Datasets:
File size: 2,800 Bytes
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"field_id": "F042",
"field": "computational materials science",
"directions": [
{
"direction_id": "G0",
"name": "Forward prediction of materials properties and responses",
"scope": "Given material descriptions, microstructures, loading, or manufacturing conditions, predict material classes, properties, and the responses of specified structures. This includes process–structure–property relationships and manufacturing decisions, as well as effective mechanical properties, spatial response fields, and fracture trajectories."
},
{
"direction_id": "G1",
"name": "Learning material interactions and governing response laws",
"scope": "Learn reusable governing material relationships for physical computation, including atomistic or coarse-grained energies, forces, and effective interactions, as well as continuum constitutive responses, internal-variable evolution, hysteresis laws, and nonlocal operators. This also includes simulation observables derived from interaction models."
},
{
"direction_id": "G2",
"name": "Simulation of material states and their evolution",
"scope": "Use learned components to accelerate or replace costly physical simulations to investigate material states, phase transitions, transport, and microstructure and defect configurations that change over time or process steps."
},
{
"direction_id": "G3",
"name": "Goal-directed materials design and candidate search",
"scope": "Find, construct, and improve candidate materials and configurations for stability or target functions. The full scope includes designing functional microstructures, porous architectures, and surface topologies; generating compositions, molecules, frameworks, and crystal structures; searching configurations on learned objective landscapes; and selecting candidates and experiments using predictive or experimental feedback."
},
{
"direction_id": "G4",
"name": "Characterization and recovery of material structure from observations",
"scope": "Obtain material-structure information from micrographs, atomic configurations, diffraction, spectra, or other incomplete observations. This includes reconstructing representative spatial microstructures and orientation maps; detecting, segmenting, and quantifying observed features; and inferring hidden atomic structures, crystal phases, or physical-model parameters."
},
{
"direction_id": "G5",
"name": "Reliability and evaluation of materials models",
"scope": "Assess the coverage, uncertainty, generalization, and task-relevant validity of materials predictions and generated candidates, including through datasets and benchmarks developed to test these capabilities."
}
]
}
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