Interpretable and Explainable Machine Learning for Materials Science and Chemistry

• URL: http://arxiv.org/abs/2111.01037v2
• Date: Wed, 3 Nov 2021 17:04:13 GMT
• Title: Interpretable and Explainable Machine Learning for Materials Science and Chemistry
• Authors: Felipe Oviedo, Juan Lavista Ferres, Tonio Buonassisi, Keith Butler
• Abstract summary: We summarize applications of interpretability and explainability techniques for materials science and chemistry. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. We showcase a number of exciting developments in other fields that could benefit interpretability in material science and chemistry problems.
• Score: 2.2175470459999636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While the uptake of data-driven approaches for materials science and chemistry is at an exciting, early stage, to realise the true potential of machine learning models for successful scientific discovery, they must have qualities beyond purely predictive power. The predictions and inner workings of models should provide a certain degree of explainability by human experts, permitting the identification of potential model issues or limitations, building trust on model predictions and unveiling unexpected correlations that may lead to scientific insights. In this work, we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies. We discuss various challenges for interpretable machine learning in materials science and, more broadly, in scientific settings. In particular, we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need of uncertainty estimates for model explanations. Finally, we showcase a number of exciting developments in other fields that could benefit interpretability in material science and chemistry problems.

Related papers

• Probing the limitations of multimodal language models for chemistry and materials research [3.422786943576035]
  We introduce MaCBench, a benchmark for evaluating how vision-language models handle real-world chemistry and materials science tasks. We find that while these systems show promising capabilities in basic perception tasks, they exhibit fundamental limitations in spatial reasoning, cross-modal information synthesis, and logical inference. Our insights have important implications beyond chemistry and materials science, suggesting that developing reliable multimodal AI scientific assistants may require advances in curating suitable training data and approaches to training those models.
  arXiv  Detail & Related papers  (2024-11-25T21:51:45Z)
• Machine Learning with Physics Knowledge for Prediction: A Survey [16.96920919164813]
  This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion.
  arXiv  Detail & Related papers  (2024-08-19T09:36:07Z)
• Improving Molecular Modeling with Geometric GNNs: an Empirical Study [56.52346265722167]
  This paper focuses on the impact of different canonicalization methods, (2) graph creation strategies, and (3) auxiliary tasks, on performance, scalability and symmetry enforcement. Our findings and insights aim to guide researchers in selecting optimal modeling components for molecular modeling tasks.
  arXiv  Detail & Related papers  (2024-07-11T09:04:12Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• A Review of Neuroscience-Inspired Machine Learning [58.72729525961739]
  Bio-plausible credit assignment is compatible with practically any learning condition and is energy-efficient. In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks. We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.
  arXiv  Detail & Related papers  (2024-02-16T18:05:09Z)
• Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences [4.442043151145212]
  This Perspective explores the sources and implications of diverse explanations in machine learning applications for physical sciences. We examine how different models, explanation methods, levels of feature attribution, and stakeholder needs can result in varying interpretations of ML outputs. Our analysis underscores the importance of considering multiple perspectives when interpreting ML models in scientific contexts.
  arXiv  Detail & Related papers  (2024-02-01T05:28:28Z)
• Large Language Models for Scientific Synthesis, Inference and Explanation [56.41963802804953]
  We show how large language models can perform scientific synthesis, inference, and explanation. We show that the large language model can augment this "knowledge" by synthesizing from the scientific literature. This approach has the further advantage that the large language model can explain the machine learning system's predictions.
  arXiv  Detail & Related papers  (2023-10-12T02:17:59Z)
• Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties [0.0]
  Several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. We develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science.
  arXiv  Detail & Related papers  (2023-09-01T07:29:44Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)
• Drug discovery with explainable artificial intelligence [0.0]
  There is a demand for 'explainable' deep learning methods to address the need for a new narrative of the machine language of the molecular sciences. This review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and dares a forecast of the future opportunities, potential applications, and remaining challenges.
  arXiv  Detail & Related papers  (2020-07-01T14:36:23Z)
• Visual Grounding of Learned Physical Models [66.04898704928517]
  Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. We present a neural model that simultaneously reasons about physics and makes future predictions based on visual and dynamics priors. Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.
  arXiv  Detail & Related papers  (2020-04-28T17:06:38Z)

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.