Thermodynamic assessment of machine learning models for solid-state synthesis prediction

• URL: http://arxiv.org/abs/2602.04075v1
• Date: Tue, 03 Feb 2026 23:25:54 GMT
• Title: Thermodynamic assessment of machine learning models for solid-state synthesis prediction
• Authors: Jane Schlesinger, Simon Hjaltason, Nathan J. Szymanski, Christopher J. Bartel,
• Abstract summary: Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized.<n>Here, we assess the alignment of several recently introduced synthesis prediction models with material and reaction thermodynamics.<n>We find these models generally overpredict the likelihood of synthesis, but some model scores do trend with thermodynamics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning models have recently emerged to predict whether hypothetical solid-state materials can be synthesized. These models aim to circumvent direct first-principles modeling of solid-state phase transformations, instead learning from large databases of successfully synthesized materials. Here, we assess the alignment of several recently introduced synthesis prediction models with material and reaction thermodynamics, quantified by the energy with respect to the convex hull and a metric accounting for thermodynamic selectivity of enumerated synthesis reactions. A dataset of successful synthesis recipes was used to determine the likely bounds on both quantities beyond which materials can be deemed unlikely to be synthesized. With these bounds as context, thermodynamic quantities were computed using the CHGNet foundation potential for thousands of new hypothetical materials generated using the Chemeleon generative model. Four recently published machine learning models for synthesizability prediction were applied to this same dataset, and the resultant predictions were considered against computed thermodynamics. We find these models generally overpredict the likelihood of synthesis, but some model scores do trend with thermodynamic heuristics, assigning lower scores to materials that are less stable or do not have an available synthesis recipe that is calculated to be thermodynamically selective. In total, this work identifies existing gaps in machine learning models for materials synthesis and introduces a new approach to assess their quality in the absence of extensive negative examples (failed syntheses).

Related papers

• Valid Inference with Imperfect Synthetic Data [39.10587411316875]
  We introduce a new estimator based on generalized method of moments.<n>We find that interactions between the moment residuals of synthetic data and those of real data can greatly improve estimates of the target parameter.
  arXiv  Detail & Related papers  (2025-08-08T18:32:52Z)
• SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction [0.0]
  We present SynCoTrain, a semi-supervised machine learning model designed to predict the synthesizability of materials. Our approach uses Positive and Unlabeled (PU) Learning to address the absence of explicit negative data. The model demonstrates robust performance, achieving high recall on internal and leave-out test sets.
  arXiv  Detail & Related papers  (2024-11-18T19:53:19Z)
• Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification [11.6055501181235]
  We investigate the use of verification on synthesized data to prevent model collapse. We show that verifiers, even imperfect ones, can indeed be harnessed to prevent model collapse.
  arXiv  Detail & Related papers  (2024-06-11T17:46:16Z)
• How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse [9.59833542807268]
  Model collapse occurs when new models are trained on synthetic data generated from previously trained models. We show that model collapse cannot be avoided when training solely on synthetic data. We estimate a maximal amount of synthetic data below which model collapse can eventually be avoided.
  arXiv  Detail & Related papers  (2024-04-07T22:15:13Z)
• A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites [0.0]
  This work proposes a physics-informed deep learning (PIDL)-based model for investigating the viscoelastic-viscoplastic behavior of short fiber-reinforced nanoparticles-filled epoxies under various ambient conditions. The PIDL model can accurately predict the mechanical behavior of epoxy-based nanocomposites for different volume fractions of fibers and nanoparticles under various hygrothermal conditions.
  arXiv  Detail & Related papers  (2024-03-27T07:22:32Z)
• Retrosynthesis prediction enhanced by in-silico reaction data augmentation [66.5643280109899]
  We present RetroWISE, a framework that employs a base model inferred from real paired data to perform in-silico reaction generation and augmentation. On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models.
  arXiv  Detail & Related papers  (2024-01-31T07:40:37Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Synthetic pre-training for neural-network interatomic potentials [0.0]
  We show that synthetic atomistic data, themselves obtained at scale with an existing machine learning potential, constitute a useful pre-training task for neural-network interatomic potential models. Once pre-trained with a large synthetic dataset, these models can be fine-tuned on a much smaller, quantum-mechanical one, improving numerical accuracy and stability in computational practice.
  arXiv  Detail & Related papers  (2023-07-24T17:16:24Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• RetroXpert: Decompose Retrosynthesis Prediction like a Chemist [60.463900712314754]
  We devise a novel template-free algorithm for automatic retrosynthetic expansion. Our method disassembles retrosynthesis into two steps. While outperforming the state-of-the-art baselines, our model also provides chemically reasonable interpretation.
  arXiv  Detail & Related papers  (2020-11-04T04:35:34Z)
• Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning [1.4680035572775534]
  We use reinforcement learning to predict optimal synthesis schedules for a prototypical quantum material, semiconducting monolayer MoS$_2$. The model can be extended to predict profiles for synthesis of complex structures including multi-phase heterostructures.
  arXiv  Detail & Related papers  (2020-09-14T20:50:45Z)
• Learning Graph Models for Retrosynthesis Prediction [90.15523831087269]
  Retrosynthesis prediction is a fundamental problem in organic synthesis. This paper introduces a graph-based approach that capitalizes on the idea that the graph topology of precursor molecules is largely unaltered during a chemical reaction. Our model achieves a top-1 accuracy of $53.7%$, outperforming previous template-free and semi-template-based methods.
  arXiv  Detail & Related papers  (2020-06-12T09:40:42Z)
• Retrosynthesis Prediction with Conditional Graph Logic Network [118.70437805407728]
  Computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. We propose a new approach to this task using the Conditional Graph Logic Network, a conditional graphical model built upon graph neural networks.
  arXiv  Detail & Related papers  (2020-01-06T05:36:57Z)

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.