohara, and E. Ohtani, Ultrahigh-pressure acoustic wave velocities of SiO 2-Al2O3 glasses up to 200 GPa, Prog. Earth Planet. Sci.3, 18 (2016). [58] G. Busca, Catalytic materials based on silica and alumina: Structural features and generation of surface acidity, Prog. Mater. Sci.104, 215 (2019). [59] V . Sanchez Escribano, G. Garbarino, E. Finocchio, and G. Busca,γ-alumina and amorphous silica-alumina: Struc- tural features, acid sites and the role of adsorbed water, Top. Catal.60, 1554 (2017)."},{"citing_arxiv_id":"2510.05020","ref_index":25,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-10-06T17:00:21+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"MACE-MPA-0 predicts Li diffusion Ea of 0.22 eV in LiF, fine-tuned version with 300 points gives 0.20 eV, close to DeePMD reference of 0.24 eV, using far less training data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2510.04015","ref_index":52,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Atomistic Machine Learning with Irreducible Cartesian Natural Tensors","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-10-05T03:26:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CarNet develops irreducible Cartesian natural tensors and an equivariant model that matches leading spherical-tensor performance for ML interatomic potentials and high-rank tensor predictions like elastic constants.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.14205","ref_index":48,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Teachers that teach the irrelevant: Pre-training machine learned interaction potentials with classical force fields for robust molecular dynamics simulations","primary_cat":"physics.chem-ph","submitted_at":"2025-09-17T17:36:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Pre-training ML interaction potentials on classical force fields followed by ab initio fine-tuning produces stable and accurate molecular dynamics simulations for gas-phase molecules, liquid water, and hydrogen combustion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.09113","ref_index":64,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-08-12T17:45:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Machine learning interatomic potentials fine-tuned on first-principles relaxation data accurately reproduce phonon spectra and optical lineshapes for defects, matching explicit calculations and experiments.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.16627","ref_index":44,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Tailored Vapor Deposition Unlocks Large-Grain, Wafer-Scale Epitaxial Growth of 2D Magnetic CrCl3","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-05-22T13:00:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Centimeter-scale epitaxial growth of phase-pure crystalline 2D CrCl3 films achieved on mica via controlled physical vapor transport with innovations in light management, high carrier-gas flow, and moisture control.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}