arXiv subjects

Diego Guedes-Sobrinho

Publications and source records attributed to Diego Guedes-Sobrinho.

5 recordsLinked to original sources

arXiv2026

Interpretable, Physics-Informed Learning Reveals Sulfur Adsorption and Poisoning Mechanisms in 13-Atom Icosahedra Nanoclusters

Transition-metal nanoclusters exhibit structural and electronic properties that depend on their size, often making them superior to bulk materials for heterogeneous catalysis. However, their performance can be limited by sulfur poisoning. Here, we use dispersion-corrected density functional theory (DFT) and physics-informed machine learning to map how atomic sulfur adsorbs and causes poisoning on 13-atom icosahedral clusters from 30 different transition metals (3$d$ to 5$d$). We measure which sites sulfur prefers to adsorb to, the thermodynamics and energy breakdown, changes in structure, such as bond lengths and coordination, and electronic properties, such as $\varepsilon_d$, the HOMO-LUMO gap, and charge transfer. Vibrational analysis reveals true energy minima and provides ZPE-based descriptors that reflect the lattice stiffening upon sulfur adsorption. For most metals, the metal-sulfur interaction mainly determines adsorption energy. At the same time, distortion penalties are usually moderate but can be significant for a few metals, suggesting these are more likely to restructure when sulfur is adsorbed. Using unsupervised \textit{k}-means clustering, we identify periodic trends and group metals based on their adsorption responses. Supervised regression models with leave-one-feature-out analysis identify the descriptors that best predict adsorption for new samples. Our results highlight the isoelectronic triad \ce{Ti}, \ce{Zr}, and \ce{Hf} as a balanced group that combines strong sulfur binding with minimal structural change. Additional DFT calculations for \ce{SO2} adsorption reveal strong binding and a clear tendency toward dissociation on these clusters, linking electronic states, lattice response, and poisoning strength. These findings offer data-driven guidelines for designing sulfur-tolerant nanocatalysts at the subnanometer scale.

physics.atm-clus↗

arXiv2025

GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols

Predictive atomistic simulations have propelled materials discovery, yet routine setup and debugging still demand computer specialists. This know-how gap limits Integrated Computational Materials Engineering (ICME), where state-of-the-art codes exist but remain cumbersome for non-experts. We address this bottleneck with GENIUS, an AI-agentic workflow that fuses a smart Quantum ESPRESSO knowledge graph with a tiered hierarchy of large language models supervised by a finite-state error-recovery machine. Here we show that GENIUS translates free-form human-generated prompts into validated input files that run to completion on $\approx$80% of 295 diverse benchmarks, where 76% are autonomously repaired, with success decaying exponentially to a 7% baseline. Compared with LLM-only baselines, GENIUS halves inference costs and virtually eliminates hallucinations. The framework democratizes electronic-structure DFT simulations by intelligently automating protocol generation, validation, and repair, opening large-scale screening and accelerating ICME design loops across academia and industry worldwide.

cs.AI↗

arXiv2025

Teaching a Transformer to Think Like a Chemist: Predicting Nanocluster Stability

Atomically precise metal nanoclusters bridge the molecular and bulk regimes, but designing bimetallic motifs with targeted stability and reactivity remains challenging. Here we combine density functional theory (DFT) and physics-grounded predictive artificial intelligence to map the configurational landscape of 13-atom icosahedral nanoclusters X$_{12}$TM, with hosts X = (Ti, Zr, Hf), and Fe and a single transition--metal dopant spanning the 3$d$-5$d$ series. Spin-polarized DFT calculations on 240 bimetallic clusters reveal systematic trends in binding and formation energies, distortion penalties, effective coordination number, d-band centre, and HOMO-LUMO gap that govern the competition between core-shell (in) and surface-segregated (out) arrangements. We then pretrain a transformer architecture on a curated set of 2968 unary clusters from the Quantum Cluster Database and fine-tune it on bimetallic data to predict formation energies and in/out preference, achieving mean absolute errors of about $0.6-0.7$eV and calibrated uncertainty intervals. The resulting model rapidly adapts to an unseen Fe-host domain with only a handful of labelled examples. At the same time, attention patterns and Shapley attributions highlight size mismatch, $d$-electron count, and coordination environment as key descriptors. All data, code, and workflows follow FAIR/TRUE principles, enabling reproducible, interpretable screening of unexplored nanocluster chemistries for catalysis and energy conversion.

physics.chem-ph↗

arXiv2018

Thermodynamic Stability and Structural Insights for CH$_3$NH$_3$Pb$_{1-x}$Si$_x$I$_3$, CH$_3$NH$_3$Pb$_{1-x}$Ge$_x$I$_3$, and CH$_3$NH$_3$Pb$_{1-x}$Sn$_x$I$_3$ Hybrid Perovskite Alloys: A Statistical Approach from First Principles Calculations

The recent reaching of 20% of conversion efficiency by solar cells based on metal hybrid perovskites (MHP), e.g., the methylammonium (MA) lead iodide, CH3NH3PbI3 (MAPbI3), has excited the scientific community devoted to the photovoltaics materials. However, the toxicity of Pb is a hindrance for large scale commercial of MHP and motivates the search of another congener eco-friendly metal. Here, we employed first-principles calculations via density functional theory combined with the generalized quasichemical approximation to investigate the structural, thermodynamic, and ordering properties of MAPb1-xSixI3, MAPb1-xGexI3, and MAPb1-xSnxI3 alloys as pseudo-cubic structures. The inclusion of a smaller second metal, as Si and Ge, strongly affects the structural properties, reducing the cavity volume occupied by the organic cation and limitating the free orientation under high temperature effects. Unstable and metaestable phases are observed at room temperature for MAPb1-xSixI3, whereas MAPb1-xGexI3 is energetically favored for Pb-rich in ordered phases even at very low temperatures. Conversely, the high miscibility of Pb and Sn into MAPb1-xSnxI3 yields an alloy energetically favored as a pseudo-cubic random alloy with tunable properties at room temperature.

physics.chem-ph↗

arXiv2018

(Meta-)stability and Core-Shell Dynamics of Gold Nanoclusters at Finite Temperature

Gold nanoclusters have been the focus of numerous computational studies but an atomistic understanding of their structural and dynamical properties at finite temperature is far from satisfactory. To address this deficiency, we investigate gold nanoclusters via ab initio molecular dynamics, in a range of sizes where a core-shell morphology is observed. We analyze their structure and dynamics using of state-of-the-art techniques, including unsupervised machine-learning nonlinear dimensionality reduction (sketch-map) for describing the similarities and differences among the range of sampled configurations. We find that, whereas the gold nanoclusters exhibit continuous structural rearrangement, they clearly show persistent motifs: a cationic core of one to five atoms is loosely bound to a shell which typically displays a substructure resulting from the competition between locally spherical vs planar fragments. Besides illuminating the properties of core-shell gold nanoclusters, the present study proposes a set of useful tools for understanding their nature in operando.

cond-mat.mes-hall↗