Materials & Chemicals

Materials

A large-scale pretraining corpus for materials science, containing approximately 780M tokens of RefinedWeb data, designed for training AI models for materials discovery and molecular dynamics research.

Materials

A technical Q&A dataset covering fundamental and advanced topics in materials science, including material properties, synthesis methods, characterization techniques, and engineering applications, designed for training technical response models.

Materials

A comprehensive materials science dataset with ~500K peer-reviewed publications, 139K phase diagrams, 409K crystalline nanostructures, and 1M+ physical property sets, enabling computational materials design and ML-driven materials informatics research.

Materials

A materials science demonstration dataset hosted on HuggingFace under the SciFy account, showcasing data organization and access patterns for materials science applications.

Materials

A materials science dataset hosted on HuggingFace, supporting machine learning and materials science cross-disciplinary research, containing relevant domain training or evaluation samples.

Materials

A collection of domain ontologies for materials science and engineering, providing semantic organization of core concepts including structure, properties, processing, and performance for knowledge reasoning applications.

Materials

Presents a general theory for rolling arbitrary 2D crystals into nanotubes, implemented as open-source NTBuilder with desktop and web interfaces, including a catalog of 20 million nanotube structures.

Materials Science / First Principles

Google DeepMind's GNN system for materials discovery predicting 2.2M new crystal structures with 380k stable ones, expanding known stable materials by nearly an order of magnitude.

Molecular Dynamics / ML Interatomic Potential

A general-purpose machine-learned interatomic potential built on an equivariant graph neural network, trained on 150k public inorganic crystals, that runs stable molecular dynamics out of the box for solids, liquids, gases and reactions, and can be fine-tuned to ab initio accuracy with only a handful of configurations.

Survey / Reading Material

A systematic survey of operator learning covering the theory, architectures and error analysis of DeepONet, FNO and their variants - the reference entry point to learning infinite-dimensional maps.

Original Research Paper

ICLR 2026 paper proposing ATOM, a quasi-equivariant pretrained transformer neural operator that requires no explicit molecular graph and uses temporal attention for parallel decoding of multiple future states; curates TG80 (80 compounds, 2.5M fs trajectories) and demonstrates zero-shot generalization on MD17/RMD17/MD22.

Materials

Presents FRPN, a Full-Resolution Polymer Network that retains both atom-level details and monomer-level connectivity for multi-scale polymer property prediction.

Materials

Investigating nonequilibrium phase-ordering kinetics in binary mixtures of uniaxial ellipsoidal particles using large-scale molecular dynamics simulations with anisotropic Gay-Berne potential, revealing two-stage ordering processes across different time scales after temperature quench.

Materials

Presents the first complete machine learning method for accelerating plane-wave DFT in materials under the PAW formalism, formalizing seven criteria that a Complete Neural Electronic Initializer must satisfy and addressing two missing structure-dependent components: augmentation occupancies and spin initialization.

Quantum/Chemistry

A physics-informed dual-state query learning framework for GPCR ligand bioactivity prediction, addressing the neglect of dynamic conformational equilibria in existing deep learning approaches to improve functional efficacy discrimination.

Quantum/Chemistry

Proposes finite-state automata encoding recursive lattice geometry for exact MPO construction of tight-binding Hamiltonians across fractals, quasicrystals, and hyperbolic lattices.

Materials

A method to predict how force correction changes energy exchange and structural statistics in molecular dynamics by measuring leading response coefficients on shared reference trajectories.

Materials

Hybrid quantum neural network extracts quantitative parameters like phase weight fractions directly from 1D powder diffraction patterns without iterative refinement, suitable for complex multiphase materials and noisy measurements.

Materials

Investigates the relationship between residual scale and learnability in Δ-learning, demonstrating through molecular graph neural network evaluation on total energy targets that smaller residual targets do not necessarily yield better learning outcomes.

Materials

Proposes SO(3) Equivariant Neural Kalman Networks (SENK) with response-state cascade to improve transferable vibrational spectral prediction across chemical spaces using equivariant transformer backbone.

ScientificML

Proposes a nearly-instant latent space Bayesian optimization method that replaces expensive sequential decision-making with sampling overhead, making BO practical for generative model-driven de novo design pipelines with cheap virtual screens.

Materials

Presents a method using sparse differentiation to compute exact molecular Hessians from machine learning interatomic potentials, reducing computational complexity from O(N²) to O(N), overcoming limitations for large atomistic systems.

Materials

Develops a general theory of rolling construction for arbitrary 2D crystals to create nanotubes, implementing an open-source library NTBuilder with desktop and web interfaces, providing a catalog of 20 million structures with exact unit cell calculations.

Quantum/Chemical

MARC-TS introduces a two-stage framework that learns endpoint-conditioned continuous reaction paths, queries at arbitrary resolution, and refines transition-state candidates via local path context, with T1x-IRC-8K dataset of 8,209 reactions and 1.09M path-resolved geometries.

Electromagnetics

Presents a 3D multiphysics FEM framework for high-frequency induction tube welding, coupling time-harmonic magnetic scalar potential with quasi-stationary heat transport for coupled electromagnetic-thermal simulation.

Materials

Neural networks trained on 4DSTEM diffraction patterns enable rapid identification of LiNiO2 and NiO material phases, providing a computationally efficient approach for analyzing large sets of materials image data.

CFD

An SO(3)-equivariant neural network predicts the full 6×6 grand resistance matrix for microparticles in Stokes flow, outperforming traditional scalar drag correlations by preserving orientation dependence and rotational response.

Materials

A region-held-out evaluation protocol for hydrogen embrittlement detection in 316L stainless steel, preventing data leakage from same specimen regions, comparing texture features vs deep learning for SEM micrograph classification.

Materials

A self-driving laboratory strategy that simultaneously and dynamically assesses composition-dependent synthetic accessibility and target properties via aggregated cost functions to accelerate optimization of multicomponent NASICON fast ionic conductors while maintaining phase purity.

Materials

A physics-informed computational screening framework that decomposes effective second-harmonic response to optimize nonlinear optical properties of 2D semiconductor/organic molecule interfaces.