jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi Xiao | Xulong Fan | Li Zeng
Source: Journal of Applied Physics (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Predicting water dynamics on nanopatterned surfaces using convolutional neural network algorithm Nanopatterns are widespread on surfaces and play a crucial role in water dynamics, which is particularly significant in the fields of surface science, electronic devices, and advanced materials. However, the relationship between nanopatterned surfaces and water diffusion is complex and requires further research. In this paper, we use a convolutional neural network (CNN) algorithm in conjunction with molecular dynamics (MD) simulation results as a database to predict the water diffusion behavior influenced by the nanopatterned distribution of oxygen-containing groups on graphene oxide (GO) surfaces. The results show that the CNN algorithm performs effectively in predicting water diffusion on regularly nanopatterned GO surfaces, achieving a Pearson correlation coefficient (r) of 0.984 and a coefficient of determination (R2) of 0.967. Furthermore, this trained CNN model was extended to predict the diffusion of water molecules on unseen irregularly nanopatterned GO surfaces, yielding an r of 0.956 and an R2 of 0.857, indicating promising but preliminary generalization capability. This work presents a novel methodology for predicting the water dynamics on nanopatterned surfaces, aiding in the design of micro- and nano-scale flow surfaces. Convolutional neural network ; Diffusion ; Generalization ; Molecular dynamics ; Dynamics (music) ; Graphene ; Artificial neural network ; Water model Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Nanopore and Nanochannel Transport Studies
Authors: Lily Wang | Irfan Alibay | Simon Boothroyd | Chapin E. Cavender | Joshua Horton | Alexandra R. McIsaac | Joshua A. Mitchell | Matthew Thompson | Jeffrey Wagner | Brent R. Westbrook | Christopher Bayly | John Chodera | Daniel Cole | James Eastwood | Michael Shirts | David Mobley
Source: ChemRxiv (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Developing and benchmarking Sage 2.3.0 with the AshGC neural network charge model Partial atomic charges are a fundamental component underlying classical molecular simulations, but assigning charges remains a computational bottleneck; many common methods rely on quantum mechanical calculations that scale poorly with molecular size and are sensitive to the choice of conformer geometry. We introduce Open Force Field (OpenFF) AshGC, a new graph convolutional neural network charge model, as well as the Sage 2.3.0 small molecule force field for drug-like molecules parameterized to be consistent with AshGC. AshGC is designed to efficiently produce conformer- independent charges of semi-empirical quality at linear cost for molecules of all sizes, from small molecules to macromolecules. AshGC largely generates charges within the range of other accepted AM1-BCC backends such as OpenEye’s oequacpac and AmberTools’ sqm, deviating most in smaller molecules between 4-9 heavy atoms, in negatively charged molecules, and areas of chemistry underrepresented in the training set, such as particular sulfur- and phosphorus-containing functional groups. We further present the development and performance of Sage 2.3.0, which has both Lennard-Jones and valence parameters that are re-trained to be consistent with neural network charges for the first time. Benchmarks spanning gas-phase geometry optimization through protein–ligand binding free energies show Sage 2.3.0 performs comparably to earlier Sage releases, with modest improvements in condensed-phase properties and a slight decrease in non-aqueous solvation free energy accuracy. As with other OpenFF force fields, Sage 2.3.0 was validated in protein-ligand benchmarks to be compatible with Amber protein force fields. All data are publicly available, along with scripts and environments for reproducing the training and benchmarking of AshGC and Sage 2.3.0 at https://github.com/openforcefield/ashgc-v1.0-fit and github.com/openforcefield/ash-sage-rc2 respectively. Benchmarking ; Parameterized complexity ; Artificial neural network ; Solvation ; Charge (physics) ; Implicit solvation ; Electrostatics ; Convolutional neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Matthew A. H. Walker | Zibo Zhang | Junayd Ul Islam | Keith T. Butler
Source: Open MIND (preprint, 2026-02-25)
Field: Materials Science — Materials Chemistry

