Atomic Cluster Expansion: A framework for fast and accurate ML force fields
Atomic Cluster Expansion: A framework for fast and accurate ML force fields
|
Yükleniyor...
 Hızlı erişim için Tubidy'yi favorilerinize ekleyin.
Lütfen bekleyiniz...
Type
Size

 İlgili Videolar

Atomic Cluster Expansion: A framework for fast and accurate ML force fields

Atomic Cluster Expansion: A framework for fast and accurate ML force fields

|
Atomic Cluster Expansion based Force Fields for Molecular Materials

Atomic Cluster Expansion based Force Fields for Molecular Materials

|
Christoph Ortner - Atomic Cluster Expansion with and Without Atoms - IPAM at UCLA

Christoph Ortner - Atomic Cluster Expansion with and Without Atoms - IPAM at UCLA

|
Christoph Ortner - Modelling Atomic Properties with the Atomic Cluster Expansion - IPAM at UCLA

Christoph Ortner - Modelling Atomic Properties with the Atomic Cluster Expansion - IPAM at UCLA

|
Stefan Chmiela - Accurate global machine learning force fields for molecules with hundreds of atoms

Stefan Chmiela - Accurate global machine learning force fields for molecules with hundreds of atoms

|
Ilyes Batatia - Unified understanding of E(3)-Equivariant Interatomic Potentials Theory/Applications

Ilyes Batatia - Unified understanding of E(3)-Equivariant Interatomic Potentials Theory/Applications

|
A Unified Understanding of Equivariant Interatomic Potentials

A Unified Understanding of Equivariant Interatomic Potentials

|
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

|
Basics of machine learning force fields | VASP Lecture

Basics of machine learning force fields | VASP Lecture

|
QUACE: Quantum Atomic Cluster Expansion

QUACE: Quantum Atomic Cluster Expansion

|
Machine learning force field for organic liquids: EC/EMC binary solvent

Machine learning force field for organic liquids: EC/EMC binary solvent

|
Reproducible Simulation Workflows and Machine Learning Directed Force Field Development

Reproducible Simulation Workflows and Machine Learning Directed Force Field Development

|
Atomistic-scale simulations of realistic, complex, reactive materials

Atomistic-scale simulations of realistic, complex, reactive materials

|
Structural Descriptors and Atomic Cluster Expansion Basis

Structural Descriptors and Atomic Cluster Expansion Basis

|
Equivariant N-centered representations for atomistic machine learning

Equivariant N-centered representations for atomistic machine learning

|
Interpretability In Atomic-Scale Machine Learning

Interpretability In Atomic-Scale Machine Learning

|
17B: Alex Huebner - Cluster expansion, ab initio thermodynamics, and cross validation

17B: Alex Huebner - Cluster expansion, ab initio thermodynamics, and cross validation

|
Gabor Csányi - Machine learning potentials: from polynomials to message passing networks

Gabor Csányi - Machine learning potentials: from polynomials to message passing networks

|
17A: Santiago Rigamonti - Cluster expansion, ab initio thermodynamics, and cross validation

17A: Santiago Rigamonti - Cluster expansion, ab initio thermodynamics, and cross validation

|
𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙞𝙣 𝙚𝙡𝙚𝙘𝙩𝙧𝙤𝙣𝙞𝙘-𝙨𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚 𝙩𝙝𝙚𝙤𝙧𝙮 by Audun Skau Hansen | University of Oslo, Norway

𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙞𝙣 𝙚𝙡𝙚𝙘𝙩𝙧𝙤𝙣𝙞𝙘-𝙨𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚 𝙩𝙝𝙚𝙤𝙧𝙮 by Audun Skau Hansen | University of Oslo, Norway

|
Copyright. All rights reserved © 2025
Rosebank, Johannesburg, South Africa

Favorilere Ekle