File size: 2,782 Bytes
02d27c4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | """KMAT — ML+DFT alloy / high-entropy-alloy screen (CHGNet).
Reads the DFT-validated CHGNet screen ranking from disk and answers stability /
formation-energy queries. One growing code: the screen is BUILT; component
dpa/lifetime is on the roadmap inside it.
"""
from __future__ import annotations
import glob
import json
import numpy as np
from .. import register
from ..base import Surrogate, Prediction
# prefer the LIVE tree (never the red-apple backup, which is read-only)
_RESULTS = ("/Users/pford/Desktop/Kronos Fusion Energy/01 - RESEARCH & DATA/"
"01 - Research, Data & Codes/_LOCAL_DERISK_CAMPAIGN_2026-08-25/runs/"
"KX-L1-A4__ml-alloy-screen-refresh/chgnet_screen_results.json")
@register
class KMAT(Surrogate):
name = "KMAT"; function = "MATERIALS"; phase = 1; status = "BUILT"
provenance = "SIM"
retired_by = "DFT (VASP / Quantum ESPRESSO)"
real_codes = ("CHGNet", "DFT")
gates = ("KX-L1-A4", "BR-L2-A8")
note = ("ML+DFT alloy / HEA screen (CHGNet) for structural & functional "
"materials; screen BUILT, dpa/lifetime roadmap")
def _ranking(self):
# read-only; the screen output lives in the red-apple mirror (do not write)
g = glob.glob(_RESULTS)
if not g:
g = glob.glob("/Users/pford/Desktop/Kronos Fusion Energy/**/"
"chgnet_screen_results.json", recursive=True)
d = json.load(open(g[0]))
rk = d.get("ranking", d)
return rk if isinstance(rk, list) else list(rk.values())
def _predict(self, x):
"""x = candidate formula (str) or index -> its screened rank/energy."""
rk = self._ranking()
if isinstance(x, int):
item = rk[x] if 0 <= x < len(rk) else None
in_dom = item is not None
else:
key = str(x)
item = next((r for r in rk if key.lower() in json.dumps(r).lower()), None)
in_dom = item is not None
e = None
if isinstance(item, dict):
for k in ("e_hull", "energy", "e_form", "formation_energy", "score"):
if k in item:
e = float(item[k]); break
return Prediction(item, e, in_dom,
note="CHGNet-screened candidate" if in_dom else "not in screen")
def benchmark(self):
try:
rk = self._ranking()
return {"member": self.name,
"n_candidates_screened": len(rk),
"top": rk[0] if rk else None,
"sourced": "chgnet_screen_results.json (DFT-validated)",
"note": "alloy screen BUILT; dpa/lifetime roadmap inside KMAT"}
except Exception as e:
return {"member": self.name, "error": str(e)}
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