Improving the accuracy of atomistic simulations of the electrochemical interface
Atomistic simulation of the electrochemical double layer is an ambitious undertaking,
requiring quantum mechanical description of electrons, phase space sampling of liquid …
requiring quantum mechanical description of electrons, phase space sampling of liquid …
How machine learning can accelerate electrocatalysis discovery and optimization
Advances in machine learning (ML) provide the means to bypass bottlenecks in the
discovery of new electrocatalysts using traditional approaches. In this review, we highlight …
discovery of new electrocatalysts using traditional approaches. In this review, we highlight …
Roadmap on ferroelectric hafnia-and zirconia-based materials and devices
Ferroelectric hafnium and zirconium oxides have undergone rapid scientific development
over the last decade, pushing them to the forefront of ultralow-power electronic systems …
over the last decade, pushing them to the forefront of ultralow-power electronic systems …
How to validate machine-learned interatomic potentials
Machine learning (ML) approaches enable large-scale atomistic simulations with near-
quantum-mechanical accuracy. With the growing availability of these methods, there arises …
quantum-mechanical accuracy. With the growing availability of these methods, there arises …
Combining machine learning and many-body calculations: coverage-dependent adsorption of CO on Rh (111)
Adsorption of carbon monoxide (CO) on transition-metal surfaces is a prototypical process in
surface sciences and catalysis. Despite its simplicity, it has posed great challenges to …
surface sciences and catalysis. Despite its simplicity, it has posed great challenges to …
Kohn–Sham accuracy from orbital-free density functional theory via Δ-machine learning
We present a Δ-machine learning model for obtaining Kohn–Sham accuracy from orbital-
free density functional theory (DFT) calculations. In particular, we employ a machine-learned …
free density functional theory (DFT) calculations. In particular, we employ a machine-learned …
Absolute standard hydrogen electrode potential and redox potentials of atoms and molecules: machine learning aided first principles calculations
Constructing a self-consistent first-principles framework that accurately predicts the
properties of electron transfer reactions through finite-temperature molecular dynamics …
properties of electron transfer reactions through finite-temperature molecular dynamics …
Data-efficient multifidelity training for high-fidelity machine learning interatomic potentials
J Kim, J Kim, J Kim, J Lee, Y Park… - Journal of the American …, 2024 - ACS Publications
Machine learning interatomic potentials (MLIPs) are used to estimate potential energy
surfaces (PES) from ab initio calculations, providing near-quantum-level accuracy with …
surfaces (PES) from ab initio calculations, providing near-quantum-level accuracy with …
Machine-learned acceleration for molecular dynamics in CASTEP
Machine learning (ML) methods are of rapidly growing interest for materials modeling, and
yet, the use of ML interatomic potentials for new systems is often more demanding than that …
yet, the use of ML interatomic potentials for new systems is often more demanding than that …
Quantum paraelectricity and structural phase transitions in strontium titanate beyond density functional theory
We demonstrate an approach for calculating temperature-dependent quantum and
anharmonic effects with beyond density-functional theory accuracy. By combining machine …
anharmonic effects with beyond density-functional theory accuracy. By combining machine …