CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Wines, Daniel, Choudhary, Kamal |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
par: Wines, Daniel, et autres
Publié: (2023)
par: Wines, Daniel, et autres
Publié: (2023)
CHIPS-TB: Evaluating Tight-Binding Models For Metals, Semiconductors, and Insulators
par: Park, In Jun, et autres
Publié: (2025)
par: Park, In Jun, et autres
Publié: (2025)
The JARVIS Infrastructure is All You Need for Materials Design
par: Choudhary, Kamal
Publié: (2025)
par: Choudhary, Kamal
Publié: (2025)
Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
par: Ibrahim, Akram, et autres
Publié: (2023)
par: Ibrahim, Akram, et autres
Publié: (2023)
M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution
par: Biryukov, Vsevolod, et autres
Publié: (2026)
par: Biryukov, Vsevolod, et autres
Publié: (2026)
Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties
par: Weber, John L., et autres
Publié: (2025)
par: Weber, John L., et autres
Publié: (2025)
Machine Learning for Predicting Magnetization from X-ray Diffraction of Iron Oxide Nanoparticles Using Simple Physics-Based Data Generation
par: Abel, Frank M., et autres
Publié: (2025)
par: Abel, Frank M., et autres
Publié: (2025)
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
par: Choudhary, Kamal, et autres
Publié: (2022)
par: Choudhary, Kamal, et autres
Publié: (2022)
AtomGPT: Atomistic Generative Pre-trained Transformer for Forward and Inverse Materials Design
par: Choudhary, Kamal
Publié: (2024)
par: Choudhary, Kamal
Publié: (2024)
AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D
par: Biryukov, Vsevolod, et autres
Publié: (2026)
par: Biryukov, Vsevolod, et autres
Publié: (2026)
Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
par: Schönbauer, Sita, et autres
Publié: (2025)
par: Schönbauer, Sita, et autres
Publié: (2025)
Machine Learning Hamiltonians are Accurate Energy-Force Predictors
par: Kim, Seongsu, et autres
Publié: (2026)
par: Kim, Seongsu, et autres
Publié: (2026)
Towards Universal Material Property Prediction with Deep Learning and Single-Descriptor electronic Density
par: Chen, Feng, et autres
Publié: (2025)
par: Chen, Feng, et autres
Publié: (2025)
Universal Machine Learning Kohn-Sham Hamiltonian for Materials
par: Zhong, Yang, et autres
Publié: (2024)
par: Zhong, Yang, et autres
Publié: (2024)
Chirality-Dependent Kinetics of Single-Walled Carbon Nanotubes from Machine-Learning Force Fields
par: Sun, Sida, et autres
Publié: (2024)
par: Sun, Sida, et autres
Publié: (2024)
Development of an eReaxFF Force Field for BZY20 Solid Oxide Electrocatalysis
par: Hossain, Md Jamil, et autres
Publié: (2023)
par: Hossain, Md Jamil, et autres
Publié: (2023)
Leveraging Machine Learning Force Fields (MLFFs) to Simulate Large Atomistic Systems for Fidelity Improvement of Superconducting Qubits and Sensors
par: Smidstrup, Søren, et autres
Publié: (2025)
par: Smidstrup, Søren, et autres
Publié: (2025)
INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics
par: Mehereen, Taskin, et autres
Publié: (2025)
par: Mehereen, Taskin, et autres
Publié: (2025)
UniMatSim: A High-Throughput Materials Simulation Automation Framework Based on Universal Machine Learning Potentials
par: Xiang, Yanjin, et autres
Publié: (2026)
par: Xiang, Yanjin, et autres
Publié: (2026)
Crystal Transformer Based Universal Atomic Embedding for Accurate and Transferable Prediction of Materials Properties
par: Jin, Luozhijie, et autres
Publié: (2024)
par: Jin, Luozhijie, et autres
Publié: (2024)
Linear Scaling Calculation of Atomic Forces and Energies with Machine Learning Local Density Matrix
par: Xin, Zaizhou, et autres
Publié: (2025)
par: Xin, Zaizhou, et autres
Publié: (2025)
Universal Machine Learning Interatomic Potentials are Ready for Phonons
par: Loew, Antoine, et autres
Publié: (2024)
par: Loew, Antoine, et autres
Publié: (2024)
