Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Riebesell, Janosh, Surta, T. Wesley, Goodall, Rhys, Gaultois, Michael, Lee, Alpha A
Format:	Preprint
Published:	2024
Subjects:	Materials Science Artificial Intelligence Machine Learning Chemical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation
by: Chiang, Yuan, et al.
Published: (2024)

Explaining the thickness-dependent dielectric permittivity of thin films
by: Zaccone, Alessio
Published: (2024)

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
by: Cheng, Austin, et al.
Published: (2024)

Terahertz-permittivity of Carbon Nitrides: Revealing humidity-enhanced dielectric properties on the picosecond timescales relevant for charge carrier photogeneration
by: Jahangir, Reehab, et al.
Published: (2024)

Narrowing band gap chemically and physically: Conductive dense hydrocarbon
by: Nakagawa, Takeshi, et al.
Published: (2024)

Symmetry-Constrained Generation of Diverse Low-Bandgap Molecules with Monte Carlo Tree Search
by: Subramanian, Akshay, et al.
Published: (2024)

ChemGraph-XANES: An Agentic Framework for XANES Simulation and Analysis
by: Grizzi, Vitor F., et al.
Published: (2026)

Benchmarking Universal Interatomic Potentials on Zeolite Structures
by: Ito, Shusuke, et al.
Published: (2025)

GENIUS: An Agentic AI Framework for Autonomous Design and Execution of Simulation Protocols
by: Soleymanibrojeni, Mohammad, et al.
Published: (2025)

AI-Guided Discovery of Novel Ionic Liquid Solvents for Industrial CO2 Capture
by: Garbelotto, Davide, et al.
Published: (2026)

Establishing baselines for generative discovery of inorganic crystals
by: Szymanski, Nathan J., et al.
Published: (2025)

$Δ$-ML Ensembles for Selecting Quantum Chemistry Methods to Compute Intermolecular Interactions
by: Wallace, Austin M., et al.
Published: (2025)

Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics
by: Ma, Andrew, et al.
Published: (2025)

UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
by: Zhang, Gongbo, et al.
Published: (2025)

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
by: Riebesell, Janosh, et al.
Published: (2023)

Are large language models superhuman chemists?
by: Mirza, Adrian, et al.
Published: (2024)

GW space-time method: Energy band-gap of solid hydrogen
by: Azadi, Sam, et al.
Published: (2020)

Harnessing AtomisticSkills for Agentic Atomistic Research
by: Deng, Bowen, et al.
Published: (2026)

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design
by: Wang, Xin-De, et al.
Published: (2025)

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
by: Liu, Ryan, et al.
Published: (2026)

Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
by: Li, Yunyang, et al.
Published: (2025)

COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy
by: Li, Zihan, et al.
Published: (2025)

Evaluating Large Language Models in Scientific Discovery
by: Song, Zhangde, et al.
Published: (2025)

Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
by: Du, Yuanqi, et al.
Published: (2025)

LEAP: A closed-loop framework for perovskite precursor additive discovery
by: Wang, Xin-De, et al.
Published: (2026)

Escaping the Hydrolysis Trap: An Agentic Workflow for Inverse Design of Durable Photocatalytic Covalent Organic Frameworks
by: Peivaste, Iman, et al.
Published: (2026)

A foundation model for atomistic materials chemistry
by: Batatia, Ilyes, et al.
Published: (2023)

UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction with Unsupervised SMILES Alignment
by: Zeng, Kaipeng, et al.
Published: (2024)

Bubble-Burst Synthesis of Ammonia, Amino Acids, and Urea Under Ambient, Catalyst-Free Conditions
by: Cuomo, Jerome J., et al.
Published: (2025)

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
by: Deng, Bowen, et al.
Published: (2024)

Exciton Bimolecular Annihilation Dynamics in Push-Pull Semiconductor Polymers
by: Zheng, Yulong, et al.
Published: (2023)

TorchSim: An efficient atomistic simulation engine in PyTorch
by: Cohen, Orion, et al.
Published: (2025)

Text-guided Diffusion Model for 3D Molecule Generation
by: Luo, Yanchen, et al.
Published: (2024)

Advancing Universal Deep Learning for Electronic-Structure Hamiltonian Prediction of Materials
by: Yin, Shi, et al.
Published: (2025)

ChemGraph: An Agentic Framework for Computational Chemistry Workflows
by: Pham, Thang D., et al.
Published: (2025)

Generative Chemical Language Models for Energetic Materials Discovery
by: Salij, Andrew, et al.
Published: (2026)

Validity of DFT+U band gaps in all its known functional forms
by: Burgess, Andrew C., et al.
Published: (2026)

MBD-ML: Many-body dispersion from machine learning for molecules and materials
by: Moerman, Evgeny, et al.
Published: (2026)

Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond
by: Takaba, Kenichiro, et al.
Published: (2023)

Solvaformer: an SE(3)-equivariant graph transformer for small molecule solubility prediction
by: Broadbent, Jonathan, et al.
Published: (2025)