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Machine learning interatomic potentials and long-range physics
Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …
networks, have resulted in short-range models that can infer interaction energies with near …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
A Euclidean transformer for fast and stable machine learned force fields
Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …
Spice, a dataset of drug-like molecules and peptides for training machine learning potentials
Abstract Machine learning potentials are an important tool for molecular simulation, but their
development is held back by a shortage of high quality datasets to train them on. We …
development is held back by a shortage of high quality datasets to train them on. We …
Extending machine learning beyond interatomic potentials for predicting molecular properties
Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …
chemical processes and materials. ML provides a surrogate model trained on a reference …
[HTML][HTML] Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
The MACE architecture represents the state of the art in the field of machine learning force
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we …
TorchANI: a free and open source PyTorch-based deep learning implementation of the ANI neural network potentials
This paper presents TorchANI, a PyTorch-based program for training/inference of ANI
(ANAKIN-ME) deep learning models to obtain potential energy surfaces and other physical …
(ANAKIN-ME) deep learning models to obtain potential energy surfaces and other physical …