Discovering new photovoltaics using optimal transport theory Searching by chemical and structural analogy is one of the most commonly used and successful approaches to materials discovery. However, formulating this task for algorithmic implementation raises the question of how we define similar materials. Methods have been proposed for searching materials space using vectors based on chemical composition and functional fragments in the material. Descriptors for structural similarity have also been proposed. However, the question of how to incorporate and balance structural and compositional similarity measures in a single metric remains open. Here, we adapt methods developed for calculating distances between undirected graphs and apply them to crystalline materials similarity. The Fused Gromov-Wasserstein (FGW) metric uses optimal transport theory to map between two graphs considering a balance of the graph structure and the information present at the nodes of the graph (atoms in crystals). We apply the method to exploring new photovoltaic materials. We demonstrate that FGW is competitive with embeddings from an equivariant graph neural network, trained on $&gt; 10^6$ materials, despite minimal training. We then apply FGW to a discovery campaign to identify materials from the Materials Project database that have not previously been explored as photovoltaics, but have similarities to known high-efficiency materials. After validating predictions with hybrid density functional theory, we identify seven previously unexplored high-efficiency photovoltaic absorber candidates, including Cs$_5$Sb$_8$, which is found to have a predicted SLME of $&gt; 30\%$ and to be thermodynamically stable. The FGW approach demonstrates the power of strong inductive biases for developing metrics for materials exploration with minimal training data. Metric (unit) ; Analogy ; Photovoltaic system ; Similarity (geometry) ; Metric space ; Graph ; Graph theory ; Photovoltaics ; Data structure Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Wei Tao | Liang Shen | Yufeng Xue | Shicheng Yuan | Tingxuan Chen | HU Qinhua | Wenjie Wu | X.L. Chen | Enjie Wang | Ye Liu | Thomas Maurer | Dengfeng Peng
Source: Research Square (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

Mechanoluminescence by Direct Laser Writing: A Seconds-Scale Fabrication Strategy Mechanoluminescence ; Fabrication ; Laser ; Luminescence ; Linearity ; Microelectromechanical systems Luminescence Properties of Advanced Materials ; Solid State Laser Technologies ; Laser-Ablation Synthesis of Nanoparticles
Authors: Jaemin Jeong | Moonsoo Ra | Jinha Jeong | Seungsoon Jang | Woong Lee
Source: Journal of the Korean Ceramic Society (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Determination of space groups and component systems from selected area electron diffraction patterns by deep learning Convolutional neural network ; Generalization ; Deep learning ; Pattern recognition (psychology) ; Component (thermodynamics) ; Artificial neural network ; Residual ; Reciprocal lattice ; Principal component analysis Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Wanqi Du | Zuoyi Liu | Bo Hu | Yonghai Feng | Minjia Meng
Source: Trends in Chemistry (article, 2026-01-20)
Field: Chemistry — Analytical Chemistry

Molecularly imprinted functional monomers: from tradition to customization Molecularly imprinted polymer ; Personalization ; Set (abstract data type) ; Rational design ; Field (mathematics) ; Paradigm shift Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Covalent Organic Framework Applications
Authors: Fanqiang Zeng | Changchun Ji | Yudong Wang | Yousheng Li
Source: New Carbon Materials (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

A review of covalent organic framework materials for CO2 adsorption Adsorption ; Covalent bond ; Zeolite ; Metal-organic framework ; Carbon fibers ; Carbon dioxide ; Covalent organic framework Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Shane Thompson | Daniel Gunlycke
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

Auto-regressive Neural Quantum State Sampling for Selected Configuration Interaction Accurate ground-state energy calculations remain a central challenge in quantum chemistry due to the exponential scaling of the many-body Hilbert space. Variational Monte Carlo and variational quantum eigensolvers offer promising ansatz optimization approaches but face limitations in convergence as well as hardware constraints. We introduce a particular Selected Configuration Interaction (SCI) algorithm that uses auto-regressive neural networks (ARNNs) to guide subspace expansion for ground-state search. Leveraging the unique properties of ARNNs, our algorithm efficiently constructs compact variational subspaces from learned ground-state statistics, which in turn accelerates convergence to the ground-state energy. Benchmarks on molecular systems demonstrate that ARNN-guided subspace expansion combines the strengths of neural-network representations and classical subspace methods, providing a scalable framework for classical and hybrid quantum-classical algorithms. Ansatz ; Subspace topology ; Linear subspace ; Hilbert space ; Convergence (economics) ; Scalability ; Artificial neural network ; Scaling ; Quantum ; Monte Carlo method Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Alexandre Blanco-González | Thea K. Schulze | Evianne Rovers | Joe G. Greener
Source: arXiv (Cornell University) (preprint, 2026-03-17)
Field: Materials Science — Materials Chemistry

