Papers Atomic Forces
“Atomic Forces” 태그가 달린 논문 28편 · 필터 해제
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
Understanding complex three-dimensional (3D) structures of graphs is essential for accurately modeling various properties, yet many existing approaches struggle with fully capturing the intricate spatial relationships an…
Atomic ForcesComputational EfficiencyGraph Property PredictionGraph Regression+1Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials
The quality of machine learning interatomic potentials (MLIPs) strongly depends on the quantity of training data as well as the quantum chemistry (QC) level of theory used. Datasets generated with high-fidelity QC method…
Atomic ForcesKnowledge DistillationMolecular Property PredictionProperty PredictionLearning atomic forces from uncertainty-calibrated adversarial attacks
Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs). While already pro…
Active LearningAtomic ForcesConstructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
The $\text{Cu}_7\text{P}\text{S}_6$ compound has garnered significant attention due to its potential in thermoelectric applications. In this study, we introduce a neuroevolution potential (NEP), trained on a dataset gene…
Atomic ForcesChemistry-Inspired Diffusion with Non-Differentiable Guidance
Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. These models can be guided in two ways: (i) explicitly, through additional features representing the c…
Atomic ForcesREBIND: Enhancing ground-state molecular conformation via force-based graph rewiring
Predicting the ground-state 3D molecular conformations from 2D molecular graphs is critical in computational chemistry due to its profound impact on molecular properties. Deep learning (DL) approaches have recently emerg…
Atomic ForcesComputational chemistryScalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGN…
Atomic ForcesEnsemble LearningGraph Neural NetworkHyperparameter Optimization+2EL-MLFFs: Ensemble Learning of Machine Leaning Force Fields
Machine learning force fields (MLFFs) have emerged as a promising approach to bridge the accuracy of quantum mechanical methods and the efficiency of classical force fields. However, the abundance of MLFF models and the …
Atomic ForcesEnsemble LearningGraph AttentionGraph Neural Network+2Symmetry-invariant quantum machine learning force fields
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making us…
Atomic ForcesQuantum Machine LearningEGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
Equivariant graph neural networks force fields (EGraFFs) have shown great promise in modelling complex interactions in atomic systems by exploiting the graphs' inherent symmetries. Recent works have led to a surge in the…
Atomic ForcesBenchmarkingFormation EnergyGraph Neural NetworkMatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures. Applying machine learning methods to so…
Atomic ForcesDiversityMulti-Task LearningMay the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations
Recent works have shown the promise of learning pre-trained models for 3D molecular representation. However, existing pre-training models focus predominantly on equilibrium data and largely overlook off-equilibrium confo…
Atomic ForcesDenoisingmolecular representationProperty PredictionQH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While nu…
Atomic ForcesA Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics
This paper proposes a special-purpose system to achieve high-accuracy and high-efficiency machine learning (ML) molecular dynamics (MD) calculations. The system consists of field programmable gate array (FPGA) and applic…
Atomic ForcesCHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials. Although classical force fields often fail to describe the coupling…
Atomic ForcesGraph Neural NetworkTransfer learning for chemically accurate interatomic neural network potentials
Developing machine learning-based interatomic potentials from ab-initio electronic structure methods remains a challenging task for computational chemistry and materials science. This work studies the capability of trans…
Atomic ForcesComputational chemistryTransfer LearningLearning inducing points and uncertainty on molecular data by scalable variational Gaussian processes
Uncertainty control and scalability to large datasets are the two main issues for the deployment of Gaussian process (GP) models within the autonomous machine learning-based prediction pipelines in material science and c…
Atomic ForcesGaussian ProcessesAntibody-Antigen Docking and Design via Hierarchical Equivariant Refinement
Computational antibody design seeks to automatically create an antibody that binds to an antigen. The binding affinity is governed by the 3D binding interface where antibody residues (paratope) closely interact with anti…
Atomic ForcesDecoderLearning Local Equivariant Representations for Large-Scale Atomistic Dynamics
A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this goal, neural message …
Atomic ForcesGraph-Convolutional Deep Learning to Identify Optimized Molecular Configurations
Tackling molecular optimization problems using conventional computational methods is challenging, because the determination of the optimized configuration is known to be an NP-hard problem. Recently, there has been incre…
Atomic ForcesDeep LearningGraph Classification