Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Advancing molecular simulation with equivariant interatomic potentials
Deep learning has the potential to accelerate atomistic simulations, but existing models
suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert …
suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert …
[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 …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Neural scaling of deep chemical models
Massive scale, in terms of both data availability and computation, enables important
breakthroughs in key application areas of deep learning such as natural language …
breakthroughs in key application areas of deep learning such as natural language …
Transferability and accuracy of ionic liquid simulations with equivariant machine learning interatomic potentials
Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from
energy storage to solvents, where they have been touted as “designer solvents” as they can …
energy storage to solvents, where they have been touted as “designer solvents” as they can …
Variational Monte Carlo on a Budget—Fine-tuning pre-trained Neural Wavefunctions
Obtaining accurate solutions to the Schrödinger equation is the key challenge in
computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) …
computational quantum chemistry. Deep-learning-based Variational Monte Carlo (DL-VMC) …
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
We present the second part of the rigorous evaluation of modern machine learning force
fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of …
fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of …
Efficient implementation of Monte Carlo algorithms on graphical processing units for simulation of adsorption in porous materials
We present enhancements in Monte Carlo simulation speed and functionality within an open-
source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant …
source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant …
Lagrangebench: A lagrangian fluid mechanics benchmarking suite
Abstract Machine learning has been successfully applied to grid-based PDE modeling in
various scientific applications. However, learned PDE solvers based on Lagrangian particle …
various scientific applications. However, learned PDE solvers based on Lagrangian particle …