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Atomic Forces
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Predicion of the atomic forces, generally calculated with a quantum mechanical code (e.g. at DFT theory).
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning atomic forces from uncertainty-calibrated adversarial attacks
Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
Chemistry-Inspired Diffusion with Non-Differentiable Guidance
REBIND: Enhancing ground-state molecular conformation via force-based graph rewiring
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields
Symmetry-invariant quantum machine learning force fields
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
Transfer learning for chemically accurate interatomic neural network potentials
Learning inducing points and uncertainty on molecular data by scalable variational Gaussian processes
Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
Simple and efficient algorithms for training machine learning potentials to force data
Neural Network Based in Silico Simulation of Combustion Reactions
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
Nudged elastic band calculations accelerated with Gaussian process regression
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