1.6k citations · 4k across the 47 of their papers we have counts for
49 papers
How good are universal MLIPs for alloys? A benchmark is only as trustworthy as its controls
Gus L. W. Hart, Jacob N. Pursley
Universal machine-learned interatomic potentials (uMLIPs) are ranked by their formation-energy error against a DFT database, as if that database were ground truth, but independent…
POPSICLE: Benchmark Datasets for Segmentation and Localization in CryoET
Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens +6
Cryo-electron tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact cell…
eGAD! double descent is explained by Generalized Aliasing Decomposition
Mark K. Transtrum, Gus L. W. Hart, Tyler J. Jarvis +1
A central problem in data science is to use potentially noisy samples of an unknown function to predict values for unseen inputs. In classical statistics, predictive error is under…
Describe, Transform, Machine Learning: Feature Engineering for Grain Boundaries and Other Variable-Sized Atom Clusters
C. Braxton Owens, Nithin Mathew, Tyce W. Olaveson +5
Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to r…
Grain boundary solute segregation across the 5D space of crystallographic character
Lydia Harris Serafin, Ethan R. Cluff, Gus L. W. Hart +1
Solute segregation in materials with grain boundaries (GBs) has emerged as a popular method to thermodynamically stabilize nanocrystalline structures. However, the impact of varied…
Optimal Routes to Ultrafast Polarization Reversal in Ferroelectric LiNbO3
R. Tanner Hardy, Conrad Rosenbrock, Gus L. W. Hart +1
We use the frozen phonon method to calculate the anharmonic potential energy surface and to model the ultrafast ferroelectric polarization reversal in LiNbO3 driven by intense puls…
Facet and energy predictions in grain boundaries: lattice matching and molecular dynamics
Bruno Dobrovolski, C. Braxton Owens, Gus L. W. Hart +2
Many material properties can be traced back to properties of their grain boundaries. Grain boundary energy (GBE), as a result, is a key quantity of interest in the analysis and mod…
Machine Learning Predictions of High-Curie-Temperature Materials
Joshua F. Belot, Valentin Taufour, Stefano Sanvito +1
Technologies that function at room temperature often require magnets with a high Curie temperature, , and can be improved with better materials. Discovering magnetic…
A set of moment tensor potentials for zirconium with increasing complexity
Yu Luo, Jason A. Meziere, German D. Samolyuk +3
Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here, we propose a hybrid small…
Accelerating Training of MLIPs Through Small-Cell Training
Jason A. Meziere, Yu Luo, Yi Zia +3
While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods…
Tensor-reduced atomic density representations
James P. Darby, Dávid P. Kovács, Ilyes Batatia +4
Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials,…
Relative Grain Boundary Energies from Triple Junction Geometry: Limitations to Assuming the Herring Condition in Nanocrystalline Thin Films
Matthew J. Patrick, Gregory S. Rohrer, Ooraphan Chirayutthanasak +5
Grain boundary character distributions (GBCD) are routinely measured from bulk microcrystalline samples by electron backscatter diffraction (EBSD) and serial sectioning can be used…
Examination of computed aluminum grain boundary structures and interface energies that span the 5D space of crystallographic character
Eric R. Homer, Gus L. W. Hart, C. Braxton Owens +3
The space of possible grain boundary structures is vast, with 5 macroscopic, crystallographic degrees of freedom that define the character of a grain boundary. While numerous datas…
Effectiveness of smearing and tetrahedron methods: best practices in DFT codes
Jeremy J. Jorgensen, Gus L. W. Hart
Density functional theory (DFT) codes are commonly treated as a "black box" in high-throughput screening of materials, with users opting for the default values of the input paramet…
A general algorithm for calculating irreducible Brillouin zones
Jeremy J. Jorgensen, John E. Christensen, Tyler J. Jarvis +1
Calculations of properties of materials require performing numerical integrals over the Brillouin zone (BZ). Integration points in density functional theory codes are uniformly spr…
The AFLOW Library of Crystallographic Prototypes: Part 3
David Hicks, Michael J. Mehl, Marco Esters +5
The AFLOW Library of Crystallographic Prototypes has been extended to include a total of 1,100 common crystal structural prototypes (510 new ones with Part 3), comprising all of th…
Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams
Conrad W. Rosenbrock, Konstantin Gubaev, Alexander V. Shapeev +4
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tens…
Generalized Regular k-point Grid Generation On The Fly
Wiley S. Morgan, John E. Christensen, Parker K. Hamilton +4
In the DFT community, it is common practice to use regular k-point grids (Monkhorst-Pack, MP) for Brillioun zone integration. Recently Wisesa et. al.\cite{wisesa2016efficient} and…
A robust algorithm for -point grid generation and symmetry reduction
Gus L. W. Hart, Jeremy J. Jorgensen, Wiley S. Morgan +1
We develop an algorithm for i) computing generalized regular -point grids, ii) reducing the grids to their symmetrically distinct points, and iii) mapping the reduced grid point…
Machine-learned multi-system surrogate models for materials prediction
Chandramouli Nyshadham, Matthias Rupp, Brayden Bekker +6
Surrogate machine-learning models are transforming computational materials science by predicting properties of materials with the accuracy of ab initio methods at a fraction of the…
Structural Characterization of Grain Boundaries and Machine Learning of Grain Boundary Energy and Mobility
Conrad W. Rosenbrock, Jonathan L. Priedeman, Gus L. W. Hart +1
Recent advances in the numerical representation of materials opened the way for successful machine learning of grain boundary (GB) energies and the classification of GB mobility an…
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials
Konstantin Gubaev, Evgeny V. Podryabinkin, Gus L. W. Hart +1
We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for th…
The AFLOW Library of Crystallographic Prototypes: Part 2
David Hicks, Michael J. Mehl, Eric Gossett +5
Materials discovery via high-throughput methods relies on the availability of structural prototypes, which are generally decorated with varying combinations of elements to produce…
Efficiency of Generalized Regular k-point Grids
Wiley S. Morgan, Jeremy J. Jorgensen, Bret C. Hess +1
Most DFT practitioners use regular grids (Monkhorst-Pack, MP) for integrations in the Brillioun zone. Although regular grids are the natural choice and easy to generate, more gener…