Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and (7) knowledge extraction and reasoning from scientific literature. Each team submission is presented in a summary table with links to the code and as brief papers in the appendix. Beyond team results, we discuss the hackathon event and its hybrid format, which included physical hubs in Toronto, Montreal, San Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable local and virtual collaboration. Overall, the event highlighted significant improvements in LLM capabilities since the previous year's hackathon, suggesting continued expansion of LLMs for applications in materials science and chemistry research. These outcomes demonstrate the dual utility of LLMs as both multipurpose models for diverse machine learning tasks and platforms for rapid prototyping custom applications in scientific research.

Open-Source Fermionic Neural Networks with Ionic Charge Initialization

Finding accurate solutions to the electronic Schr\"odinger equation plays an important role in discovering important molecular and material energies and characteristics. Consequently, solving systems with large numbers of electrons has become increasingly important. Variational Monte Carlo (VMC) methods, especially those approximated through deep neural networks, are promising in this regard. In this paper, we aim to integrate one such model called the FermiNet, a post-Hartree-Fock (HF) Deep Neural Network (DNN) model, into a standard and widely used open source library, DeepChem. We also propose novel initialization techniques to overcome the difficulties associated with the assignment of excess or lack of electrons for ions.

Differentiable Chemical Physics by Geometric Deep Learning for Gradient-based Property Optimization of Mixtures

Chemical mixtures, satisfying multi-objective performance metrics and constraints, enable their use in chemical processes and electrochemical devices. In this work, we develop a differentiable chemical-physics framework for modeling chemical mixtures, DiffMix, where geometric deep learning (GDL) is leveraged to map from molecular species, compositions and environment conditions, to physical coefficients in the mixture physics laws. In particular, we extend mixture thermodynamic and transport laws by creating learnable physical coefficients, where we use graph neural networks as the molecule encoder and enforce component-wise permutation-invariance. We start our model evaluations with thermodynamics of binary mixtures, and further benchmarked multicomponent electrolyte mixtures on their transport properties, in order to test the model generalizability. We show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, we demonstrate the efficient optimization of electrolyte transport properties, built on the gradient obtained using DiffMix auto-differentiation. Our simulation runs are then backed up by the data generated by a robotic experimentation setup, Clio. By combining mixture physics and GDL, DiffMix expands the predictive modeling methods for chemical mixtures and provides low-cost optimization approaches in large chemical spaces.

Open Source Infrastructure for Differentiable Density Functional Theory

Learning exchange correlation functionals, used in quantum chemistry calculations, from data has become increasingly important in recent years, but training such a functional requires sophisticated software infrastructure. For this reason, we build open source infrastructure to train neural exchange correlation functionals. We aim to standardize the processing pipeline by adapting state-of-the-art techniques from work done by multiple groups. We have open sourced the model in the DeepChem library to provide a platform for additional research on differentiable quantum chemistry methods.