Artificial intelligence / ai/ai-for-science
AI for science
AI systems that propose, test and refine scientific hypotheses, designs and experiments well enough that their results are confirmed in the laboratory at a rate that speeds up discovery.
Scope
In scope: models and agents used to generate and rank candidates (molecules, proteins, materials, experiments) and to close the loop with laboratory or simulation feedback. Out of scope: the energy cost of running them, covered by ai/energy-efficient-inference; the specific discoveries they enable, covered by technologies such as biotech/de-novo-protein-design and health/antibiotics-for-resistant-bacteria.
- Readiness
- not assessed
- Serves
- Good health and well-being, Industry, innovation and infrastructure
- Last reviewed
- never
- Curators
- none yet: volunteer
Proposed: the statement and scope are written, but the metrics, target or gaps are not complete yet. One sourced number is a real contribution.
Metrics
No metric yet.
Gaps
No gap recorded yet.
Dependencies
Requires
- Energy-efficient AI inference Screening and agentic loops run many model calls per experiment, so the energy and cost per token set how far discovery loops can scale.
Required by
- De novo protein design Design methods are deep-learning models trained on protein structures.
- Antibiotics for resistant bacteria Machine-learning models screen and generate candidate molecules far faster than screening libraries by hand.
Holds open
- Success rate varies widely between targets and between groupsDe novo protein design
Arrows point from a technology to what it requires. Select a node to open it.
Evidence
No evidence cited yet.