1.9k citations · 2.2k across the 19 of their papers we have counts for
19 papers
Fast and Accurate Equivariant Foundation Models for Atomistic Simulation
Seán R. Kavanagh, Chuin Wei Tan, Menghang Wang +12
Machine-learned interatomic potentials (MLIPs) have emerged as a transformative tool for computational materials science and chemistry, with universal potentials trained on large a…
Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices
Taufeq Mohammed Razakh, Thomas Linker, Ye Luo +12
Light-matter dynamics in topological quantum materials enables ultralow-power, ultrafast devices. A challenge is simulating multiple field and particle equations for light, electro…
Coupled reaction and diffusion governing interface evolution in solid-state batteries
Jingxuan Ding, Laura Zichi, Matteo Carli +4
Understanding and controlling the atomistic-level reactions governing the formation of the solid-electrolyte interphase (SEI) is crucial for the viability of next-generation solid…
High-performance training and inference for deep equivariant interatomic potentials
Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak +11
Machine learning interatomic potentials, particularly those based on deep equivariant neural networks, have demonstrated state-of-the-art accuracy and computational efficiency in a…
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
Menghang Wang, Jingxuan Ding, Grace Xiong +8
In solid acid solid electrolytes CsHPO and CsHSO, mechanisms of fast proton conduction have long been debated and attributed to either local proton hopping or polyanion…
Atomistic evolution of active sites in multi-component heterogeneous catalysts
Cameron J. Owen, Lorenzo Russotto, Christopher R. O'Connor +4
Multi-component metal nanoparticles (NPs) are of paramount importance in the chemical industry, as most processes therein employ heterogeneous catalysts. While these multi-componen…
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
Blake R. Duschatko, Xiang Fu, Cameron Owen +4
We present a differentiable formalism for learning free energies that is capable of capturing arbitrarily complex model dependencies on coarse-grained coordinates and finite-temper…
A Recipe for Charge Density Prediction
Xiang Fu, Andrew Rosen, Kyle Bystrom +5
In density functional theory, charge density is the core attribute of atomic systems from which all chemical properties can be derived. Machine learning methods are promising in si…
Unified Differentiable Learning of Electric Response
Stefano Falletta, Andrea Cepellotti, Anders Johansson +4
Predicting response of materials to external stimuli is a primary objective of computational materials science. However, current methods are limited to small-scale simulations due…
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang +11
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as ``designer solvents'' a…
Learning Interatomic Potentials at Multiple Scales
Xiang Fu, Albert Musaelian, Anders Johansson +2
The need to use a short time step is a key limit on the speed of molecular dynamics (MD) simulations. Simulations governed by classical potentials are often accelerated by using a…
Accurate Surface and Finite Temperature Bulk Properties of Lithium Metal at Large Scales using Machine Learning Interaction Potentials
Mgcini Keith Phuthi, Archie Mingze Yao, Simon Batzner +4
The properties of lithium metal are key parameters in the design of lithium ion and lithium metal batteries. They are difficult to probe experimentally due to the high reactivity a…
Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size
Albert Musaelian, Anders Johansson, Simon Batzner +1
This work brings the leading accuracy, sample efficiency, and robustness of deep equivariant neural networks to the extreme computational scale. This is achieved through a combinat…
Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set
Cameron J. Owen, Steven B. Torrisi, Yu Xie +6
This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In exhaustive detail, we co…
Fast Uncertainty Estimates in Deep Learning Interatomic Potentials
Albert Zhu, Simon Batzner, Albert Musaelian +1
Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current appro…
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács +6
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic…
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
Albert Musaelian, Simon Batzner, Anders Johansson +4
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences.…
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner, Albert Musaelian, Lixin Sun +6
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations f…
Unsupervised landmark analysis for jump detection in molecular dynamics simulations
Leonid Kahle, Albert Musaelian, Nicola Marzari +1
Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of t…