Parameterization of a reactive force field using a Monte Carlo algorithm

Abstract Parameterization of a Molecular Dynamics force field is essential in realistically modelling the physico-chemical processes involved in a molecular system. This step is often challenging when the equations involved in describing the force field are complicated as well as when the parameters are mostly empirical. ReaxFF is one such reactive force field which uses hundreds of parameters to describe the interactions between atoms. The optimization of the parameters in ReaxFF is done such that the the properties predicted by ReaxFF matches with a set of quantum chemical or experimental data. Usually, the optimization of the parameters is done by an inefficient single parameter parabolic-search algorithm. In this study, we use a robust Metropolis Monte-Carlo algorithm with Simulated Annealing (MMC-SA) to search for the optimum parameters for the ReaxFF force field in a high-dimensional parameter space. The optimization is done against a set of quantum chemical data for M gSO 4 hydrates. The optimized force field reproduced the chemical structures, the Equations of State and the water binding curves of M gSO 4 hydrates. The transferability test of the ReaxFF force field shows the extend of transferability for a particular molecular system. This study points out that the ReaxFF force field is not indefinitely transferable

The ReaxFF reactive force-field : development, applications and future directions

The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method

ReaxFF parameter optimization with Monte-Carlo and evolutionary algorithms : guidelines and insights

ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be optimized by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular, genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, that is, Monte-Carlo force field optimizer (MCFF) and covariance matrix adaptation evolutionary strategy (CMA-ES). In this work, CMA-ES, MCFF, and a GA method (OGOLEM) are systematically compared using three training sets from the literature. By repeating optimizations with different random seeds and initial parameter guesses, it is shown that a single optimization run with any of these methods should not be trusted blindly: nonreproducible, poor or premature convergence is a common deficiency. GA shows the smallest risk of getting trapped into a local minimum, whereas CMA-ES is capable of reaching the lowest errors for two-third of the cases, although not systematically. For each method, we provide reasonable default settings, and our analysis offers useful guidelines for their usage in future work. An important side effect impairing parameter optimization is numerical noise. A detailed analysis reveals that it can be reduced, for example, by using exclusively unambiguous geometry optimization in the training set. Even without this noise, many distinct near-optimal parameter vectors can be found, which opens new avenues for improving the training set and detecting overfitting artifacts

Parameterization of a reactive force field using a Monte Carlo algorithm

Parameterization of a molecular dynamics force field is essential in realistically modeling the physicochemical processes involved in a molecular system. This step is often challenging when the equations involved in describing the force field are complicated as well as when the parameters are mostly empirical. ReaxFF is one such reactive force field which uses hundreds of parameters to describe the interactions between atoms. The optimization of the parameters in ReaxFF is done such that the properties predicted by ReaxFF matches with a set of quantum chemical or experimental data. Usually, the optimization of the parameters is done by an inefficient single-parameter parabolic-search algorithm. In this study, we use a robust metropolis Monte-Carlo algorithm with simulated annealing to search for the optimum parameters for the ReaxFF force field in a high-dimensional parameter space. The optimization is done against a set of quantum chemical data for MgSO4 hydrates. The optimized force field reproduced the chemical structures, the equations of state, and the water binding curves of MgSO4 hydrates. The transferability test of the ReaxFF force field shows the extend of transferability for a particular molecular system. This study points out that the ReaxFF force field is not indefinitely transferable

