QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery

Abstract

Structural elucidation of chemical compounds is challenging experimentally, and theoretical chemistry methods have added important insight into molecules, nanoparticles, alloys, and materials geometries and properties. However, finding the optimum structures is a bottleneck due to the huge search space, and global search algorithms have been used successfully for this purpose. In this work, we present the quantum machine learning software/agent for materials design and discovery (QMLMaterial), intended for automatic structural determination in silico for several chemical systems: atomic clusters, atomic clusters and the spin multiplicity together, doping in clusters or solids, vacancies in clusters or solids, adsorption of molecules or adsorbents on surfaces, and finally atomic clusters on solid surfaces/materials or encapsulated in porous materials. QMLMaterial is an artificial intelligence (AI) software based on the active learning method, which uses machine learning regression algorithms and their uncertainties for decision making on the next unexplored structures to be computed, increasing the probability of finding the global minimum with few calculations as more data is obtained. The software has different acquisition functions for decision making (e.g., expected improvement and lower confidence bound). Also, the Gaussian process is available in the AI framework for regression, where the uncertainty is obtained analytically from Bayesian statistics. For the artificial neural network and support vector regressor algorithms, the uncertainty can be obtained by K-fold cross-validation or nonparametric bootstrap resampling methods. The software is interfaced with several quantum chemistry codes and atomic descriptors, such as the many-body tensor representation. QMLMaterial's capabilities are highlighted in the current work by its applications in the following systems: Na 20 , Mo 6 C 3 (where the spin multiplicity was considered), H 2 O@CeNi 3 O 5 , Mg 8 @graphene, Na 3 Mg 3 @CNT (carbon nanotube).

Pd8 Cluster: Too Small to Melt? A BOMD Study

“…Despite these challenges, research on the design and use of global optimization algorithms dedicated to the PES exploration of clusters has seen notable advances over the past years. For instance, the works by Schön et al Doye and Wales, Heiles and Johnston, and Lourenço et al are inspiring examples that have employed empirical and ab initio schemes to perform extensive global optimization searches to locate the different minima of a PES. Most interestingly, recent experimental and theoretical studies have shed some light on the structural stability of the located minimum-energy structures through the analysis of the energy barriers among the different isomer structures. − Foster et al determined the energy difference between isomers of gold nanoparticles at low (20–125 °C) and high (125–500 °C) temperatures, in both cases observing cluster structural rearrangements between different symmetries.…”