Application of Density Functional Tight Binding and Machine Learning to Evaluate the Stability of Biomass Intermediates on the Rh(111) Surface

2021 ◽  
Author(s):  
Chaoyi Chang ◽  
Andrew J. Medford
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Tight Binding ◽  
The Stability

Predicting the stability of ternary intermetallics with density functional theory and machine learning

2018 ◽  
Vol 148 (24) ◽  
pp. 241728 ◽  
Author(s):  
Jonathan Schmidt ◽  
Liming Chen ◽  
Silvana Botti ◽  
Miguel A. L. Marques
Keyword(s):  
Machine Learning ◽  
Density Functional Theory ◽  
Density Functional ◽  
Functional Theory ◽  
The Stability

scholarly journals Ab initio thermodynamics of liquid and solid water

2019 ◽  
Vol 116 (4) ◽  
pp. 1110-1115 ◽  
Author(s):  
Bingqing Cheng ◽  
Edgar A. Engel ◽  
Jörg Behler ◽  
Christoph Dellago ◽  
Michele Ceriotti
Keyword(s):  
Machine Learning ◽  
Thermodynamic Properties ◽  
Ab Initio ◽  
Density Functional ◽  
Machine Learning Techniques ◽  
Intermediate Step ◽  
Hybrid Functional ◽  
Learning Techniques ◽  
The Stability ◽  
Comprehensive Framework

Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations, and proton disorder. This is made possible by combining advanced free-energy methods and state-of-the-art machine-learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments and reliable estimates of the melting points of light and heavy water. We observe that nuclear-quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine-learning potentials as an intermediate step.

Computational studies of energetics, electronic structure, and vibrational spectra of PETN nanoparticles

2021 ◽  
Vol 99 (1) ◽  
pp. 63-71
Author(s):  
Qiannan Ma ◽  
Weihua Zhu
Keyword(s):  
Electronic Structure ◽  
Vibrational Spectra ◽  
Density Functional ◽  
Solid Phase ◽  
Tight Binding ◽  
High Energy ◽  
Pentaerythritol Tetranitrate ◽  
Energy Bands ◽  
Extra Energy ◽  
The Stability

The density functional tight binding method was used to explore the energetics, electronic structure, and vibrational spectra of pentaerythritol tetranitrate (PETN) nanoparticles (NPs). The surface energy of the PETN NP is anisotropic and its extra energy decreases with the increase of size. The energy bands of the NPs are significantly expanded and the band gaps are narrowed, thus reducing the stability due to nanometer size effect. The surface of the NP is mainly covered by the NO2 group. The high-energy surface may play a role in triggering chemical decomposition. The vibration frequencies of the PETN NPs present a wider distribution than those of the gas and solid phase PETN, which will increase the probability of energy transfer to the molecules in the system and promote the decomposition of PETN. Our results provide a basic understanding from a molecular perspective to the energy properties of nano explosives.

scholarly journals Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning

2018 ◽  
Vol 14 (5) ◽  
pp. 2341-2352 ◽  
Author(s):  
Julian J. Kranz ◽  
Maximilian Kubillus ◽  
Raghunathan Ramakrishnan ◽  
O. Anatole von Lilienfeld ◽  
Marcus Elstner
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Tight Binding ◽  
Unsupervised Machine Learning ◽  
Repulsive Potentials

Theoretical study on six-fold coordinated silicon in silicophosphate compounds

2006 ◽  
Vol 84 (8) ◽  
pp. 1024-1030 ◽  
Author(s):  
Hassan Rabaâ ◽  
Fatima Bkiri
Keyword(s):  
Density Functional ◽  
Tight Binding ◽  
High Energy ◽  
Functional Theory ◽  
Model Cluster ◽  
Hartree Fock ◽  
Ir Frequencies ◽  
The Stability ◽  
Remarkable Agreement ◽  
Cluster 2

