Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

• URL: http://arxiv.org/abs/2410.05604v1
• Date: Tue, 8 Oct 2024 01:34:42 GMT
• Title: Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning
• Authors: Md Rajib Khan Musa, Yichen Qian, Jie Peng, David Cereceda,
• Abstract summary: We develop a physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning. We demonstrate the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy.
• Score: 8.695927973994577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

Related papers

• Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges [0.36868085124383626]
  This work explores the use of machine learning interatomic potentials (MLIPs) to significantly accelerate phase diagram calculations. Using case studies including Cr-Mo, Cu-Au, and Pt-W, we demonstrate that MLIPs, particularly ORB, achieve computational speedups exceeding three orders of magnitude compared to DFT.
  arXiv  Detail & Related papers  (2024-11-22T21:24:13Z)
• Predicting ionic conductivity in solids from the machine-learned potential energy landscape [68.25662704255433]
  Superionic materials are essential for advancing solid-state batteries, which offer improved energy density and safety. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. We propose an approach for the quick and reliable evaluation of ionic conductivity through the analysis of a universal interatomic potential.
  arXiv  Detail & Related papers  (2024-11-11T09:01:36Z)
• MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks [42.61784133509237]
  Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. Existing approaches, including ab initio calculations and even deep generative models, struggle with the complexity of MOF structures due to the large number of atoms in the unit cells. We introduce MOFFlow, the first deep generative model tailored for MOF structure prediction.
  arXiv  Detail & Related papers  (2024-10-07T13:51:58Z)
• Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders [0.0]
  We propose a novel approach where generative machine learning is used to yield a representative set of configurations for accurate property evaluation. Our method employs a specific type of variational autoencoder with inverse roles for the encoder and decoder. We illustrate our approach by computing point-defect formation energies and concentrations in (U, Pu)O2 mixed-oxide fuels.
  arXiv  Detail & Related papers  (2024-08-27T10:05:37Z)
• 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)
• D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
  We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT) We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity. In addition, we show that our approach enables us to explore more complex neural-based wave functions.
  arXiv  Detail & Related papers  (2023-03-01T10:38:10Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery [2.7810723668216575]
  We describe the advances needed in accuracy, efficiency, and approach beyond what is typical in conventional DFT-based machine learning (ML) For DFT to be trusted for a given data point in a high- throughput screen, it must pass a series of tests. For DFT to be trusted for a given data point in a high- throughput screen, it must pass a series of tests.
  arXiv  Detail & Related papers  (2022-05-06T00:34:50Z)
• 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)
• Finite Temperature Auxiliary Field Quantum Monte Carlo in the Canonical Ensemble [15.163835008676621]
  We present a new approach for performing AFQMC simulations in the canonical ensemble that does not require knowledge of chemical potentials. We benchmark the accuracy of our technique on illustrative Bose and Fermi Hubbard models and demonstrate that it can converge more quickly to the ground state than grand canonical AFQMC simulations.
  arXiv  Detail & Related papers  (2020-10-19T19:44:59Z)
• Machine Learning Force Fields [54.48599172620472]
  Machine Learning (ML) has enabled numerous advances in computational chemistry. One of the most promising applications is the construction of ML-based force fields (FFs) This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them.
  arXiv  Detail & Related papers  (2020-10-14T13:14:14Z)

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.