Phase 3 in detail
The long-term pieces, each my hypothesis, with the pain point it addresses and what it would take.
A knowledge sharing platform for the field
The long-term goal: a shared record of measured data, failed runs, controls and analyses that labs contribute to and draw from, with each lab in control of what's shared and when. Failed runs would count too, as phase 2 starts to show. Sharing has to be easy: an agent that packages the data and code behind a paper could remove most of that work, starting with papers already published, while the data still exists: in biology, the odds that a paper's data could still be found fell by 17% for every year after publication. Physics already has pieces of this: HEPData holds the numbers behind the plots of several thousand particle physics papers, and NOMAD takes condensed matter data in a standard format. Where samples vary, pooling many groups' devices shows which features are common and whether an artifact appears on similar devices elsewhere, without each lab building the control experiment itself, though it can't rule out an artifact in one particular device.1,2,3,4
What it would take. A neutral host, funding to build and run it, data standards for the field's instrument formats, access rules labs trust, and enough early contributors that drawing from it is worth contributing, with enough sample detail to compare devices across labs.
Reproducible science as the norm
Every published result can be rerun from its data and code, and the field moves there as a whole rather than lab by lab. Economics shows it can be done: its main society checks every computational article before publication.5
What it would take. Journals and funders that ask for it, tools that make it cheap, and a check before publication. Analyses with interactive steps done by hand are the hard part.
An AlphaFold for materials, as far as that's possible
AlphaFold was trained on the Protein Data Bank's measured structures, an archive that journals have required deposits into since 1989 and that passed 200,000 entries in 2023, all in one standard form. Materials have a partial equivalent for structure and much less for measured properties, and values mined from papers by software are imprecise: one text-mined set of magnetic transition temperatures was about 72% precise. A shared record of measured properties, failures included, is what such a model would need, and it would be harder than for proteins: transport and STM data describe a device with its contacts, not only a material.6,7,8,9
What it would take. Measured properties such as transport, spectroscopy and phase diagrams, with the sample details and conditions behind them, in a standard form, from many labs, including the runs that showed nothing.
A map of where the field invests
A map of where people research and publish, what's being funded, and where nobody is working, so funders can see where money would do the most good. In biomedicine, scientists follow conservative strategies near well-studied molecules, which slows collective discovery, and in materials science, research concentrates on fewer concepts over time. Nobody has measured this for condensed matter. A solar physicist pointed out a catch to me: a funding gap isn't always a scientific gap, because some areas get little money because they're largely solved.10,11,12
What it would take. Publication and funding data, plus a way to tell open questions from solved ones, which probably needs physicists' judgment as well as data. The map has to find real scientific gaps, not just quiet areas.
Credit for less exciting work
Controls, replications, null results and careful ruling-out of simple explanations count for funding, hiring and publication. Today they often don't: journals turned down one group's replications of condensed matter claims as not novel or no longer relevant, and the paper that finally appeared in Science spent what the university called a record two years under review. The Pittsburgh report asks funders, publishers and universities to count this work. Other fields have one working model in Registered Reports, a format in which the plan is peer reviewed before the data is collected and the paper is published regardless of outcome.13,14,15,16
What it would take. Journals with a place for this work, funders and hiring committees that count it, and a record that makes it visible. Registered Reports fit confirmation experiments better than exploratory ones.
Agents across the research workflow, building on phase 1
The phase 1 assistant is the start. Built out, the same agents would walk a researcher from the literature review to publication, and do the less exciting work that often gets skipped: packaging data and code, testing simple explanations against shared data, rerunning analyses, and reanalyzing old data with new methods. General models still struggle with condensed matter reasoning: on 50 expert-written theory problems, the best model in 2025 solved 30%. Old data can pay off: particle physicists revived data from a collider that shut down in 1986 and reanalyzed it with modern theory and simulation, and kept raw data makes the same possible for old transport, STM or ARPES measurements.17,18
What it would take. The library phase 1 starts to build, extended to instruments, data formats and known artifacts; access to the full text of the literature; and data and code in a form agents can run. Condensed matter analyses are larger and less standard than the cases tested so far.
Nearly all of this needs two things phases 1 and 2 start to build: a library that knows the field, and shared data, failures included.