Zr2SN2 thin films are transparent across most of the visible range, show an average refractive index of 2.95, and exhibit degenerate n-type conductivity with carrier density above 10^20 cm-3 and mobility above 8 cm2 V-1 s-1.
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16 Pith papers cite this work, alongside 148 external citations. Polarity classification is still indexing.
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CD-ARPES reveals p-wave OAM texture in chiral (TaSe4)2I where orbital polarization dominates spin, controllable by crystal handedness.
Ion irradiation creates a layer-disordered phase with planar defects in MnBi2Te4 that suppresses symmetry and anomalous Hall conductivity fivefold while magnetic order persists.
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
AQVolt26 is a new high-temperature halide dataset that improves universal ML interatomic potentials for distorted configurations while showing that near-equilibrium relaxation data is not universally helpful.
A modified Moss rule anchored to the optical absorption edge identifies (Hf,Zr)₂(S,Se)N₂ chalconitrides as ultra-high refractive index, UV-transparent materials surpassing TiO₂ and SiC.
INCARBench evaluates 19 LLMs on VASP INCAR configuration generation and repair, showing high semantic accuracy but lower scientific correctness especially for DFT+U, magnetism, and correlated materials.
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
Machine learning molecular dynamics simulations show that bilayer hexagonal boron nitride switches ferroelectric polarization via coherent single-domain rigid sliding in 5 ps and produces hysteresis loops qualitatively matching experiments.
CarNet is an equivariant graph-neural-network framework built on irreducible Cartesian natural tensors that predicts interatomic potentials and high-rank tensorial properties such as the elastic constant tensor.
First-principles calculations predict zero or negative linear compressibility in six MCN phases (M = Ag, Au, Cu) arising from a 'bamboo forest' geometry of rigid 1D chains with sparse packing.
A pretrained universal MLIP without spin or Hubbard-U corrections predicts the same chemisorbed S removal and O uptake under 15 eV O+/O2+ bombardment of WS2 as after three rounds of PBE+D3+U+spin fine-tuning, which still reduces energy/force MAE to 4.5 meV/atom and 0.076 eV/A.
SymADiT generates stable symmetric materials by enforcing Wyckoff-position and space-group constraints inside a latent generative model built on the prior ADiT architecture.
An affordable Arduino-based IoT setup generates real-time optical data for students to compare traversal, Bayesian, and deep learning methods in a self-driving experimental workflow.
On the Materials Project Battery Explorer dataset, CrabNet predicts gravimetric and volumetric capacity and average voltage from composition more accurately than MODNet or Magpie-random-forest.
PCA scatterplots misleadingly indicate clusters in Kuehneotherium teeth data, whereas t-SNE and persistent homology detect a ring-like one-dimensional manifold, backed by a generative model of uniform sampling from a unit circle whose cosine distances follow an arcsine distribution.
citing papers explorer
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A sulfonitride transparent conductive thin film with ultra-high refractive index
Zr2SN2 thin films are transparent across most of the visible range, show an average refractive index of 2.95, and exhibit degenerate n-type conductivity with carrier density above 10^20 cm-3 and mobility above 8 cm2 V-1 s-1.
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p-Wave Orbital Angular Momentum Texture in a Chiral Crystal
CD-ARPES reveals p-wave OAM texture in chiral (TaSe4)2I where orbital polarization dominates spin, controllable by crystal handedness.
-
Disorder-driven symmetry suppression by van der Waals planar defects in a magnetic topological insulator
Ion irradiation creates a layer-disordered phase with planar defects in MnBi2Te4 that suppresses symmetry and anomalous Hall conductivity fivefold while magnetic order persists.
-
Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs
Kernels from pretrained MLIP latent spaces outperform standard acquisition methods in active learning for reactive chemistry, reducing required labels by 38% for energy error and 28% for force error.
-
AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries
AQVolt26 is a new high-temperature halide dataset that improves universal ML interatomic potentials for distorted configurations while showing that near-equilibrium relaxation data is not universally helpful.
-
A modified Moss rule highlights underexplored classes of high refractive index materials
A modified Moss rule anchored to the optical absorption edge identifies (Hf,Zr)₂(S,Se)N₂ chalconitrides as ultra-high refractive index, UV-transparent materials surpassing TiO₂ and SiC.
-
INCARBench: A Benchmark for Scientific Configuration in VASP INCAR by Large Language Models
INCARBench evaluates 19 LLMs on VASP INCAR configuration generation and repair, showing high semantic accuracy but lower scientific correctness especially for DFT+U, magnetism, and correlated materials.
-
Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs
Force-aware Neural Tangent Kernels combined with chunked acquisition provide scalable and distribution-robust active learning for MLIPs, outperforming baselines on OC20 and remaining competitive on other benchmarks.
-
Ultrafast Sliding Ferroelectric Switching in Bilayer Hexagonal Boron Nitride Revealed by Deep Learning Molecular Dynamics
Machine learning molecular dynamics simulations show that bilayer hexagonal boron nitride switches ferroelectric polarization via coherent single-domain rigid sliding in 5 ps and produces hysteresis loops qualitatively matching experiments.
-
Atomistic Machine Learning with Irreducible Cartesian Natural Tensors
CarNet is an equivariant graph-neural-network framework built on irreducible Cartesian natural tensors that predicts interatomic potentials and high-rank tensorial properties such as the elastic constant tensor.
-
Negative and Zero Linear Compressibility in MCN (M = Ag, Au, Cu): A First-Principles Study
First-principles calculations predict zero or negative linear compressibility in six MCN phases (M = Ag, Au, Cu) arising from a 'bamboo forest' geometry of rigid 1D chains with sparse packing.
-
Fine-Tuning a Universal Machine-Learned Interatomic Potential for Oxygen Plasma Interactions with WS$_2$
A pretrained universal MLIP without spin or Hubbard-U corrections predicts the same chemisorbed S removal and O uptake under 15 eV O+/O2+ bombardment of WS2 as after three rounds of PBE+D3+U+spin fine-tuning, which still reduces energy/force MAE to 4.5 meV/atom and 0.076 eV/A.
-
Generating Symmetric Materials using Latent Flow Matching
SymADiT generates stable symmetric materials by enforcing Wyckoff-position and space-group constraints inside a latent generative model built on the prior ADiT architecture.
-
Building an Affordable Self-Driving Lab: Practical Machine Learning Experiments for Physics Education Using Internet-of-Things
An affordable Arduino-based IoT setup generates real-time optical data for students to compare traversal, Bayesian, and deep learning methods in a self-driving experimental workflow.
-
Machine Learning for Electrode Materials: Property Prediction via Composition
On the Materials Project Battery Explorer dataset, CrabNet predicts gravimetric and volumetric capacity and average voltage from composition more accurately than MODNet or Magpie-random-forest.
-
Beyond Explained Variance: A Cautionary Tale of PCA
PCA scatterplots misleadingly indicate clusters in Kuehneotherium teeth data, whereas t-SNE and persistent homology detect a ring-like one-dimensional manifold, backed by a generative model of uniform sampling from a unit circle whose cosine distances follow an arcsine distribution.