Training a force field for proteins and small molecules from scratch Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a graph neural network that assigns all force field parameters for diverse molecules using continuous atom typing. The freely-available model, called Garnet, was trained on quantum mechanical, condensed phase and protein nuclear magnetic resonance data without the use of existing parameters. The resulting force field shows comparable performance to current force fields on small molecules, folded proteins, protein complexes and disordered proteins. It shows similar results to popular approaches for relative binding free energy predictions across a range of targets. Assessing different functional forms shows that the double exponential potential is a flexible and accurate alternative to the Lennard-Jones potential. Garnet provides a platform for automated, reproducible force field discovery that brings the benefits of machine learning to classical force fields. Force field (fiction) ; Transferability ; Limiting ; Magnetic field ; Field (mathematics) ; Range (aeronautics) ; Small molecule ; Scratch ; Quantum Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Yu Zhang | Bai Xue | Lingjun Zeng | Xiaoke Bu | Lan Xie | Quanwei Chen | Junhua Zhang
Source: Journal of Material Science and Technology (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Interpretable high-throughput machine learning-based screening of high-thermal-conductivity composites with experimental validation Interpretability ; Categorical variable ; Feature (linguistics) ; Reliability (semiconductor) ; Artificial neural network ; Support vector machine ; Feature vector ; Thermal conductivity ; Pattern recognition (psychology) ; Aerospace Thermal properties of materials ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Yongqi Jin | Yecheng Wang | Jun-jie Wang | Rong Zhu | Guolin Ke | Weinan E
Source: ArXiv.org (article, 2026-01-26)
Field: Chemistry — Spectroscopy

From Human Labels to Literature: Semi-Supervised Learning of NMR Chemical Shifts at Scale Accurate prediction of nuclear magnetic resonance (NMR) chemical shifts is fundamental to spectral analysis and molecular structure elucidation, yet existing machine learning methods rely on limited, labor-intensive atom-assigned datasets. We propose a semi-supervised framework that learns NMR chemical shifts from millions of literature-extracted spectra without explicit atom-level assignments, integrating a small amount of labeled data with large-scale unassigned spectra. We formulate chemical shift prediction from literature spectra as a permutation-invariant set supervision problem, and show that under commonly satisfied conditions on the loss function, optimal bipartite matching reduces to a sorting-based loss, enabling stable large-scale semi-supervised training beyond traditional curated datasets. Our models achieve substantially improved accuracy and robustness over state-of-the-art methods and exhibit stronger generalization on significantly larger and more diverse molecular datasets. Moreover, by incorporating solvent information at scale, our approach captures systematic solvent effects across common NMR solvents for the first time. Overall, our results demonstrate that large-scale unlabeled spectra mined from the literature can serve as a practical and effective data source for training NMR shift models, suggesting a broader role of literature-derived, weakly structured data in data-centric AI for science. Chemical shift ; Robustness (evolution) ; Matching (statistics) ; Spectral line ; NMR spectra database ; Generalization ; Set (abstract data type) ; Training set Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Matthew A. H. Walker | Zibo Zhou | Junayd Ul Islam | Keith T. Butler
Source: ArXiv.org (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Discovering new photovoltaics using optimal transport theory Searching by chemical and structural analogy is one of the most commonly used and successful approaches to materials discovery. However, formulating this task for algorithmic implementation raises the question of how we define similar materials. Methods have been proposed for searching materials space using vectors based on chemical composition and functional fragments in the material. Descriptors for structural similarity have also been proposed. However, the question of how to incorporate and balance structural and compositional similarity measures in a single metric remains open. Here, we adapt methods developed for calculating distances between undirected graphs and apply them to crystalline materials similarity. The Fused Gromov-Wasserstein (FGW) metric uses optimal transport theory to map between two graphs considering a balance of the graph structure and the information present at the nodes of the graph (atoms in crystals). We apply the method to exploring new photovoltaic materials. We demonstrate that FGW is competitive with embeddings from an equivariant graph neural network, trained on $> 10^6$ materials, despite minimal training. We then apply FGW to a discovery campaign to identify materials from the Materials Project database that have not previously been explored as photovoltaics, but have similarities to known high-efficiency materials. After validating predictions with hybrid density functional theory, we identify seven previously unexplored high-efficiency photovoltaic absorber candidates, including Cs$_5$Sb$_8$, which is found to have a predicted SLME of $> 30\%$ and to be thermodynamically stable. The FGW approach demonstrates the power of strong inductive biases for developing metrics for materials exploration with minimal training data. Metric (unit) ; Analogy ; Photovoltaic system ; Similarity (geometry) ; Metric space ; Graph ; Graph theory ; Photovoltaics ; Data structure Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Cesare Malosso | Filippo Bigi | Paolo Pegolo | Joseph W. Abbott | Philip R. Loche | Mariana Rossi | Michele Ceriotti | Arslan Mazitov
Source: arXiv (Cornell University) (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

