Michele Ceriotti Epfl Cis Equivariance And Universal Approximation For Geometric Point Clouds

Center For Intelligent Systems CIS EPFL - YouTube

Center For Intelligent Systems CIS EPFL - YouTube Abstract: as with many fields of science, machine learning has become an essential part of the toolbox for modeling matter at the atomic scale, with many frameworks having become well established,. Since 2013 he leads the laboratory for computational science and modeling, in the institute of materials at epfl, that focuses on method development for atomistic materials modeling based on statistical mechanics and machine learning.

Michele Ceriotti — People - EPFL

Michele Ceriotti — People - EPFL [email protected] abstract point clouds are versatile representations of 3d objects and have found widespread appli. ation in science and engineering. many successful deep learning models have bee. Achieving a complete and symmetric description of a group of point particles, such as atoms in a molecule, is a common problem in physics and theoretical chemistry. ‪associate professor at epfl, institute of materials‬ ‪‪cited by 21,188‬‬ ‪atomic scale modeling‬ ‪machine learning‬ ‪materials science‬ ‪statistical mechanics‬ ‪physical chemistry‬. I have recently developed a generalized langevin equation framework which is useful to improve and manipulate the sampling properties of molecular dynamics, and an algorithm to analyze the high dimensional data resulting from the simulation of complex systems by techniques derived from the machine learning community.

Michele Ceriotti Awarded An ERC Consolidator Grant Of 2 Million Euro - EPFL

Michele Ceriotti Awarded An ERC Consolidator Grant Of 2 Million Euro - EPFL ‪associate professor at epfl, institute of materials‬ ‪‪cited by 21,188‬‬ ‪atomic scale modeling‬ ‪machine learning‬ ‪materials science‬ ‪statistical mechanics‬ ‪physical chemistry‬. I have recently developed a generalized langevin equation framework which is useful to improve and manipulate the sampling properties of molecular dynamics, and an algorithm to analyze the high dimensional data resulting from the simulation of complex systems by techniques derived from the machine learning community. In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. In this work, we explore a third route to tackle this learning problem, where equivariant functions are expressed as the product of a scalar function of the point cloud coordinates and a small basis of tensors with the appropriate symmetry. Michele ceriotti received his ph.d. in physics from eth zürich in 2010. he spent three years in oxford as a junior research fellow at merton college. since 2013 he leads the laboratory for computational science and modeling in the institute of materials at epfl. We apply a recently proposed scheme to compress chemical information in a lower dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a.

Michele Ceriotti To Represent EPFL In New ELLIS Program - EPFL

Michele Ceriotti To Represent EPFL In New ELLIS Program - EPFL In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. In this work, we explore a third route to tackle this learning problem, where equivariant functions are expressed as the product of a scalar function of the point cloud coordinates and a small basis of tensors with the appropriate symmetry. Michele ceriotti received his ph.d. in physics from eth zürich in 2010. he spent three years in oxford as a junior research fellow at merton college. since 2013 he leads the laboratory for computational science and modeling in the institute of materials at epfl. We apply a recently proposed scheme to compress chemical information in a lower dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a.

Michele Ceriotti Wins E. Bright Wilson Prize - EPFL

Michele Ceriotti Wins E. Bright Wilson Prize - EPFL Michele ceriotti received his ph.d. in physics from eth zürich in 2010. he spent three years in oxford as a junior research fellow at merton college. since 2013 he leads the laboratory for computational science and modeling in the institute of materials at epfl. We apply a recently proposed scheme to compress chemical information in a lower dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a.

Michele Ceriotti (EPFL-CIS): “Equivariance and universal approximation for geometric point clouds”