AI materials science figure generator

Draft materials, device, and processing figures

Describe the composition, layers, interfaces, processing sequence, and measured property. SciFig creates a visual draft for materials-science review.

Choose a materials science starting point

Replace generic materials, phases, dimensions, and properties with values and terminology verified in your study.

0/2000

Bezplatná ukázka s výstupem ve formátu 1K.Přihlásit sePoté lze odemknout stránky 2K / 4K a uložit historii.

Multiple AI models

GPT Image 2, Nano Banana and Z-Image — pick the right one per figure.

Up to 4K export

Render at 1K, 2K or 4K for print-ready manuscript output.

Text & sketch to figure

Start from a prompt, or refine a rough hand-drawn sketch.

Free to start

Generate a 1K preview with no sign-up required.

AI materials science figure generator

Materials science visual structures to explore

These AI-generated examples show visual hierarchy only. Verify phases, lattice geometry, interfaces, dimensions, transport direction, and performance data.

Materials science workflow

Connect composition, structure, process, and property

AI-generated thin-film deposition process illustration

Declare each length scale

Separate atomic lattice, nanoscale feature, grain, film, component, and complete device. Use inset boundaries and scale labels so readers do not interpret a conceptual transition as a continuous physical view.

Name composition, phase, orientation, and interface
Mark schematic views as not to scale
Use source microscopy for observed morphology
Keep crystal axes and device axes distinct

AI-generated stress-strain testing curve illustration

Show processing without inventing conditions

Organize synthesis, deposition, heat treatment, patterning, and assembly as a controlled sequence. Insert verified temperatures, times, atmospheres, and dimensions only from laboratory records.

Check every label and scientific term
Verify arrows, structures, and causal relationships
Compare the draft with the source data or manuscript

AI-generated material microstructure grain illustration

Represent structure-property evidence honestly

Distinguish measured correlation, proposed mechanism, simulation result, and established causal effect. Performance curves, diffraction patterns, microscopy, and fitted parameters must come from source data.

Confirm dimensions, file format, and resolution
Review the target venue's AI-use policy
Complete a final expert review before use

Materials science figure generator questions

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Create a materials science figure draft

Define the scale, composition, interfaces, process, and verified property before generating.

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