1k citations · 4.1k across the 28 of their papers we have counts for
30 papers
wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows
Elena Gelžinytė, Simon Wengert, Tamás K. Stenczel +4
Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle t…
Nested sampling for physical scientists
Greg Ashton, Noam Bernstein, Johannes Buchner +20
We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey deve…
Structural transitions in dense disordered silicon from quantum-accurate ultra-large-scale simulations
Volker L. Deringer, Noam Bernstein, Gábor Csányi +3
Structurally disordered materials continue to pose fundamental questions, including that of how different disordered phases ("polyamorphs") can coexist and transform from one to an…
Impact of biaxial and uniaxial strain on VO
Darshana Wickramaratne, Noam Bernstein, I. I. Mazin
Using first-principles calculations we determine the role of compressive and tensile uniaxial and equibiaxial strain on the structural, electronic and magnetic properties of VO…
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…
De novo exploration and self-guided learning of potential-energy surfaces
Noam Bernstein, Gábor Csányi, Volker L. Deringer
Interatomic potential models based on machine learning (ML) are rapidly developing as tools for materials simulations. However, because of their flexibility, they require large fit…