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Artificial Intelligence In Materials Science: Application to Molecular and Particulate Simulations

Published online by Cambridge University Press:  17 March 2011

John F. Maguire ,
Mark Benedict ,
Leslie V. Woodcock  and
Steven R. LeClair
John F. Maguire
Affiliation:
Email address:[email protected]
Mark Benedict
Affiliation:
Air Force Research Laboratory Materials and Manufacturing Directorate WPAFB, Dayton OH 45433-7746
Leslie V. Woodcock
Affiliation:
Permanent address:, University of Manchester Institute of Science and Technology (UMIST), UK
Steven R. LeClair
Affiliation:
Air Force Research Laboratory Materials and Manufacturing Directorate WPAFB, Dayton OH 45433-7746
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Abstract

We illustrate how emerging methods in artificial intelligence (AI) may be useful in materials science. Historically, these methods were developed in the area of materials process control and, more recently, in the nascent field of materials discovery. However, machine intelligence is of much broader import and our primary objective here is to illustrate how such methods may be used to circumvent some serious roadblocks in the computer simulation of a significant class of computationally hard problems in materials science. This is illustrated by a new approach to solving the dynamics of the N-body problem for large numbers of objects of essentially arbitrarily complex geometry or interaction potential. The approach, based on a particulate artificial neural net dynamics algorithm (PANNDA) is more than two orders of magnitude faster than existing methods when applied to large systems and is only marginally slower (∼10%) than the theoretical lower limiting case of hard spheres. In this method an artificial neural net is trained to predict accurately the time to next collision for binary encounters spanning the Hilbert space of relative positions, orientations and momenta (linear and angular). This approach, which can be extended to soft complex systems, enables construction of exact, albeit numerical, models for the thermodynamic, transport and non-equilibrium properties of very large ensembles of hard or soft objects of arbitrarily complex shape or interaction potential. Our results open up the possibility of immediate application to an usually wide spectrum of contemporary computationally intractable “hard” problems ranging from granular materials with asperities through inclusion of complex many-body terms in the intermolecular interaction in molecular dynamics calculations of complex fluids and polymers.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 700: Symposium S – Combinatorial and Artificial Intelligence Methods in Materials Science , 2001 , S8.1
Copyright
Copyright © Materials Research Society 2002

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