Extended Hückel tight-binding (EHTB) calculations were performed on silicophosphate compounds with six-coordinated silicon. Speculative structures related to silicon coordination in SiP2O7 are reported. To account for the particular structural distortion caused by the presence of SiO6 in the silicon pyrophosphate, it is important to examine how the stability and the band gap of the extended structure of SiP2O7 are affected. Different theoretical tools are used (EHTB, ab initio Hartree–Fock, and density functional theory DFT-B3LYP). To obtain detailed descriptions of the incorporation of hexacoordinated silicon in this material, the band structures in SiP2O7 and [P2O7]4– were analyzed. It seems that the diffuse orbitals of silicon and the high energy of the Si 3p orbital lead to higher energy coordination and contribute to the breaking of the P-O-P bridge and the forming of a Si-O-P entity in this material. In addition, to provide more evidence of the existence of the octahedral silicon coordination in SiP2O7 (1), two model clusters [P4Si2O23H18] (2) and [P4Si2O19H10] (3) involving silicon atoms in octahedral and tetrahedral sites were investigated using Hartree–Fock and DFT theories. A remarkable agreement between calculated and experimental bond lengths for Si—O and P—O is obtained using the DFT calculation. The model cluster 2 corroborates the structural change in the Si-O-P and P-O-P fragments seen in 1. The IR vibrational frequencies are calculated for both model clusters and are predicted to shift towards lower frequencies in the octahedral Si sites, which is consistent with experimental data.Key words: silicophosphate, SiO6, band structure, tight-binding calculations, Hartree-Fock, DFT, B3LYP, model cluster, IR frequencies.

Predicting Atom Types of Anatase TiO2 Nanoparticles with Machine Learning

2021 ◽  
Vol 880 ◽  
pp. 89-94
Author(s):  
Hasan Kurban ◽  
Mustafa Kurban ◽  
Parichit Sharma ◽  
Mehmet M. Dalkilic
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Nearest Neighbor ◽  
Tight Binding ◽  
Learning Performance ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Different Temperatures ◽  
Geometrical Data ◽  
Temperature Levels

Machine learning (ML) has recently made a major contribution to the fields of Material Science (MS). In this study, ML algorithms are used to learn atoms types over structural geometrical data of anatase TiO2 nanoparticles produced at different temperature levels with the density-functional tight-binding method (DFTB). Especially for this work, Random Forest (RF), Decision Trees (DT), K-Nearest Neighbor (KNN), Naïve Bayes (NB), which are among the most popular ML algorithms, were run to learn titanium (Ti) and oxygen (O) atoms. RF outperforms other algorithms, almost succeeding in learning this skewed data set close to perfect. The use of ML algorithms with datasets compatible with its mathematical design increases their learning performance. Therefore, we find it remarkable that a certain type of ML algorithm performs almost perfectly. Because it can help material scientists predict the behavior and structural and electronic properties of atoms at different temperatures.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

Vapour Pressure Isotope Effect on Evaporation from Pure Organic Phases - a PIMD Approach

2020 ◽  
Author(s):  
Luis Vasquez ◽  
Agnieszka Dybala-Defratyka
Keyword(s):  
Path Integral ◽  
Vapour Pressure ◽  
Density Functional ◽  
Isotope Effects ◽  
Tight Binding ◽  
Decomposition Analysis ◽  
Energy Decomposition Analysis ◽  
Special Importance ◽  
Non Covalent Interactions ◽  
Covalent Interactions

