Chemical Properties from Graph Neural Network-Predicted Electron Densities

According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods to obtain important chemical properties from model-predicted electron densities. We introduce graph neural network architectural choices that provide physically relevant and useful electron density predictions. Despite not training to predict atomic charges, the model is able to predict atomic charges with an order of magnitude lower error than a sum of atomic charge densities. Similarly, the model predicts dipole moments with half the error of the sum of atomic charge densities method. We demonstrate that larger data sets lead to more useful predictions in these tasks. These results pave the way for an alternative path in atomistic machine learning, where data-driven approaches and existing physical methods are used in tandem to obtain a variety of chemical properties in an explainable and self-consistent manner

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\sim$120M systems from OC20, OC22, ANI-1x, and Transition-1x. We evaluate performance and generalization by fine-tuning over a diverse set of downstream tasks and datasets including: QM9, rMD17, MatBench, QMOF, SPICE, and MD22. JMP demonstrates an average improvement of 59% over training from scratch, and matches or sets state-of-the-art on 34 out of 40 tasks. Our work highlights the potential of pre-training strategies that utilize diverse data to advance property prediction across chemical domains, especially for low-data tasks

Application of RELAP5/Mod3.3 - Fluent coupling codes to CIRCE-HERO

This paper presents the work ongoing at the DICI (Dipartimento di Ingegneria Civile e Industriale) of the University of Pisa on the application of coupled methodology between Fluent CFD code and RELAP5/Mod3.3 system code. In particular, this methodology was applied to the LBE-water heat exchanger HERO, with the aim to analyse the performances of this component. The test section object of this study is installed inside the vessel S100 of the CIRCE facility, built at ENEA Brasimone Research Centre. In the proposed methodology the CFD code is adopted to simulate the LBE side of the HERO heat exchanger, whereas the secondary side (two-phase flow, water-vapour) is simulated by the STH code. In this procedure, the variables exchanged between the boundaries of the two codes are: the bulk temperature and heat transfer coefficient of the ascending water (in two-phase flow) obtained from RELAP5 and transferred to Fluent code; the wall temperature at the water side surface of the pipes is calculated by Fluent and passed to RELAP5 code. The coupling procedure was verified by comparing the obtained results with the analogous ones achieved with the RELAP5 stand-alone calculation, proving that the developed coupling methodology is reliable. Further, the coupled simulation allows to obtain more accurate information on the LBE side

Application of RELAP5/Mod3.3–Fluent coupling codes to CIRCE-HERO

This paper presents the work ongoing at the DICI (Dipartimento di Ingegneria Civile e Industriale) of the University of Pisa on the application of coupled methodology between Fluent CFD code and RELAP5/Mod3.3 system code. In particular, this methodology was applied to the LBE-water heat exchanger HERO, with the aim to analyse the performances of this component. The test section object of this study is installed inside the vessel S100 of the CIRCE facility, built at ENEA Brasimone Research Centre. In the proposed methodology the CFD code is adopted to simulate the LBE side of the HERO heat exchanger, whereas the secondary side (two-phase flow, water-vapour) is simulated by the STH code. In this procedure, the variables exchanged between the boundaries of the two codes are: the bulk temperature and heat transfer coefficient of the ascending water (in two-phase flow) obtained from RELAP5 and transferred to Fluent code; the wall temperature at the water side surface of the pipes is calculated by Fluent and passed to RELAP5 code. The coupling procedure was verified by comparing the obtained results with the analogous ones achieved with the RELAP5 stand-alone calculation, proving that the developed coupling methodology is reliable. Further, the coupled simulation allows to obtain more accurate information on the LBE side

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning

Surface phase diagrams are necessary for understanding surface chemistry in electrochemical catalysis, where a range of adsorbates and coverages exist at varying applied potentials. These diagrams are typically constructed using intuition, which risks missing complex coverages and configurations at potentials of interest. More accurate cluster expansion methods are often difficult to implement quickly for new surfaces. We adopt a machine learning approach to rectify both issues. Using a Gaussian process regression model, the free energy of all possible adsorbate coverages for surfaces is predicted for a finite number of adsorption sites. Our result demonstrates a rational, simple, and systematic approach for generating accurate free-energy diagrams with reduced computational resources. The Pourbaix diagram for the IrO₂(110) surface (with nine coverages from fully hydrogenated to fully oxygenated surfaces) is reconstructed using just 20 electronic structure relaxations, compared to approximately 90 using typical search methods. Similar efficiency is demonstrated for the MoS₂ surface

All solid-state lithium-sulfur battery using a glass-type P2S5-Li2S electrolyte: Benefits on anode kinetics

Lithium-sulfur (Li-S) batteries are promising candidates for next generation electrical energy storage d evices due to their high specific energy. Despite intense research, there are still a number of technical challenges in developing a high performance Li-S battery. To elucidate the issues, an all solid-state Li-S battery was fabricated using Li3PSz solid electrolyte. Most of the theoretical capacity of sulfur, 1600 mAhg-1 was attained in the initial discharge-charge cycles with a high coulombic efficiency approaching 99%. To verify the benefit of the solid state electrolyte, galvanostatic stripping-deposition tests were also carried out on a symmetrical Li/Li cell and compared with those of a liquid electrolyte (1M-lithium bis(trifluoromethane sulfonyl) imide (LiTFSI) in a mixture of 1,3-dioxolane (DOL)-diethoxyethane (DEE)). The kinetics and thermodynamics of the solid-state cell are discussed from the viewpoint of the charge transfer processes. This study demonstrates both the merits and drawbacks of using the solid sulfide electrolyte in a Li-S battery and facilitates the further improvement of this important high energy storage device

Open Challenges in Developing Generalizable Large Scale Machine Learning Models for Catalyst Discovery

The development of machine learned potentials for catalyst discovery has predominantly been focused on very specific chemistries and material compositions. While effective in interpolating between available materials, these approaches struggle to generalize across chemical space. The recent curation of large-scale catalyst datasets has offered the opportunity to build a universal machine learning potential, spanning chemical and composition space. If accomplished, said potential could accelerate the catalyst discovery process across a variety of applications (CO2 reduction, NH3 production, etc.) without additional specialized training efforts that are currently required. The release of the Open Catalyst 2020 (OC20) has begun just that, pushing the heterogeneous catalysis and machine learning communities towards building more accurate and robust models. In this perspective, we discuss some of the challenges and findings of recent developments on OC20. We examine the performance of current models across different materials and adsorbates to identify notably underperforming subsets. We then discuss some of the modeling efforts surrounding energy-conservation, approaches to finding and evaluating the local minima, and augmentation of off-equilibrium data. To complement the community's ongoing developments, we end with an outlook to some of the important challenges that have yet to be thoroughly explored for large-scale catalyst discovery.Comment: submitted to ACS Catalysi