5 papers
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials
William J. Baldwin, Ilyes Batatia, Martin Vondrák +4
Machine learning interatomic potentials (MLIPs) have become widely used tools in atomistic simulations. For much of the history of this field, the most commonly employed architectu…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
Spectral tuning of hyperbolic shear polaritons in monoclinic gallium oxide via isotopic substitution
Giulia Carini, Mohit Pradhan, Elena Gelzinyte +26
Hyperbolic phonon polaritons - hybridized modes arising from the ultrastrong coupling of infrared light to strongly anisotropic lattice vibrations in uniaxial or biaxial polar crys…
Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials
Philipp Pracht, Yuthika Pillai, Venkat Kapil +5
Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target for the computational investigation of molecular materials. Building o…
Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper during Chemical Vapor Deposition
Valentina Rein, Hao Gao, Hendrik H. Heenen +10
In recent years, liquid metal catalysts have emerged as a compelling choice for the controllable, large-scale, and high-quality synthesis of two-dimensional materials. At present,…