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Researcher profile

Samir Darouich

Samir Darouich contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial Intelligencecond-mat.mtrl-sciMachine Learningphysics.chem-ph

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Published work

2 published item(s)

preprint2026arXiv

INTERFACE Force Field for Alumina with Validated Bulk Phases and a pH-Resolved Surface Model Database for Electrolyte and Organic Interfaces

Alumina and aluminum oxyhydroxides underpin chemical-engineering technologies from heterogeneous catalysis, corrosion protection, functional coatings, energy-storage devices, to biomedical components. Yet molecular models that predictively connect phase structure, pH-dependent surface chemistry, electrolyte organization, and adsorption across operating conditions remain limited. Here we introduce a unified INTERFACE Force Field (IFF) parameterization together with a curated, ready-to-use pH-resolved surface model database that provides the most accurate and transferable atomistic description of major alumina phases to date. The framework covers a-Al2O3, g-Al2O3, boehmite, diaspore, and gibbsite using a single, physically interpretable parameter set that is directly compatible with CHARMM, AMBER, OPLS-AA, CVFF, and PCFF. Across structural, thermodynamic, mechanical, and interfacial benchmarks, simulations reproduce experimental reference data with more than 95 percent accuracy, exceeding existing force fields and the reliability of current density-functional approaches. A key advance is the first transferable treatment of surface ionization and charge regulation across alumina phases over a broad range of pH values, enabling simulations of realistic solid electrolyte interfaces without phase-specific reparameterization. Quantitative reliability is demonstrated by reproducing trends in zeta potentials and pH-dependent adsorption of a corrosion inhibitor at alumina-water interfaces. Predicted adsorption free energies and surface contact times correlate with experiments across more than an order of magnitude. Relative to ML-DFT workflows, the speed 100 to 1000 times faster, reaching system sizes and time scales inaccessible to quantum methods. The results establish a predictive computational platform to design alumina-containing functional materials under realistic process conditions.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SymDrift: One-Shot Generative Modeling under Symmetries

Generative modeling of physical systems, such as molecules, requires learning distributions that are invariant under global symmetries, such as rotations in three-dimensional space. Equivariant diffusion and flow matching models can incorporate such invariances effectively, even when trained on a non-invariant empirical distribution, but they typically rely on costly multi-step sampling. Recently, drifting models have emerged as an efficient alternative, enabling single-step generation and achieving state-of-the-art performance in generative modeling tasks. However, we show that drifting models face a symmetry-specific challenge, since an equivariant generator does not generally produce the same drifting field as the one obtained from the symmetrized target distribution. Addressing this issue would require expensive symmetrization of the empirical distribution. To avoid this cost, we propose SymDrift, a framework that makes the drifting field itself symmetry-aware. We introduce two complementary strategies: (i) a symmetrized drift in coordinate space based on optimal alignment, and (ii) a $G$-invariant embedding that removes symmetry ambiguity by construction. Empirically, SymDrift outperforms existing one-shot methods on standard benchmarks for conformer and transition state generation, while remaining competitive with significantly more expensive multi-step approaches. By enabling one-shot inference, SymDrift reduces computational overhead by up to 40$\times$ compared to existing baselines, making it promising for high-throughput applications such as virtual drug screening and large-scale reaction network exploration.

0 citations0 reviewsNo code linkedOpen access