Alloybase Blog

“PBE functional, PAW pseudopotentials, 520 eV cutoff.” That sentence appears in thousands of computational materials papers. It is not a methods section. It is a caption. Lejaeghere et al. compared 71 elemental crystals across 15 DFT codes using 40 different potentials or basis set types (Science, 2016). Most modern codes converged, but only when parameters were carefully controlled. Hegde et al. later quantified what happens when they are not: comparing the same materials across AFLOW, Materials Project, and OQMD, formation energy variance reached 0.105 eV/atom (Phys. Rev. Materials, 2023). Up to 7% of materials disagreed on whether a compound was metallic or insulating. Fifteen percent disagreed on magnetic state. The three root causes: pseudopotential choice, DFT+U implementation, and elemental reference states. All three are routinely underreported in methods sections. ...

You ran an OPTIMADE query in January and retrieved 847 perovskite structures. In July, the same filter returns 891. The band gaps on 23 entries have changed. Three entries from your training set no longer appear in default results. None of that was announced. There is no changelog entry for it, no notification, no version bump you could have subscribed to. OPTIMADE (the open REST API standard for querying computational materials databases) providers update their databases continuously: DFT recalculations, pipeline bug fixes, new high-throughput runs, deprecated structures. None of these changes are surfaced through the standard query interface. ...

Building an OPTIMADE client is a reasonable engineering decision. The spec is well-documented, the Python tooling is open source, and most R&D teams have the capability. The question is not whether you can build it. The question is what it actually costs, and whether that cost makes sense for a team whose output is materials science, not infrastructure. What Does “Building It” Actually Mean? A minimal client that queries one OPTIMADE provider and returns results is an afternoon of work. That’s the prototype. ...

The Same Perovskite, Different Stability Verdicts Query SrTiO3 in Materials Project and OQMD, and you will get different hull distances. Not because one database made an error. Both did the calculation correctly. The differences come from parameter choices that are individually defensible but produce inconsistent numbers when compared directly. A 2023 study in Physical Review Materials (Kingsbury et al., PRM 7, 053805) found that up to 7% of compounds across AFLOW, MP, and OQMD disagree on whether a material is metallic, and up to 15% disagree on whether it is magnetic. For perovskites with transition-metal B-sites (the majority of technologically relevant oxide perovskites), these rates are the floor, not the ceiling. ...

These Tools Solve Different Problems Most researchers comparing the two are starting from the wrong question. The useful question is which problem each tool actually solves. Citrine Informatics manages proprietary R&D data. Alloybase searches and curates public computational materials databases. These are adjacent tools, not direct competitors. Understanding that distinction is the decision. What Citrine Informatics Actually Does Citrine’s platform has two modules. Citrine DataManager organizes proprietary experimental and simulation data using materials-specific schema. Citrine VirtualLab runs generative AI experiments on that proprietary data to suggest new formulations and compress lab iteration cycles. ...

OPTIMADE (Open Databases Integration for Materials Design) is a standardized REST API protocol for querying computational materials databases using a shared filter syntax. Thirteen databases currently support it, including Materials Project, AFLOW, OQMD, JARVIS-DFT, NOMAD, COD, TCOD, Materials Cloud, odbx, MPDS, and NREL Materials Database. The protocol was designed so that a single query string works uniformly across all participating databases. In practice, most researchers still open a separate browser tab for each one. ...

The OPTIMADE ecosystem looks different in 2026 than it did two years ago. Materials Project finished its r2SCAN migration. Alexandria added millions of new structures. Several smaller providers went offline; new ones came online. Most comparison resources date from 2022–2023. The r2SCAN migration alone changes which MP values you should trust. Follow an old tutorial and you may end up filtering on the wrong thermo endpoint. This directory covers every active OPTIMADE provider as of early 2026: entry counts, functionals, primary data types, compliance level, and the specific workflows each one handles well. ...

Querying the same compound in Materials Project and AFLOW returns different formation energies. Both values are correct. They used different DFT functionals, different pseudopotentials, different reference states. That’s not an error. That’s the thing you need to understand before you build a training set from any of them. Materials Project, AFLOW, OQMD, and JARVIS-DFT each make different computational choices. Here’s what those choices mean for your workflow. What each database actually offers Materials Project Over 150,000 inorganic compounds (check materialsproject.org for the current count; it grows regularly). The mp-api Python client is the best-designed API in this space: clean, typed, easy to filter on any property. Calculations use GGA+U for most compounds, with r2SCAN available for a growing subset. ...

If you’ve used optimade.science or similar OPTIMADE demos for a screening run, you know where they fall apart: the session ends and everything disappears. No saved query, no stored results, no record of which provider returned which row or when the data was fetched. That’s a reasonable tradeoff for an interactive demo. It’s a problem when you’re building a training dataset, documenting a DFT screening workflow for publication, or trying to reproduce a filter you ran three weeks ago. ...