Artificial intelligence enhanced molecular simulations
J Zhang, D Chen, Y **a, YP Huang, X Lin… - Journal of Chemical …, 2023 - ACS Publications
Molecular simulations, which simulate the motions of particles according to fundamental
laws of physics, have been applied to a wide range of fields from physics and materials …
laws of physics, have been applied to a wide range of fields from physics and materials …
Pymoo: Multi-objective optimization in python
Python has become the programming language of choice for research and industry projects
related to data science, machine learning, and deep learning. Since optimization is an …
related to data science, machine learning, and deep learning. Since optimization is an …
Evaluating analytic gradients on quantum hardware
An important application for near-term quantum computing lies in optimization tasks, with
applications ranging from quantum chemistry and drug discovery to machine learning. In …
applications ranging from quantum chemistry and drug discovery to machine learning. In …
Pennylane: Automatic differentiation of hybrid quantum-classical computations
PennyLane is a Python 3 software framework for differentiable programming of quantum
computers. The library provides a unified architecture for near-term quantum computing …
computers. The library provides a unified architecture for near-term quantum computing …
Difftaichi: Differentiable programming for physical simulation
We present DiffTaichi, a new differentiable programming language tailored for building high-
performance differentiable physical simulators. Based on an imperative programming …
performance differentiable physical simulators. Based on an imperative programming …
How important are activation functions in regression and classification? A survey, performance comparison, and future directions
Inspired by biological neurons, the activation functions play an essential part in the learning
process of any artificial neural network (ANN) commonly used in many real-world problems …
process of any artificial neural network (ANN) commonly used in many real-world problems …
Continuous-variable quantum neural networks
We introduce a general method for building neural networks on quantum computers. The
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …
quantum neural network is a variational quantum circuit built in the continuous-variable (CV) …
Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
We introduce a deep learning framework designed to train smoothed elastoplasticity models
with interpretable components, such as the stored elastic energy function, yield surface, and …
with interpretable components, such as the stored elastic energy function, yield surface, and …
The elements of differentiable programming
Artificial intelligence has recently experienced remarkable advances, fueled by large
models, vast datasets, accelerated hardware, and, last but not least, the transformative …
models, vast datasets, accelerated hardware, and, last but not least, the transformative …
Differentiable programming tensor networks
Differentiable programming is a fresh programming paradigm which composes
parameterized algorithmic components and optimizes them using gradient search. The …
parameterized algorithmic components and optimizes them using gradient search. The …