Universal Machine Learning Potential for Systems with Reduced Dimensionality
par: Benedini, Giulio, et autres
Publié: (2025)
par: Benedini, Giulio, et autres
Publié: (2025)
Machine Learning-Driven Insights into Excitonic Effects in 2D Materials
par: Javed, Ahsan, et autres
Publié: (2025)
par: Javed, Ahsan, et autres
Publié: (2025)
Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
par: Mannan, Sajid, et autres
Publié: (2025)
par: Mannan, Sajid, et autres
Publié: (2025)
Defects and Impurity Properties of VN precipitates in ARAFM Steels: Modelling using a Universal Machine Learning Potential and Experimental Validation
par: Stroud, R. S., et autres
Publié: (2025)
par: Stroud, R. S., et autres
Publié: (2025)
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
par: Raja, Sanjeev, et autres
Publié: (2024)
par: Raja, Sanjeev, et autres
Publié: (2024)
A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials
par: Choudhary, Kamal, et autres
Publié: (2022)
par: Choudhary, Kamal, et autres
Publié: (2022)
Machine Learning-Driven Analytical Models for Threshold Displacement Energy Prediction in Materials
par: Duque, Rosty B. Martinez, et autres
Publié: (2025)
par: Duque, Rosty B. Martinez, et autres
Publié: (2025)
Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials
par: Fragapane, Natascia L., et autres
Publié: (2025)
par: Fragapane, Natascia L., et autres
Publié: (2025)
Accelerating Phonon Thermal Conductivity Prediction by an Order of Magnitude Through Machine Learning-Assisted Extraction of Anharmonic Force Constants
par: Srivastava, Yagyank, et autres
Publié: (2024)
par: Srivastava, Yagyank, et autres
Publié: (2024)
DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns using Generative Pre-trained Transformer
par: Choudhary, Kamal
Publié: (2025)
par: Choudhary, Kamal
Publié: (2025)
LeapFrog: Getting the Jump on Multi-Scale Materials Simulations Using Machine Learning
par: Pinto, Damien, et autres
Publié: (2024)
par: Pinto, Damien, et autres
Publié: (2024)
Third-Body Stabilization of Supercritical CO2 in CO Oxidation: Development and Application of a ReaxFF Force Field for the CO/O/CO2 System
par: Chowdhury, Emdadul Haque, et autres
Publié: (2025)
par: Chowdhury, Emdadul Haque, et autres
Publié: (2025)
Parameter-Efficient Fine-Tuning of Machine-Learning Interatomic Potentials for Phonon and Thermal Properties
par: Grandel, Jonas, et autres
Publié: (2026)
par: Grandel, Jonas, et autres
Publié: (2026)
LTAU-FF: Loss Trajectory Analysis for Uncertainty in Atomistic Force Fields
par: Vita, Joshua A., et autres
Publié: (2024)
par: Vita, Joshua A., et autres
Publié: (2024)
Long-Range Machine Learning of Electron Density for Twisted Bilayer Moiré Materials
par: Lou, Zekun, et autres
Publié: (2026)
par: Lou, Zekun, et autres
Publié: (2026)
Effect of Concentration Fluctuations on Material Properties of Disordered Alloys
par: Liang, Han-Pu, et autres
Publié: (2025)
par: Liang, Han-Pu, et autres
Publié: (2025)
Unbiased Atomistic Predictions of Crystal Dislocation Dynamics using Bayesian Force Fields
par: Owen, Cameron J., et autres
Publié: (2024)
par: Owen, Cameron J., et autres
Publié: (2024)
Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
par: Pisal, Prajwal, et autres
Publié: (2026)
par: Pisal, Prajwal, et autres
Publié: (2026)
Documents similaires
-
Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
par: Wines, Daniel, et autres
Publié: (2023) -
CHIPS-TB: Evaluating Tight-Binding Models For Metals, Semiconductors, and Insulators
par: Park, In Jun, et autres
Publié: (2025) -
The JARVIS Infrastructure is All You Need for Materials Design
par: Choudhary, Kamal
Publié: (2025) -
Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations
par: Ibrahim, Akram, et autres
Publié: (2023) -
M-CODE: Materials Categorization via Ontology, Dimensionality and Evolution
par: Biryukov, Vsevolod, et autres
Publié: (2026)