arXiv subjects

Alejandra Hinostroza Caldas

Publications and source records attributed to Alejandra Hinostroza Caldas.

2 recordsLinked to original sources

arXiv2026

QUPEX: A Streamlit-Based Framework for Visualizing Quantum-Mechanical Property Spaces of Organic Molecules

The growing availability of quantum-mechanical (QM) datasets offers new opportunities to investigate structure-property and property-property relationships in drug-like molecules using data-driven methods. However, interactive tools that integrate molecular visualization, statistical analysis of QM properties, clustering, and machine learning (ML) remain limited, despite their value in facilitating dataset exploration and interpretation. To address this gap, we introduce QUPEX (QUantum Property EXplorer), an open-source Streamlit-based framework for the interactive exploration of QM property spaces in molecular systems. As a demonstration, we apply QUPEX to a subset of the QM7-X dataset comprising 41,537 equilibrium molecular structures containing up to seven non-hydrogen atoms (C, N, O, S, and Cl) and their 24 associated QM properties. In addition to property-space analysis, QUPEX incorporates an XGBoost regression pipeline for predicting PBE0 dipole moments from tight-binding-derived electronic descriptors, together with model evaluation and SHAP-based interpretability functionality. By combining interactive molecular visualization, data analysis, and predictive modeling, QUPEX provides an accessible and extensible environment for investigating molecular datasets and identifying patterns in their structural and electronic properties. These capabilities can support the exploration of chemical space and inform the rational design of drug-like molecules with targeted QM properties.

physics.chem-ph↗

arXiv2026

Perspective: Towards sustainable exploration of chemical spaces with machine learning

Artificial intelligence is transforming molecular and materials science, but its growing computational and data demands raise critical sustainability challenges. In this Perspective, we examine resource considerations across the AI-driven discovery pipeline--from quantum-mechanical (QM) data generation and model training to automated, self-driving research workflows--building on discussions from the ``SusML workshop: Towards sustainable exploration of chemical spaces with machine learning'' held in Dresden, Germany. In this context, the availability of large quantum datasets has enabled rigorous benchmarking and rapid methodological progress, while also incurring substantial energy and infrastructure costs. We highlight emerging strategies to enhance efficiency, including general-purpose machine learning (ML) models, multi-fidelity approaches, model distillation, and active learning. Moreover, incorporating physics-based constraints within hierarchical workflows, where fast ML surrogates are applied broadly and high-accuracy QM methods are used selectively, can further optimize resource use without compromising reliability. Equally important is bridging the gap between idealized computational predictions and real-world conditions by accounting for synthesizability and multi-objective design criteria, which is essential for practical impact. Finally, we argue that sustainable progress will rely on open data and models, reusable workflows, and domain-specific AI systems that maximize scientific value per unit of computation, enabling efficient and responsible discovery of technological materials and therapeutics.

cs.LG↗