Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration

• URL: http://arxiv.org/abs/2003.01878v3
• Date: Thu, 8 Oct 2020 22:42:06 GMT
• Title: Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration
• Authors: Sen Liu (1), Branden B. Kappes (1), Behnam Amin-ahmadi (1), Othmane Benafan (2), Xiaoli Zhang (1), Aaron P. Stebner (1,3) ((1) Mechanical Engineering, Colorado School of Mines, Golden (2) Materials and Structures Division, NASA Glenn Research Center (3) Mechanical Engineering and Materials Science and Engineering, Georgia Institute of Technology)
• Abstract summary: Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space. A physics-informed featured engineering approach is shown to enable otherwise poorly performing ML models to perform well with the same data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A physics-informed featured engineering approach is shown to enable otherwise poorly performing ML models to perform well with the same data. Specifically, previously engineered elemental features based on alloy chemistries are combined with newly engineered heat treatment process features. The new features result from first transforming the heat treatment parameter data as it was previously recorded using nonlinear mathematical relationships known to describe the thermodynamics and kinetics of phase transformations in alloys. The ability of the ML model to be used for predictive design is validated using blind predictions. Composition - process - property relationships for thermal hysteresis of shape memory alloys (SMAs) with complex microstructures created via multiple melting-homogenization-solutionization-precipitation processing stage variations are captured, in addition to the mean transformation temperatures of the SMAs. The quantitative models of hysteresis exhibited by such highly processed alloys demonstrate the ability for ML models to design for physical complexities that have challenged physics-based modeling approaches for decades.

Related papers

• Interpolation and differentiation of alchemical degrees of freedom in machine learning interatomic potentials [1.1016723046079784]
  We report the use of continuous and differentiable alchemical degrees of freedom in atomistic materials simulations. The proposed method introduces alchemical atoms with corresponding weights into the input graph, alongside modifications to the message-passing and readout mechanisms of MLIPs. The end-to-end differentiability of MLIPs enables efficient calculation of the gradient of energy with respect to the compositional weights.
  arXiv  Detail & Related papers  (2024-04-16T17:24:22Z)
• Online Variational Sequential Monte Carlo [49.97673761305336]
  We build upon the variational sequential Monte Carlo (VSMC) method, which provides computationally efficient and accurate model parameter estimation and Bayesian latent-state inference. Online VSMC is capable of performing efficiently, entirely on-the-fly, both parameter estimation and particle proposal adaptation.
  arXiv  Detail & Related papers  (2023-12-19T21:45:38Z)
• Electronic excited states from physically-constrained machine learning [0.0]
  We present an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on.
  arXiv  Detail & Related papers  (2023-11-01T20:49:59Z)
• Supervised Machine Learning and Physics based Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy [0.0]
  correlations between process parameters, thermal profiles, and resulting in AFSD remain poorly understood. This work employs a framework combining supervised machine learning ( neural) and physics-informed networks (PINNs) to predict peak temperature distribution in AFSD from process parameters.
  arXiv  Detail & Related papers  (2023-09-13T09:39:42Z)
• A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing [0.0]
  Metal additive manufacturing (MAM) has experienced significant developments and gained much attention. Predicting the impact of processing parameters on the characteristics of an MAM printed clad is challenging due to the complex nature of MAM processes. Machine learning (ML) techniques can help connect the physics underlying the process and processing parameters to the clad characteristics.
  arXiv  Detail & Related papers  (2023-07-04T18:32:41Z)
• FAENet: Frame Averaging Equivariant GNN for Materials Modeling [123.19473575281357]
  We introduce a flexible framework relying on frameaveraging (SFA) to make any model E(3)-equivariant or invariant through data transformations. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling.
  arXiv  Detail & Related papers  (2023-04-28T21:48:31Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)
• A Supervised Machine Learning Approach for Accelerating the Design of Particulate Composites: Application to Thermal Conductivity [0.0]
  A supervised machine learning (ML) based computational methodology for the design of particulate multifunctional composite materials is presented. Design variables are physical descriptors of the material microstructure that directly link microstructure to the material's properties. Our optimized ML method is trained over the generated database and establishes the complex relationship between the structure and properties.
  arXiv  Detail & Related papers  (2020-09-30T18:18:00Z)
• Predictive modeling approaches in laser-based material processing [59.04160452043105]
  This study aims to automate and forecast the effect of laser processing on material structures. The focus is centred on the performance of representative statistical and machine learning algorithms. Results can set the basis for a systematic methodology towards reducing material design, testing and production cost.
  arXiv  Detail & Related papers  (2020-06-13T17:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.