1.9k citations · 2k across the 8 of their papers we have counts for
8 papers
TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era
Weitang Li, Jonathan Allcock, Lixue Cheng +5
TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high performance on the simulation o…
Multi-Constraint Molecular Generation using Sparsely Labelled Training Data for Localized High-Concentration Electrolyte Diluent Screening
Jonathan P. Mailoa, Xin Li, Jiezhong Qiu +1
Recently, machine learning methods have been used to propose molecules with desired properties, which is especially useful for exploring large chemical spaces efficiently. However,…
Protein-Ligand Complex Generator & Drug Screening via Tiered Tensor Transform
Jonathan P. Mailoa, Zhaofeng Ye, Jiezhong Qiu +2
The generation of small molecule candidate (ligand) binding poses in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ig…
Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions
Kirill Shmilovich, Devin Willmott, Ivan Batalov +3
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm o…
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
Simon Batzner, Albert Musaelian, Lixin Sun +6
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations f…
A community-powered search of machine learning strategy space to find NMR property prediction models
Lars A. Bratholm, Will Gerrard, Brandon Anderson +26
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML st…
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture
Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause +3
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural n…
Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
Jonathan P. Mailoa, Mordechai Kornbluth, Simon L. Batzner +5
Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents th…