LEVERAGING INFORMATICS FOR ACCELERATING THE DISCOVERY OF MATERIALS

The application of materials informatics for the rational design of materials has been inspired by the increasing number of examples of success of machine learning in many fields, and it has been facilitated by the greater access to computational resources, the advances in algorithms and the growing open-source code community. This thesis presents two ways in which we have advanced the field of computational materials science through materials informatics. A promising application of materials informatics to materials science is the development of machine-learned interatomic potentials models that are orders of magnitude faster than ab initio methods such as density functional theory and can be nearly as accurate. However, these models are typically orders of magnitude slower than physics-derived models such as the embedded atom method (EAM), and they usually do not generalize well. We present a supervised machine learning approach for developing interatomic potential models to simulate atomic systems at large time and length scales from ab initio data. The models developed with our symbolic regression algorithm are computationally fast, simple (and interpretable), accurate, and transferrable. A reason for the success of our algorithm is that it learns models using a physics-informed hypothesis space. Another important component of our algorithm is the minimization of a multi-objective cost function to search simple, accurate and fast interatomic potential models. We first demonstrate our approach for elemental Cu, and then show how the models discovered for Cu transfer well to other fcc transition metals close to Cu on the periodic table. Then, we demonstrate how our algorithm can be used to discover new functional forms for the fcc transition metals close to Cu on the periodic table, benefiting from the information encoded in known models as a seed to the search. The machine learning interatomic potential models developed with our approach are 2-3 orders of magnitude faster than other machine learned potentials, they are on average one order of magnitude simpler than EAM-type models, and their transferability is at least as good as that of other EAM-type models. In addition, their simplicity opens the door for studying their functional forms to possibly gain insights into the atomic systems. This thesis also addresses the need for a database of atomically precise nanoclusters at the density functional theory level of accuracy. Our approach used a genetic algorithm to identify low-energy clusters, and to our knowledge, it constitutes the largest database of atomically precise nanoclusters at the level of accuracy of density functional theory. This database can inform studies that aim to design clusters for a variety of applications, it can be used to train machine learning models, or it can be used as a benchmark for other studies

Molecular simulation of CO2 capture using hydrotalcite

The excessive CO2 emissions generated by human activities are the main factor resulting in climate change. A likely increase of 2ºC in the global average temperature is predicted to induce sea level rise and extreme weather events. Carbon Capture, Utilisation and Storage technologies will play a pivotal role in reducing these emissions and lessening the negative impacts. Currently, the most mature technology for carbon capture is chemical absorption using solvents such as mono-ethanolamine (MEA). However, this technology has the disadvantages of high energy requirements, the use of corrosive substances and elevated cost. The search for alternative cheaper and reliable capture methods is ongoing. Adsorption-based post-combustion carbon capture (PCC) has shown promising results, requiring less energy and using innocuous materials. Nevertheless, the development of adequate adsorbent materials and efficient process design for large scale implementation is still in the early stages. In this work, we focus on adsorption-based PCC. After carrying out an extensive literature review in the advancements on adsorption carbon capture from the experimental and molecular simulation perspectives, and a survey of the bench and pilot-scale projects around the world using this technology, we selected hydrotalcites (HTs) as a potential adsorbent for capturing CO2 from gas-fired power plant flue gases. HTs are better suited to work at the desired temperature (200ºC) in contrast with other adsorbent materials such as zeolites and activated carbon. In addition, they exhibit high CO2 selectivity and are widely available. The main challenge for their large-scale implementation is their relatively low adsorption capacity in contrast with chemical solvents. Since their performance is influenced by their composition, synthesis, and operational conditions, an experimental approach is impractical, thus molecular simulations were employed. Molecular simulations enable systematic studies without the need for columns settings and with the appropriate tools, in less time. To the best knowledge of the author, this is the first work employing the ReaxFF method for studying CO2 capture using HT as adsorbents. This molecular simulation method allows the simulation of the formation of chemical bonds, even for large and complex systems as the HT. This study is the first step towards gaining a better understanding of CO2 capture on HT at molecular level considering HT calcination, chemisorption and physisorption. First, we developed a Mg-Al-CO_3 HT structure geometry with Density Functional Theory calculations. The results showed that the developed structure lattice parameters agreed with experimental measurements. Next, we developed a specialised reactive force field (FF) and employed it for simulating the calcination process HT undergo for activation with molecular dynamics (MD) simulations. To the knowledge of the authors, this is the first FF capable of working with this HT structure. The FF generated with the Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) which had the lowest error function value was employed to carry out the calcination MD simulations. Finally, we carried out CO_2 adsorption studies with Grand Canonical Monte Carlo (GCMC) simulations at 200ºC and 1 bar to reflect PCC settings. The MD simulations for analysing HT during calcination showed a similar decomposition trend as the one reported in the literature, starting with dehydration, a subsequent dihydroxylation, and finally a decarbonation, resulting in a mixed metallic oxides structure. For validation, we compared the surface area of the calcined simulated HT against experimental data. The simulated calcined HT exhibited a surface area of 247.63 m2/g, which is in the expected range for calcined Mg-Al-CO3 HT surface area reported by experiments. The GCMC simulations of the adsorption studies showed the HT structure has an adsorption capacity of 34.78 molCO2/kgHT, which is much higher than reported in experimental studies. We attribute the disparity between the experimental and literature values to many factors related to the incipient nature of the generated FF and structure