<p></p><p>Very often in order to understand physical and chemical processes taking place among several phases fractionation of naturally abundant isotopes is monitored. Its measurement can be accompanied by theoretical determination to provide a more insightful interpretation of observed phenomena. Predictions are challenging due to the complexity of the effects involved in fractionation such as solvent effects and non-covalent interactions governing the behavior of the system which results in the necessity of using large models of those systems. This is sometimes a bottleneck and limits the theoretical description to only a few methods.<br> In this work vapour pressure isotope effects on evaporation from various organic solvents (ethanol, bromobenzene, dibromomethane, and trichloromethane) in the pure phase are estimated by combining force field or self-consistent charge density-functional tight-binding (SCC-DFTB) atomistic simulations with path integral principle. Furthermore, the recently developed Suzuki-Chin path integral is tested. In general, isotope effects are predicted qualitatively for most of the cases, however, the distinction between position-specific isotope effects observed for ethanol was only reproduced by SCC-DFTB, which indicates the importance of using non-harmonic bond approximations.<br> Energy decomposition analysis performed using the symmetry-adapted perturbation theory (SAPT) revealed sometimes quite substantial differences in interaction energy depending on whether the studied system was treated classically or quantum mechanically. Those observed differences might be the source of different magnitudes of isotope effects predicted using these two different levels of theory which is of special importance for the systems governed by non-covalent interactions.</p><br><p></p>

Quasi-Barrierless Submolecular Motion in Mechanically Interlocked Carbon Nanotubes

2020 ◽  
Author(s):  
Julia Villalva ◽  
Belén Nieto-Ortega ◽  
Manuel Melle-Franco ◽  
Emilio Pérez
Keyword(s):  
Density Functional ◽  
Close Contact ◽  
Tight Binding ◽  
Energetic Cost ◽  
Molecular Fragments ◽  
Activation Barriers ◽  
Flat Surfaces ◽  
Different Types ◽  
Atomically Flat ◽  
Derivatives Of

The motion of molecular fragments in close contact with atomically flat surfaces is still not fully understood. Does a more favourable interaction imply a larger barrier towards motion even if there are no obvious minima? Here, we use mechanically interlocked rotaxane-type derivatives of SWNTs (MINTs) featuring four different types of macrocycles with significantly different affinities for the SWNT thread as models to study this problem. Using molecular dynamics, we find that there is no direct correlation between the interaction energy of the macrocycle with the SWNT and its ability to move along or around it. Density functional tight-binding calculations reveal small (<2.5 Kcal·mol-1) activation barriers, the height of which correlates with the commensurability of the aromatic moieties in the macrocycle with the SWNT. Our results show that macrocycles in MINTs rotate and translate freely around and along SWNTs at room temperature, with an energetic cost lower than the rotation around the C−C bond in ethane.<br>

Communication: Photoinduced Carbon Dioxide Binding with Surface-Functionalized Silicon Quantum Dots

2018 ◽  
Author(s):  
Oscar A. Douglas-Gallardo ◽  
Cristián Gabriel Sánchez ◽  
Esteban Vöhringer-Martinez
Keyword(s):  
Carbon Dioxide ◽  
Quantum Dots ◽  
Atmospheric Carbon Dioxide ◽  
Density Functional ◽  
Tight Binding ◽  
Carbon Dioxide Concentration ◽  
Research Area ◽  
Silicon Quantum Dots ◽  
Dependent Model ◽  
Photoinduced Charge

<div> <div> <div> <p>Nowadays, the search of efficient methods able to reduce the high atmospheric carbon dioxide concentration has turned into a very dynamic research area. Several environmental problems have been closely associated with the high atmospheric level of this greenhouse gas. Here, a novel system based on the use of surface-functionalized silicon quantum dots (sf -SiQDs) is theoretically proposed as a versatile device to bind carbon dioxide. Within this approach, carbon dioxide trapping is modulated by a photoinduced charge redistribution between the capping molecule and the silicon quantum dots (SiQDs). Chemical and electronic properties of the proposed SiQDs have been studied with Density Functional Theory (DFT) and Density Functional Tight-Binding (DFTB) approach along with a Time-Dependent model based on the DFTB (TD-DFTB) framework. To the best of our knowledge, this is the first report that proposes and explores the potential application of a versatile and friendly device based on the use of sf -SiQDs for photochemically activated carbon dioxide fixation. </p> </div> </div> </div>

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