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Found 8 papers, 4 papers with code
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.
Detecting Label Noise via Leave-One-Out Cross-Validation
Optimizing the noise model using maximum likelihood estimation leads to the containment of the GPR model's predictive error by the posterior standard deviation in leave-one-out cross-validation.
Constant-Depth Circuits for Dynamic Simulations of Materials on Quantum Computers
Current algorithms for Hamiltonian simulation, however, produce circuits that grow in depth with increasing simulation time, limiting feasible simulations to short-time dynamics.
Quantum Physics
Quantum Markov Chain Monte Carlo with Digital Dissipative Dynamics on Quantum Computers
Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment.
Quantum Physics Statistical Mechanics
Composable Programming of Hybrid Workflows for Quantum Simulation
Subsequently, it allows a synthesis of new hybrid algorithms and workflows via the extension, specialization, and dynamic customization of the abstract core classes defined by our design.
Quantum Physics
On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters
We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.
Computational Physics Distributed, Parallel, and Cluster Computing Chemical Physics
ML4Chem: A Machine Learning Package for Chemistry and Materials Science
It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users.
Prediction of Atomization Energy Using Graph Kernel and Active Learning
Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management.
