Active matter, which ranges from molecular motors to groups of animals, exists at different length scales and timescales, and various computational models have been proposed to describe and predict its behaviour. The diversity of the methods and the challenges in modelling active matter primarily origi...
Active matter, which ranges from molecular motors to groups of animals, exists at different length scales and timescales, and various computational models have been proposed to describe and predict its behaviour. The diversity of the methods and the challenges in modelling active matter primarily origi...
A large-scale electronic circular dichroism spectrum dataset is proposed and the ECDFormer framework is developed to achieve accurate and interpretable ECD spectrum prediction for natural products. Hao Li Da Long Fanyang Mo Article03 Jan 2025 Large language models act as if they are part of a...
This view has become especially promising with recent advances in both large language models and vector symbolic architectures. These innovations show how vectors can handle many properties traditionally thought to be out of reach for neural models, including compositionality, definitions, structures, and...
Purpose Simulating the interaction of the human body with electromagnetic fields is an active field of research. Individualized models are increasingly being used, as anatomical differences affect the simulation results. We introduce a processing pipeline for creating individual surface‐based models of ...
A computer docking study has been carried out on the crystal surfaces of cellulose Ialpha crystal models for the carbohydrate binding module (CBM) protein ... T Yui,H Shiiba,Y Tsutsumi,... - 《Journal of Physical Chemistry B》 被引量: 59发表: 2010年 Expression and characterisation of chymo...
dipeptide and MIL-53(Al), generating MLIPs that represent both configurational spaces more accurately than models trained with conventional MD. Similar content being viewed by others Uncertainty-driven dynamics for active learning of interatomic potentials...
There are other good models that are also suitable for this. The primary reason why I chose the LNP model is that it is very close to deep learning. This makes this model perfect to compare the architecture of a neuron to the architecture of a convolutional net. I will do this in the...
Taking a deep dive with active learning for drug discovery Active machine learning is employed in academia and industry to support drug discovery. A recent study unravels the factors that influence a deep learning models’ ability to guide iterative discovery. ...
heightened interest in experimental active matter, there is significant opportunity to leverage the MGI paradigm and machine learning methods to analyze complex 4D data sets, extract emergent phenomena (e.g., spontaneous generation and motion of defects), and rapidly compare to phenomenological models....