Formation Energy

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

14 leaderboard tables shown for this task, 14 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 14 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

8 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 31 papers with code (54 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Neural Message Passing for Quantum Chemistry
  4 Apr 2017 20 repositories listed Syntology ran 3 of 3 samples · 0 unverified · 1 pointer-only (licence)
  Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
• Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
  8 Jun 2018 5 repositories listed Syntology ran 4 of 22 samples · 18 unverified · 4 pointer-only (licence)
  Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials.
• SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
  26 Jun 2017 5 repositories listed
  Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
• Directional Message Passing for Molecular Graphs
  6 Mar 2020 4 repositories listed Syntology ran 1 of 10 samples · 9 unverified · 3 pointer-only (licence)
  Each message is associated with a direction in coordinate space.
• Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
  28 Aug 2023 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We present Matbench Discovery, an evaluation framework for ML energy models, applied as pre-filters for high-throughput searches of stable inorganic crystals.
• TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials
  10 Jun 2023 3 repositories listed Syntology ran 3 of 7 samples · 4 unverified
  The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research.
• Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
  15 Nov 2020 3 repositories listed
  The prediction of physicochemical properties from molecular structures is a crucial task for artificial intelligence aided molecular design.
• Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability
  7 Feb 2023 2 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  We compare the performance of xGPR with the reported performance of various deep learning models on 20 benchmarks, including small molecule, protein sequence and tabular data.
• Periodic Graph Transformers for Crystal Material Property Prediction
  23 Sep 2022 2 repositories listed Syntology ran 4 of 9 samples · 5 unverified
  Our Matformer is designed to be invariant to periodicity and can capture repeating patterns explicitly.
• Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
  15 May 2019 2 repositories listed
  The possibilities for prediction in a realistic computational screening setting is investigated on a dataset of 5976 ABSe₃ selenides with very limited overlap with the OQMD training set.
• AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use
  19 May 2025 1 repository listed
  Machine learning-based interatomic potentials and force fields depend critically on accurate atomic structures, yet such data are scarce due to the limited availability of experimentally resolved crystals.
• InvDesFlow-AL: Active Learning-based Workflow for Inverse Design of Functional Materials
  14 May 2025 1 repository listed
  Generative models based on diffusion principles can directly produce new materials that meet performance constraints, thereby significantly accelerating the material design process.
• MatMMFuse: Multi-Modal Fusion model for Material Property Prediction
  30 Apr 2025 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  In this work, we propose Material Multi-Modal Fusion(MatMMFuse), a fusion based model which uses a multi-head attention mechanism for the combination of structure aware embedding from the Crystal Graph Convolution…
• A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation
  30 Jan 2025 1 repository listed
  In diffraction-based crystal structure analysis, thermal ellipsoids, quantified via Anisotropic Displacement Parameters (ADPs), are critical yet challenging to determine.
• Unleashing the power of novel conditional generative approaches for new materials discovery
  5 Nov 2024 1 repository listed
  For a very long time, computational approaches to the design of new materials have relied on an iterative process of finding a candidate material and modeling its properties.
• Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informatics
  30 Jun 2024 1 repository listed
  This allows the pretraining of supervised models on large materials datasets without the need for property labels and without requiring the model to reconstruct the crystal from a representation vector.
• Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization
  28 May 2024 1 repository listed
  Generative modeling of crystal structures is significantly challenged by the complexity of input data, which constrains the ability of these models to explore and discover novel crystals.
• Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
  11 Jan 2024 1 repository listed
  Meanwhile, we report the first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a band gap of 2.
• Crystal-GFN: sampling crystals with desirable properties and constraints
  7 Oct 2023 1 repository listed
  Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
• Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
  12 Jun 2023 1 repository listed
  This is enabled by our approximations of infinite potential summations, where we extend the Ewald summation for several potential series approximations with provable error bounds.
• CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materials
  14 Jan 2023 1 repository listed Syntology ran 1 of 2 samples · 1 unverified
  To leverage these untapped data, this paper presents CrysGNN, a new pre-trained GNN framework for crystalline materials, which captures both node and graph level structural information of crystal graphs using a huge…
• Discovery of 2D materials using Transformer Network based Generative Design
  14 Jan 2023 1 repository listed
  Recently, deep learning, data-mining, and density functional theory (DFT)-based high-throughput calculations are widely performed to discover potential new materials for diverse applications.
• Materials Property Prediction with Uncertainty Quantification: A Benchmark Study
  4 Nov 2022 1 repository listed
  Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models.
• OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials Science
  30 Sep 2022 1 repository listed
  We introduce a large-scale dataset of quantum-mechanically calculated properties of crystalline materials for graph representation learning that contains approximately 900k entries (OQM9HK).
• Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
  27 Mar 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Discovering new materials is a challenging task in materials science crucial to the progress of human society.
• Semi-supervised teacher-student deep neural network for materials discovery
  12 Dec 2021 1 repository listed
  For synthesizability prediction, our model significantly increases the baseline PU learning's true positive rate from 87.
• Distributed Representations of Atoms and Materials for Machine Learning
  30 Jul 2021 1 repository listed
  To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required.
• Heterogeneous Molecular Graph Neural Networks for Predicting Molecule Properties
  26 Sep 2020 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  As they carry great potential for modeling complex interactions, graph neural network (GNN)-based methods have been widely used to predict quantum mechanical properties of molecules.
• Crystal Graph Neural Networks for Data Mining in Materials Science
  27 May 2019 1 repository listed
  This paper proposes crystal graph neural networks (CGNNs) that use no bond distances, and introduces a scale-invariant graph coordinator that makes up crystal graphs for the CGNN models to be trained on the dataset…
• DScribe: Library of Descriptors for Machine Learning in Materials Science
  18 Apr 2019 1 repository listed
  DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations.

Syntology lines on 11 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.