arXiv subjects

Julio J. Valdés

Publications and source records attributed to Julio J. Valdés.

3 recordsLinked to original sources

arXiv2025

afspm: A Framework for Manufacturer-Agnostic Automation in Scanning Probe Microscopy

Scanning probe microscopy (SPM) is a valuable technique by which one can investigate the physical characteristics of the surfaces of materials. However, its widespread use is hampered by the time-consuming nature of running an experiment and the significant domain knowledge required. Recent studies have shown the value of multiple forms of automation in improving this, but their use is limited due to the difficulty of integrating them with SPMs other than the one it was developed for. With this in mind, we propose an automation framework for SPMs aimed toward facilitating code sharing and reusability of developed components. Our framework defines generic control and data structure schemas which are passed among independent software processes (components), with the final SPM commands sent after passing through an SPM-specific translator. This approach permits multi-language support and allows for experimental components to be decoupled among multiple computers. Our mediation logic limits access to the SPM to a single component at a time, with a simple override mechanism in order to correct detected experiment problems. To validate our proposal, we integrated and tested it with two SPMs from separate manufacturers, and ran an experiment involving a thermal drift correction component.

physics.ins-det↗

arXiv2023

Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning

This paper explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic properties. Understanding the structure and characteristics of this kind of data is important when predicting the atomic composition from the properties in inverse molecular designs. Intrinsic dimension analysis, clustering, and outlier detection methods were used in the study. They revealed that for both datasets the intrinsic dimensionality is several times smaller than the descriptive dimensions. The QM7b data is composed of well defined clusters related to atomic composition. The QM9 data consists of an outer region predominantly composed of outliers, and an inner core region that concentrates clustered, inliner objects. A significant relationship exists between the number of atoms in the molecule and its outlier/inner nature. Despite the structural differences, the predictability of variables of interest for inverse molecular design is high. This is exemplified with models estimating the number of atoms of the molecule from both the original properties, and from lower dimensional embedding spaces.

physics.chem-ph↗

arXiv2020

Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design

Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the discovery of new molecules, drugs, and materials. Models that combine QM with ML (QM-ML) have been very effective in delivering the precision of QM at the high speed of ML. In this study, we show that by integrating well-known signal processing (SP) techniques (i.e. short time Fourier transform, continuous wavelet analysis and Wigner-Ville distribution) in the QM-ML pipeline, we obtain a powerful machinery (QM-SP-ML) that can be used for representation, visualization and forward design of molecules. More precisely, in this study, we show that the time-frequency-like representation of molecules encodes their structural, geometric, energetic, electronic and thermodynamic properties. This is demonstrated by using the new representation in the forward design loop as input to a deep convolutional neural networks trained on DFTQM calculations, which outputs the properties of the molecules. Tested on the QM9 dataset (composed of 133,855 molecules and 19 properties), the new QM-SP-ML model is able to predict the properties of molecules with a mean absolute error (MAE) below acceptable chemical accuracy (i.e. MAE < 1 Kcal/mol for total energies and MAE < 0.1 ev for orbital energies). Furthermore, the new approach performs similarly or better compared to other ML state-of-the-art techniques described in the literature. In all, in this study, we show that the new QM-SP-ML model represents a powerful technique for molecular forward design. All the codes and data generated and used in this study are available as supporting materials at https://github.com/TABeau/QM-SP-ML.

physics.chem-ph↗