Umesh Waghmare
http://dbpedia.org/resource/Umesh_Waghmare an entity of type: Thing
Umesh Waghmare is an Indian physicist, and presently a Professor in the Theoretical Sciences Unit at Jawaharlal Nehru Centre for Advanced Scientific Research. Research in his Materials Theory Group is fundamentally based on computer simulations of electronic motion governed by quantum physics and resulting electron-mediated inter-atomic interactions which are responsible for multi-scale behaviour of materials. Deriving material-specific information on chemical bonding and microscopic couplings that are essential to the specific properties of a material, they develop fundamental understanding of a material in terms of its atomic structure and electronic structure. The goal of their theoretical analysis is to derive material-specific properties on the macroscopic and intermediate length and
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Umesh Waghmare
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Umesh Waghmare is an Indian physicist, and presently a Professor in the Theoretical Sciences Unit at Jawaharlal Nehru Centre for Advanced Scientific Research. Research in his Materials Theory Group is fundamentally based on computer simulations of electronic motion governed by quantum physics and resulting electron-mediated inter-atomic interactions which are responsible for multi-scale behaviour of materials. Deriving material-specific information on chemical bonding and microscopic couplings that are essential to the specific properties of a material, they develop fundamental understanding of a material in terms of its atomic structure and electronic structure. The goal of their theoretical analysis is to derive material-specific properties on the macroscopic and intermediate length and time scales starting from a first principles description of chemistry and atomic structure. They complement experimental work by accessing the microscopic information that may be hard to access in a laboratory. Their work leads to design of new materials or modification of existing materials to yield desired properties, or narrowing down the choices of new materials for design by experiment. Recently, they have shown how techniques of machine learning can be constrained by dimensional analysis and physical laws to develop simple and predictive models that benefit from both the data and existing knowledge.
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