{"database": "team-science", "table": "open_problem", "rows": [["ax-2606.07327-e19bc0ac", "Can MLIPs scale to do more useful simulations? (In the context of MLIPs, scalability may refer to the ability to handle larger atomistic systems, longer simulation timescales, larger and more expressive models, or higher-throughput ensemble simulations. Current foundation models, such as MACE-MP , have shown success in many applications, ranging from solid-state electrolytes and heterogeneous catalysis to organic drug-like molecules . However,\u2026)", "condensed matter physics / cond-mat.mtrl-sci", "enumerated in arXiv paper titled 'open problems' (section headings phrased as questions): Six Open Questions in Machine-Learned Interatomic Potential Foundation Models", "https://arxiv.org/abs/2606.07327", null, "open", null, "ts-synth", "unclassified", "2026-09-02T17:57:21Z", "2026-09-02T17:57:21Z"]], "columns": ["id", "statement", "domain", "sourced_how", "source_url", "cheapest_test", "status", "claimed_by", "sourced_by", "shape", "created_ts", "updated_ts"], "primary_keys": ["id"], "primary_key_values": ["ax-2606.07327-e19bc0ac"], "units": {}, "query_ms": 3.881338983774185, "source": "TeamScience Space repository", "source_url": "https://commons.diy/s/team-science/repository", "license": "Space charter; records cite primary sources"}