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What Are the Best Materials To Separate a Xenon/Krypton Mixture?

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What Are the Best Materials To Separate a Xenon/Krypton Mixture?

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Rapid screening of nanopore candidates in nanoporous single-layer graphene for selective separations using molecular visualization and interatomic potentials

Transferable machine-learning models of complex materials: the case of GaAs

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

Physics-enhanced machine learning with symmetry-adapted and long-range representations

A method for faster application of process integration techniques in retrofit situations

Efficient Kr/Xe separation from triangular g-C(3)N(4)nanopores, a simulation study

Advancing computational and data-driven methods for the design and discovery of nanoporous materials

Atomistic modeling of the solid-liquid interface of metals and alloys

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Transferable machine-learning models of complex materials: the case of GaAs

Rapid screening of nanopore candidates in nanoporous single-layer graphene for selective separations using molecular visualization and interatomic potentials

Physics-enhanced machine learning with symmetry-adapted and long-range representations

A method for faster application of process integration techniques in retrofit situations

Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

Atomistic modeling of the solid-liquid interface of metals and alloys

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems

Efficient Kr/Xe separation from triangular g-C(3)N(4)nanopores, a simulation study

Advancing computational and data-driven methods for the design and discovery of nanoporous materials