Trending Research
DeePMD-kit v2: A software package for Deep Potential models
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models.
Chemical Physics Atomic and Molecular Clusters J.2
Qulacs: a fast and versatile quantum circuit simulator for research purpose
To explore the possibilities of a near-term intermediate-scale quantum algorithm and long-term fault-tolerant quantum computing, a fast and versatile quantum circuit simulator is needed.
Quantum Physics Computational Physics
Quantum Supremacy Is Both Closer and Farther than It Appears
We simulate approximate sampling from the output of a circuit with 7x8 qubits and depth 1+40+1 by producing one million bitstring probabilities with fidelity 0. 5%, at an estimated cost of $35184.
Quantum Physics Distributed, Parallel, and Cluster Computing Emerging Technologies
Social Force Model for Pedestrian Dynamics
The corresponding force concept is discussed in more detail and can be also applied to the description of other behaviors.
Statistical Mechanics Pattern Formation and Solitons patt-sol
Urban Mobility
In this chapter, we discuss urban mobility from a complexity science perspective.
Physics and Society Computers and Society
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
txie-93/cgcnn
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Phys. Rev. Lett. 2017
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.
Ranked #5 on
Band Gap
on Materials Project
Band Gap
Formation Energy
Materials Science
Classification and Construction of Topological Phases of Quantum Matter
The power of the framework is demonstrated in a number of applications: (1) the classification and construction of 3D fermionic SPT phases in Wigner-Dyson classes A and AII with glide symmetry, (2) the classification and construction of 3D bosonic SPT phases with space-group symmetries for all 230 space groups, (3) the derivation of a Mayer-Vietoris sequence relating the classification of SPT phases with and without reflection symmetry, and (4) an interpretation of the structure of general crystalline SPT phases via the Atiyah-Hirzebruch spectral sequence.
Strongly Correlated Electrons High Energy Physics - Theory Mathematical Physics Mathematical Physics Quantum Physics
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
atomistic-machine-learning/schnetpack
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SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials.
Computational Physics Chemical Physics
Using the Julia framework to teach quantum entanglement
Entanglement, a phenomenon that has puzzled scientists since its discovery, has been extensively studied by many researchers through both theoretical and experimental aspect of both quantum information processing (QIP) and quantum mechanics (QM).
Physics Education Quantum Physics
Accelerated Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
We introduce a computational framework to predict band offsets of semiconductor interfaces using density functional theory (DFT) and graph neural networks (GNN).
Materials Science
