Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

• URL: http://arxiv.org/abs/2205.02967v1
• Date: Fri, 6 May 2022 00:34:50 GMT
• Title: Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery
• Authors: Chenru Duan, Fang Liu, Aditya Nandy, and Heather J. Kulik
• Abstract summary: 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.
• Score: 2.7810723668216575
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the biases of training data derived from density functional theory (DFT) and leads to many attempted calculations that are doomed to fail. Many compelling functional materials and catalytic processes involve strained chemical bonds, open-shell radicals and diradicals, or metal-organic bonds to open-shell transition-metal centers. Although promising targets, these materials present unique challenges for electronic structure methods and combinatorial challenges for their discovery. In this Perspective, we describe the advances needed in accuracy, efficiency, and approach beyond what is typical in conventional DFT-based ML workflows. These challenges have begun to be addressed through ML models trained to predict the results of multiple methods or the differences between them, enabling quantitative sensitivity analysis. For DFT to be trusted for a given data point in a high-throughput screen, it must pass a series of tests. ML models that predict the likelihood of calculation success and detect the presence of strong correlation will enable rapid diagnoses and adaptation strategies. These "decision engines" represent the first steps toward autonomous workflows that avoid the need for expert determination of the robustness of DFT-based materials discoveries.

Related papers

• Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning [8.695927973994577]
  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.
  arXiv  Detail & Related papers  (2024-10-08T01:34:42Z)
• FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
  We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications. FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
  arXiv  Detail & Related papers  (2024-08-15T16:45:16Z)
• Reliable edge machine learning hardware for scientific applications [34.87898436984149]
  Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements.
  arXiv  Detail & Related papers  (2024-06-27T20:45:08Z)
• Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement [69.51496713076253]
  In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods. We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground. We also propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL.
  arXiv  Detail & Related papers  (2024-02-05T22:15:55Z)
• Grad DFT: a software library for machine learning enhanced density functional theory [0.0]
  Density functional theory (DFT) stands as a cornerstone in computational quantum chemistry and materials science. Recent work has begun to explore how machine learning can expand the capabilities of DFT. We present Grad DFT: a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange-correlation energy functionals.
  arXiv  Detail & Related papers  (2023-09-23T00:25:06Z)
• Accurate machine learning force fields via experimental and simulation data fusion [0.0]
  Machine Learning (ML)-based force fields are attracting ever-increasing interest due to their capacity to span scales of classical interatomic potentials at quantum-level accuracy. Here we leverage both Density Functional Theory (DFT) calculations and experimentally measured mechanical properties and lattice parameters to train an ML potential of titanium. We demonstrate that the fused data learning strategy can concurrently satisfy all target objectives, thus resulting in a molecular model of higher accuracy compared to the models trained with a single source data.
  arXiv  Detail & Related papers  (2023-08-17T18:22:19Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles [4.700621178941319]
  We introduce an approach to rapidly obtain property predictions from 23 representative DFAs spanning multiple families and "rungs" We train independent ML models for each DFA and observe convergent trends in feature importance. By requiring consensus of the ANN-predicted DFA properties, we improve correspondence of these computational lead compounds with literature-mined, experimental compounds.
  arXiv  Detail & Related papers  (2021-06-24T15:43:57Z)
• 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)
• Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources [78.72922528736011]
  We propose a novel approach, black-box adversarial reprogramming (BAR), that repurposes a well-trained black-box machine learning model. Using zeroth order optimization and multi-label mapping techniques, BAR can reprogram a black-box ML model solely based on its input-output responses. BAR outperforms state-of-the-art methods and yields comparable performance to the vanilla adversarial reprogramming method.
  arXiv  Detail & Related papers  (2020-07-17T01:52:34Z)

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.