{"slug": "a-physics-chemistry-informed-neural-network-pcinn-for-real-time-spatial-ald-and", "title": "A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverage Prediction and Reliable Kinetics Inversion", "summary": "Researchers introduced a physics-chemistry-informed neural network (PCINN) that predicts surface coverage in spatial atomic layer deposition (SALD) in about 7 milliseconds, roughly 50,000 times faster than CFD, with a test R²_log of 0.998 from only 30 training cases. The hybrid surrogate encodes known surface kinetics as a trainable layer, enabling reliable inversion of adsorption energy and desorption rate, while identifying a degeneracy valley for prefactor and activation energy. The study, based on simulated data, verifies pipeline self-consistency and identifiability boundaries rather than real parameters.", "body_md": "arXiv:2608.00212v1 Announce Type: new\nAbstract: Spatial atomic layer deposition (SALD) is a leading atmospheric-pressure, high-throughput route to industrial ALD, but design and control are limited by the cost of predicting surface coverage: high-fidelity CFD is far too slow for operating-window scans, while analytic models miss transport modulation such as the gas curtain. We present a physics-chemistry-informed neural network (PCINN), a hybrid surrogate with CFD-level accuracy at real-time speed: a query returns coverage in about 7 ms, roughly 5x10^4 times faster than a CFD solve, reaching a test R^2_log = 0.998 (leave-one-out R^2_raw = 0.974) from only 30 training cases spanning four orders of magnitude in coverage.\nThe architecture is not a black box: a small network learns only the operating-condition to near-wall concentration closure, while the known surface kinetics is a hard-coded, trainable chemistry layer integrated along the substrate trajectory. This single-scalar bottleneck keeps it accurate under sparse data, interpretable and invertible.\nWe add a full identifiability analysis (Fisher information, profile likelihood). The adsorption energy E_ads and desorption rate k_des are robustly identifiable; k_ads is not separately identifiable at a single temperature (only k_ads*c_wall is). Across four temperatures the prefactor nu and E_ads bind along a weakly identifiable degeneracy valley of slope 0.065 eV/decade, derived analytically as k_B T_eff ln(10) and turned into a reliability diagnostic: a seven-chemistry mismatch matrix shows it is invariant under any single-Arrhenius mismatch and shifts only when a second thermally activated process appears, so a slope departure flags unmodelled site heterogeneity.\nData come from simulation with known ground truth inverted by the same kinetic form, so the study verifies pipeline self-consistency and the identifiability boundary, not real parameters.", "url": "https://wpnews.pro/news/a-physics-chemistry-informed-neural-network-pcinn-for-real-time-spatial-ald-and", "canonical_source": "https://arxiv.org/abs/2608.00212", "published_at": "2026-08-04 04:00:00+00:00", "updated_at": "2026-08-04 04:34:02.600592+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence", "ai-research"], "entities": ["PCINN", "SALD"], "alternates": {"html": "https://wpnews.pro/news/a-physics-chemistry-informed-neural-network-pcinn-for-real-time-spatial-ald-and", "markdown": "https://wpnews.pro/news/a-physics-chemistry-informed-neural-network-pcinn-for-real-time-spatial-ald-and.md", "text": "https://wpnews.pro/news/a-physics-chemistry-informed-neural-network-pcinn-for-real-time-spatial-ald-and.txt", "jsonld": "https://wpnews.pro/news/a-physics-chemistry-informed-neural-network-pcinn-for-real-time-spatial-ald-and.jsonld"}}