Investigation of the Effect of Functional Group Substitutions on the Gas-Phase Electron Affinities and Ionization Energies of Room-Temperature Ionic Liquids Ions using Density Functional Theory

The cathodic and anodic stabilities of room-temperature ionic liquids (ILs) are important factors in their applications in electrochemical devices. In this work, we investigated the electron affinities of cations and ionization energies of anions for ionic liquids by density functional theory (DFT) calculations at the B3LYP/6-311+G(2d,p)//B3LYP/6-31+G(d) level. Over 200 unique cations and anions, formed from a set of six base cation structures, three base anion structures, and seven functional groups, were investigated. We find the trends in calculated EAs of alkylated cations and IEs of alkylated anions to be in good agreement with observed experimental trends in relative cathodic and anodic stabilities of various ILs. In addition, we also investigated the effect that functional group substitution at distinct positions in the ions have on the EA of the 1,2,3-trimethylimidazolium cation and the IE of the PF5CF3 anion. The overall impact on the EA or IE can be explained by the known electron-donating and electron-withdrawing inductive and resonance effects of the attached functional group, and the relative strength of the effect depends on the substitution position.DuPont MIT AllianceNational Science Foundation (U.S.) (TeraGrid resouces

From the computer to the laboratory: materials discovery and design using first-principles calculations

The development of new technological materials has historically been a difficult and time-consuming task. The traditional role of computation in materials design has been to better understand existing materials. However, an emerging paradigm for accelerated materials discovery is to design new compounds in silico using first-principles calculations, and then perform experiments on the computationally designed candidates. In this paper, we provide a review of ab initio computational materials design, focusing on instances in which a computational approach has been successfully applied to propose new materials of technological interest in the laboratory. Our examples include applications in renewable energy, electronic, magnetic and multiferroic materials, and catalysis, demonstrating that computationally guided materials design is a broadly applicable technique. We then discuss some of the common features and limitations of successful theoretical predictions across fields, examining the different ways in which first-principles calculations can guide the final experimental result. Finally, we present a future outlook in which we expect that new models of computational search, such as high-throughput studies, will play a greater role in guiding materials advancements

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals. We demonstrate that the MEGNet models outperform prior ML models such as the SchNet in 11 out of 13 properties of the QM9 molecule data set. Similarly, we show that MEGNet models trained on $\sim 60,000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set. We present two new strategies to address data limitations common in materials science and chemistry. First, we demonstrate a physically-intuitive approach to unify four separate molecular MEGNet models for the internal energy at 0 K and room temperature, enthalpy and Gibbs free energy into a single free energy MEGNet model by incorporating the temperature, pressure and entropy as global state inputs. Second, we show that the learned element embeddings in MEGNet models encode periodic chemical trends and can be transfer-learned from a property model trained on a larger data set (formation energies) to improve property models with smaller amounts of data (band gaps and elastic moduli)

Predicting the Volumes of Crystals

New crystal structures are frequently derived by performing ionic substitutions on known crystal structures. These derived structures are then used in further experimental analysis, or as the initial guess for structural optimization in electronic structure calculations, both of which usually require a reasonable guess of the lattice parameters. In this work, we propose two lattice prediction schemes to improve the initial guess of a candidate crystal structure. The first scheme relies on a one-to-one mapping of species in the candidate crystal structure to a known crystal structure, while the second scheme relies on data-mined minimum atom pair distances to predict the crystal volume of the candidate crystal structure and does not require a reference structure. We demonstrate that the two schemes can effectively predict the volumes within mean absolute errors (MAE) as low as 3.8% and 8.2%. We also discuss the various factors that may impact the performance of the schemes. Implementations for both schemes are available in the open-source pymatgen software.Comment: 8 figures, 2 table

Accurate Force Field for Molybdenum by Machine Learning Large Materials Data

In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods still do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including energies, forces, stresses, elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large-scale, long-time scale simulations.Comment: 25 pages, 9 figure