High-quality, high-information datasets for universal atomistic machine learning The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many widely used electronic-structure databases are assembled having materials screening as primary goal rather than robust force-field learning, are limited in their scope to a specific class of chemical compounds, and/or employ inconsistent DFT functionals and settings. Here we introduce MAD-1.5, a highly curated dataset designed explicitly for training broadly applicable atomistic models across the periodic table at high levels of theory. MAD-1.5 extends the MAD dataset with targeted enrichment strategies that improve the coverage of chemical space to 102 elements while keeping the total number of configurations compact. All structures are computed with a single, standardized all-electron DFT workflow using the r$^2$SCAN meta-GGA functional and consistent convergence settings, ensuring uniformity across chemically heterogeneous systems. The dataset encompasses molecules, clusters, bulk crystals, surfaces, and low-dimensional structures, and its quality and consistency are further enhanced by outlier removal using uncertainty quantification. We demonstrate the high accuracy that can be achieved with the proposed dataset by training PET-MAD-1.5, a generally applicable r$^2$SCAN interatomic potential that covers 102 elements in the periodic table and achieves exceptional levels of benchmark accuracy and stability in challenging simulation protocols. Workflow ; Stability (learning theory) ; Benchmark (surveying) ; Consistency (knowledge bases) ; Outlier ; Convergence (economics) ; Table (database) ; Anomaly detection ; Class (philosophy) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Connor Edwards | Oliver M. Linder‐Patton | Jack D. Evans
Source: ArXiv.org (article, 2026-01-23)
Field: Chemistry — Inorganic Chemistry

Simulations of High Temperature Decomposition of Metal-Organic Frameworks to form Amorphous Catalysts Metal-organic framework (MOF) derived materials formed through high temperature processes show great potential as catalysts. However, understanding of structure-property relationships between the initial MOF and the resulting MOF-derived catalyst is limited because the amorphous nature of the catalyst challenges standard structural characterization methods. Neural network approaches that learn interatomic potentials from density functional theory offer a promising solution. We simulated the pyrolysis of UiO-66, UiO-67 and MIP-206 using both foundational and fine-tuned machine learned interatomic potentials (MLIPs). To mimic experimental conditions, an atmosphere of CO2 and H2 was introduced and the structures were doped with 20 wt% copper to probe the effect of copper on the structural evolution of MOFs. These simulations provide atomistic insights into gas evolution, metal nanoparticle formation, and linker decomposition that were compared to available experimental data. Overall, this work demonstrates the potential of MLIPs to accurately model high temperature MOF dynamics under experimentally relevant conditions and guide the design of new catalytic materials. Amorphous solid ; Catalysis ; Decomposition ; Characterization (materials science) ; Molecular dynamics ; Nanoparticle ; Work (physics) Metal-Organic Frameworks: Synthesis and Applications ; Thermal Expansion and Ionic Conductivity ; Catalysts for Methane Reforming
Authors: Connor Edwards | Oliver M. Linder‐Patton | Jack D. Evans
Source: arXiv (Cornell University) (preprint, 2026-01-23)
Field: Chemistry — Inorganic Chemistry

Simulations of High Temperature Decomposition of Metal-Organic Frameworks to form Amorphous Catalysts Metal-organic framework (MOF) derived materials formed through high temperature processes show great potential as catalysts. However, understanding of structure-property relationships between the initial MOF and the resulting MOF-derived catalyst is limited because the amorphous nature of the catalyst challenges standard structural characterization methods. Neural network approaches that learn interatomic potentials from density functional theory offer a promising solution. We simulated the pyrolysis of UiO-66, UiO-67 and MIP-206 using both foundational and fine-tuned machine learned interatomic potentials (MLIPs). To mimic experimental conditions, an atmosphere of CO2 and H2 was introduced and the structures were doped with 20 wt% copper to probe the effect of copper on the structural evolution of MOFs. These simulations provide atomistic insights into gas evolution, metal nanoparticle formation, and linker decomposition that were compared to available experimental data. Overall, this work demonstrates the potential of MLIPs to accurately model high temperature MOF dynamics under experimentally relevant conditions and guide the design of new catalytic materials. Amorphous solid ; Catalysis ; Decomposition ; Characterization (materials science) ; Molecular dynamics ; Nanoparticle ; Work (physics) Metal-Organic Frameworks: Synthesis and Applications ; Thermal Expansion and Ionic Conductivity ; Catalysts for Methane Reforming