F. 7211 / NIGHT REGISTER

Da Vinci Labs

We are a Berkeley research lab. We build MaterialsCodeGraph and OpenMaterials for computational materials physics.

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Open the record

F. 7212 / RESULT RECORD

One result

Material
Ge
Quantity
Thermal conductivity
Value
Code
kALDo
Conditions
300 K, Ge.tersoff, 8x8x8 mesh
Lineage prefix
1e0e4ccad42d03ee
Record
openmaterials.ai/examples/ge-kappa-kaldo.json
GeGe.tersoffmesh 8x8x8T = 300FORCE_CONSTANTS_3RDphonon_bte_kappakALDo bte_solver=direct_inversekappa = 15.276 W/(m K)
Nodes and edges are read from the record. Positions are schematic.

Press the value to trace its recorded ancestry; press again to clear.

  • Ancestor
  • Recorded join
  • Result

F. 7213 / PRODUCTS

Products

MaterialsCodeGraph
MaterialsCodeGraph runs materials simulations for AI agents over MCP, with a binding token quote before every run and a permanent record after it.
Type
Hosted MCP server
Registry
com.materialscodegraph/mcg (0.1.0, active)
Endpoint
https://api.materialscodegraph.com/mcp
Product
materialscodegraph.com
Open example
Trace the derivation
Pipeline
STRUCTUREMD / GPUMDTRANSPORT / MESKALCHEMISTRY / xtbRECORD
OpenMaterials
Records keep their version and are addressed by content hash.
Type
Open initiative, CC BY 4.0 data
Site
openmaterials.ai
Version
f69b18c18fb7
Quantities
114 nodes, 100 observable
Codes
15
Unit
one lineage: a result and its ancestry
Open example
Explore the map

F. 7214 / METHOD

Working method

  1. 1Reproduce a published reference within its stated error bars.
  2. 2Canonicalize the ancestry and hash it into a lineage address.
  3. 3Quote the whole bill before the run.
  4. 4Write the record to OpenMaterials, versioned and content-addressed.
Lineage
A result and the ancestry that produced it.
Stewardship
OpenMaterials is stewarded by OpenMaterials-AI, a foundation in formation.
Mission
Build shared infrastructure for computational physics.

F. 7215 / PUBLICATIONS

Publications

Giuseppe Barbalinardo, first author and principal developer of kALDo (github.com/nanotheorygroup/kaldo, BSD-3-Clause, release 2.2.1, 2026-07-11).

2026
G. Barbalinardo, Z. Chen, D.A. Folkner et al.kALDo 2.0: Scalable thermal transport from first principles and machine learning potentials.Comput. Phys. Commun. 327, 110282 10.1016/j.cpc.2026.110282
2021
G. Barbalinardo, Z. Chen, H. Dong et al.Ultrahigh convergent thermal conductivity of carbon nanotubes from comprehensive atomistic modeling.Phys. Rev. Lett. 127, 025902 10.1103/PhysRevLett.127.025902
2021
N.W. Lundgren, G. Barbalinardo, D. DonadioMode localization and suppressed heat transport in amorphous alloys.Phys. Rev. B 103, 024204 10.1103/PhysRevB.103.024204
2020
G. Barbalinardo, Z. Chen, N.W. Lundgren et al.Efficient anharmonic lattice dynamics calculations of thermal transport in crystalline and disordered solids.J. Appl. Phys. 128, 135104 10.1063/5.0020443
2019
L. Isaeva, G. Barbalinardo, D. Donadio et al.Modeling heat transport in crystals and glasses from a unified lattice-dynamical approach.Nat. Commun. 10, 3853 10.1038/s41467-019-11572-4