345 citations · 1.7k across the 28 of their papers we have counts for
31 papers
Oxygen K-edge X-ray Absorption Spectroscopy Database for NMC811 Layered Cathode Materials
Jian He, Bowen Xiu, Feng Ryan Wang +2
X-ray absorption spectroscopy (XAS) probes the local chemical environment of the absorbing atom and is one of the most powerful characterization techniques for battery materials. H…
Dyna-Mat: End-to-end benchmarking of foundation machine learning interatomic potentials in finite-temperature ensembles
Mikołaj J. Gawkowski, Nongnuch Artrith, Silvia Bonfanti +14
Foundation machine learning interatomic potentials (MLIPs) are increasingly being used as drop-in replacements for first-principles calculations, enabling simulations of materials…
Building a physics-aware AI ecosystem for solid-state hydrogen storage materials
Seong-Hoon Jang, Yiwen Yao, Chuanyu Liu +66
Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evo…
Direct Simulation of LiNi0.8Mn0.1Co0.1O2 Transport Properties Using an Efficient and Accurate Machine Learning Potential
Jian He, Constantijn H. J. A. van de Wetering, Rolande W. Nolsen +1
The rate capability of layered lithium nickel manganese cobalt oxide (NMC) cathode materials plays a decisive role in high-power applications such as fast charging, necessitating a…
Complex magnetic interactions in geometrically frustrated TbOF
Pim Witte, Denis Sheptyakov, Elsa Lhotel +5
We have identified TbOF as a unique frustrated and mixed-anion lattice, hosting unconventional magnetism. By means of magnetization, specific heat and neutron diffraction measureme…
Synthesis of epitaxial TaO thin films on AlO by suboxide molecular-beam epitaxy and thermal laser epitaxy
Yorick A. Birkhölzer, Anna S. Park, Noah Schnitzer +28
Tantalum dioxide (TaO2) is a metastable tantalum compound. Here, we report the epitaxial stabilization of TaO2 on Al2O3 (1-102) (r-plane sapphire) substrates using suboxide molecul…
Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials
Riccardo Farris, Emanuele Telari, Nongnuch Artrith +2
Neural-network-based machine learning interatomic potentials have emerged as powerful tools for predicting atomic energies and forces, enabling accurate and efficient simulations i…
A practical guide to machine learning interatomic potentials -- Status and future
Ryan Jacobs, Dane Morgan, Siamak Attarian +27
The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not exper…
Mechanistic Insights into the Oxygen Evolution Reaction on Nickel-Doped Barium Titanate via Machine Learning-Accelerated Simulations
Kajjana Boonpalit, Nongnuch Artrith
Electrocatalytic water splitting, which produces hydrogen and oxygen through water electrolysis, is a promising method for generating renewable, carbon-free alternative fuels. Howe…
Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation
In Won Yeu, Annika Stuke, Jon L. pez-Zorrilla +5
Artificial neural network (ANN) potentials enable highly accurate atomistic simulations of complex materials at unprecedented scales. Despite their promise, training ANN potentials…
Highly Antioxidative Lithium Salt Enables High-Voltage Ether Electrolyte for Lithium Metal Battery
Jian He, Shihan Qi, Jianmin Ma +1
Ether-based electrolytes exhibit excellent compatibility with Li metal anodes, but their instability at high voltages limits their use in high-voltage Li metal batteries. To addres…
Machine Learning Potential Powered Insights into the Mechanical Stability of Amorphous Li-Si Alloys
Zixiong Wei, Nongnuch Artrith
Understanding the mechanical properties of solid-state materials at the atomic scale is crucial for developing novel materials. For example, amorphous LiSi alloys are attractive an…
Overcoming the Size Limit of First Principles Molecular Dynamics Simulations with an In-Distribution Substructure Embedding Active Learner
Lingyu Kong, Jielan Li, Lixin Sun +7
Large-scale first principles molecular dynamics are crucial for simulating complex processes in chemical, biomedical, and materials sciences. However, the unfavorable time complexi…
Constructing and Compressing Global Moment Descriptors from Local Atomic Environments
Vahe Gharakhanyan, Max Aalto, Aminah Alsoulah +2
Local atomic environment descriptors (LAEDs) are used in the materials science and chemistry communities, for example, for the development of machine learning interatomic potential…
Atomic Insights into the Oxidative Degradation Mechanisms of Sulfide Solid Electrolytes
Chuntian Cao, Matthew R. Carbone, Cem Komurcuoglu +15
Electrochemical degradation of solid electrolytes is a major roadblock in the development of solid-state batteries, and the formed solid-solid interphase (SSI) plays a key role in…
ænet-PyTorch: a GPU-supported implementation for machine learning atomic potentials training
Jon Lopez-Zorrilla, Xabier M. Aretxabaleta, Inwon Yue +3
In this work, we present ænet-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials. Developed as an extensio…
Simulated sulfur K-edge X-ray absorption spectroscopy database of lithium thiophosphate solid electrolytes
Haoyue Guo, Matthew R. Carbone, Chuntian Cao +9
X-ray absorption spectroscopy (XAS) is a premier technique for materials characterization, providing key information about the local chemical environment of the absorber atom. In t…
Ultrafast X-ray imaging of the light-induced phase transition in VO2
Allan S. Johnson, Daniel Pérez-Salinas, Khalid M. Siddiqui +26
Using light to control transient phases in quantum materials is an emerging route to engineer new properties and functionality, with both thermal and non-thermal phases observed ou…
AI-Aided Mapping of the Structure-Composition-Conductivity Relationships of Glass-Ceramic Lithium Thiophosphate Electrolytes
Haoyue Guo, Qian Wang, Alexander Urban +1
Lithium thiophosphates (LPS) with the composition (LiS)(PS) are among the most promising prospective electrolyte materials for solid-state batteries (SSBs),…
Understanding the onset of surface degradation in LiNiO2 cathodes
Xinhao Li, Qian Wang, Haoyue Guo +2
Nickel-based layered oxides offer an attractive platform for the development of energy-dense cobalt-free cathodes for lithium-ion batteries but suffer from degradation via oxygen g…
AENET-LAMMPS and AENET-TINKER: Interfaces for Accurate and Efficient Molecular Dynamics Simulations with Machine Learning Potentials
Michael S. Chen, Tobias Morawietz, Hideki Mori +2
Machine learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the comp…
Data-driven Approach to Parameterize SCAN+U for an Accurate Description of 3d Transition Metal Oxide Thermochemistry
Nongnuch Artrith, José Antonio Garrido Torres, Alexander Urban +1
Semi-local DFT methods exhibit significant errors for the phase diagrams of transition-metal oxides that are caused by an incorrect description of molecular oxygen and the large se…
Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations
April M. Miksch, Tobias Morawietz, Johannes Kästner +2
Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventio…
Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning
Nongnuch Artrith, Zhexi Lin, Jingguang G. Chen
Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrat…