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View article: MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules Open
Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferabili…
View article: The design space of E(3)-equivariant atom-centred interatomic potentials
The design space of E(3)-equivariant atom-centred interatomic potentials Open
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has …
View article: SiMGen example molecules
SiMGen example molecules Open
Examples of structures generated using SiMGen.Macrocycles, including their generation trajectories.Small molecules.Baseline comparison of molecules generated via the linear interpolation mentioned in the paper.Structures generated for anal…
View article: A foundation model for atomistic materials chemistry
A foundation model for atomistic materials chemistry Open
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. O…
View article: MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules Open
Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferabilit…
View article: Evaluation of the MACE force field architecture: From medicinal chemistry to materials science
Evaluation of the MACE force field architecture: From medicinal chemistry to materials science Open
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 further evaluate MACE by fitting models for publish…
View article: First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects
First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects Open
We predict the quantum vibrational spectra of complex aqueous interfaces. We learn potentials that encode the quantum nuclear effects and physics-based models of dielectric responses, reducing quantum dynamics to classical molecular dynami…
View article: Tensor-Reduced Atomic Density Representations
Tensor-Reduced Atomic Density Representations Open
Density-based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modeling, and the visualizat…
View article: Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science Open
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 further evaluate MACE by fitting models for publishe…
View article: Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials Open
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating accura…
View article: Tensor-reduced atomic density representations
Tensor-reduced atomic density representations Open
Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisat…
View article: 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 Open
Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, several equivariant message passing neural networks (MPNNs) have been shown to outperform models built using o…
View article: The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials Open
The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic Cluster Expansion (ACE), which unified many of the earlier …
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE Open
We demonstrate that fast and accurate linear force fields can be built for molecules using the atomic cluster expansion (ACE) framework. The ACE models parametrize the potential energy surface in terms of body-ordered symmetric polynomials…
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE Open
We demonstrate that fast and accurate linear force fields can be built for molecules using the Atomic Cluster Expansion (ACE) framework. The ACE models parametrize the Potential Energy Surface in terms of body ordered symmetric polynomials…
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE Open
We demonstrate that fast and accurate linear force fields can be built for molecules using the Atomic Cluster Expansion (ACE) framework. The ACE models parametrize the Potential Energy Surface in terms of body ordered symmetric polynomials…
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE Open
We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditiona…
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE Open
We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditiona…
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE Open
We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditiona…
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE Open
We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditiona…
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE Open
We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditiona…
View article: Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE Open
We demonstrate that accurate linear force fields can be built using the Atomic Cluster Ex- pansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural exten- sion of traditiona…
View article: Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias
Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias Open
Organic synthesis remains a stumbling block in drug discovery. Although a plethora of machine learning models have been proposed as solutions in the literature, they suffer from being opaque black-boxes. It is neither clear if the models a…
View article: Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias
Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias Open
Organic synthesis remains a stumbling block in drug discovery. Although a plethora of machine learning models have been proposed as solutions in the literature, they suffer from being opaque black-boxes. It is neither clear if the models a…