Machine learning-accelerated discovery and design of electrode materials and electrolytes for lithium ion batteries
With the development of artificial intelligence and the intersection of machine learning (ML)
and materials science, the reclamation of ML technology in the realm of lithium ion batteries …
and materials science, the reclamation of ML technology in the realm of lithium ion batteries …
How Does Structural Disorder Impact Heterogeneous Catalysts? The Case of Ammonia Decomposition on Non-stoichiometric Lithium Imide
Among the many catalysts suggested for ammonia decomposition, Li2NH has been shown
to be quite promising. In the recent past, we have performed extensive ab initio-quality …
to be quite promising. In the recent past, we have performed extensive ab initio-quality …
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide
range of technological applications. However, predicting these quantities at first-principles …
range of technological applications. However, predicting these quantities at first-principles …
Anion-derived contact ion pairing as a unifying principle for electrolyte design
Enabling new electrochemical technologies requires systems that can operate under ever-
more demanding conditions, and progress in energy storage applications reveals tantalizing …
more demanding conditions, and progress in energy storage applications reveals tantalizing …
Thermophysical properties of Molten FLiNaK: A moment tensor potential approach
Fluoride salts demonstrate significant potential for applications in next-generation nuclear
reactors, necessitating a comprehensive understanding of their thermophysical properties …
reactors, necessitating a comprehensive understanding of their thermophysical properties …
Machine-Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes
PWV Butler, R Hafizi, GM Day - The Journal of Physical Chemistry …, 2024 - ACS Publications
A primary challenge in organic molecular crystal structure prediction (CSP) is accurately
ranking the energies of potential structures. While high-level solid-state density functional …
ranking the energies of potential structures. While high-level solid-state density functional …
A dual-cutoff machine-learned potential for condensed organic systems obtained via uncertainty-guided active learning
Machine-learned potentials (MLPs) trained on ab initio data combine the computational
efficiency of classical interatomic potentials with the accuracy and generality of the first …
efficiency of classical interatomic potentials with the accuracy and generality of the first …
Transferability of datasets between Machine-Learning Interaction Potentials
With the emergence of Foundational Machine Learning Interatomic Potential (FMLIP)
models trained on extensive datasets, transferring data between different ML architectures …
models trained on extensive datasets, transferring data between different ML architectures …
Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly
F Zills, MR Schäfer, N Segreto… - The Journal of …, 2024 - ACS Publications
The field of machine learning potentials has experienced a rapid surge in progress, thanks
to advances in machine learning theory, algorithms, and hardware capabilities. While the …
to advances in machine learning theory, algorithms, and hardware capabilities. While the …
On the design space between molecular mechanics and machine learning force fields
A force field as accurate as quantum mechanics (QM) and as fast as molecular mechanics
(MM), with which one can simulate a biomolecular system efficiently enough and …
(MM), with which one can simulate a biomolecular system efficiently enough and …