Anharmonic Phonons in Graphene from First Principles

In this work, we develop a new flexible formalism to calculate anharmonic interatomic interactions from first principles at arbitrary order. Using the recently-developed slave-mode basis, we Taylor-expand the potential with a minimal number of independent coefficients. The anharmonic dynamical tensor, a higher-order generalization of the dynamical matrix in strain+reciprocal space, is calculated via a generalized frozen phonon methodology. We perform high-throughput calculations, emphasizing efficiency with multidimensional finite differences and Hellman-Feynman forces. Applying the methodology to graphene, we show convergence through fifth order terms. Our calculated force constants produce stress-strain curves, bond-length relaxations, and phonon spectra that agree well with those expected within DFT. We show that to fully capture anharmonic effects, long-range interactions must be included

Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions

Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous works focuses on generating predictions for only a fixed set of properties. Recent lines of research instead aim to explicitly learn the electronic structure via molecular wavefunctions from which other quantum chemical properties can directly be derived. While previous methods generate predictions as a function of only the atomic configuration, in this work we present an alternate approach that directly purposes basis dependent information to predict molecular electronic structure. The backbone of our model, Orbital Mixer, uses MLP Mixer layers within a simple, intuitive, and scalable architecture and achieves competitive Hamiltonian and molecular orbital energy and coefficient prediction accuracies compared to the state-of-the-art

A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published "in-house" efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties