jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maria Petrov | Raymond G. Schireman | Davide Mitoli | Michael T. Ruggiero
Source: ChemRxiv (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Deep Learning Approaches for Determining Solid-State Anharmonic Phonon Dynamics and Thermodynamics: Quantum Accuracy without the Cost Understanding lattice dynamics is essential for explaining and predicting the thermal, electronic, and spectroscopic behavior of crystalline materials, yet accurate simulations remain computationally prohibitive when treated using fully ab initio methods, particularly for phonon dispersion, thermodynamics, and anharmonic vibrational effects. Here we present a deep-learning-enabled quantum lattice dynamics framework that retains the rigor of quantum mechanical vibrational analysis while dramatically reducing computational cost. Using ice-XI as a model system due to its well-known anharmonic dynamics, we integrate machine-learned interatomic potentials into a conventional solid-state vibrational workflow 1 to compute harmonic phonons, phonon dispersion relations, quasi-harmonic thermodynamic properties, and fully anharmonic vibrational dynamics using the vibrational self-consistent field (VSCF) and vibrational configuration interaction (VCI) methods. Remarkably, the transferable ANI-2x neural network potential reproduces many solid-state vibrational properties with good fidelity despite being trained only on molecular datasets. We further demonstrate that training a system-specific E(3)-equivariant neural network potential on a modest set of ab initio configurations yields near-DFT accuracy across harmonic and anharmonic observables, including coupled-mode interactions and two-quantum vibrational excitations. The resulting models reproduce DFT frequencies with sub-percent errors while reducing the computational cost of lattice-dynamical simulations by orders of magnitude. These results establish a practical route to quantum-accurate simulations of anharmonic phonons in complex molecular crystals-or even disordered solids-and demonstrate how deep learning can transform the computational study of solid-state vibrational dynamics. Anharmonicity ; Phonon ; Ab initio ; Molecular vibration ; Quantum ; Molecular dynamics ; Ab initio quantum chemistry methods ; Force field (fiction) Machine Learning in Materials Science ; Thermal properties of materials ; Crystallography and molecular interactions
Authors: Leon Schlosser | Nils Rendel | Julius Gemen | Frank Glorius | Kjell Jorner
Source: ChemRxiv (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Inverse Molecular Design for the Discovery of Organic Energy Transfer Photocatalysts: Bridging Global and Local Chemical Space Exploration The discovery of new organic photocatalysts (PCs) for energy transfer (EnT) catalysis remains a significant challenge, largely due to the vast and underexplored chemical space and the delicate balance of photocatalytic properties. While transition-metal catalysts are effective, their high cost and environmental impact necessitate the development of metal-free alternatives. In this work, we present a hybrid inverse molecular design strategy that combines global exploration with targeted local optimization to discover novel, highly efficient organic PCs. Our approach leverages a generative model, guided by machine learning predictions and semiempirical simulations, to efficiently navigate chemical space and identify promising molecular scaffolds. We demonstrate the utility of this strategy by rediscovering known PCs and, more importantly, by exploring uncharted structural regions, leading to the identification of novel candidates with favorable photophysical properties. A subsequent local exploration stage, using quantum mechanical calculations, allows refinement of the properties as well as to control the synthetic complexity. The practical applicability of the approach is demonstrated by performing a local exploration of one of the identified scaffolds and successfully synthesizing four candidate PCs. We showcase their catalytic aptitude in three different EnT-mediated reactions, including a challenging aza-photocycloaddition where one of our designed PCs achieved 90% yield, a performance comparable to a state-of-the-art iridium-based catalyst. This study highlights the power of a data-driven inverse design framework to bridge computational discovery and experimental validation, accelerating the identification of novel PCs and expanding the scope of EnT catalysis. Chemical space ; Bridging (networking) ; Space exploration ; Identification (biology) ; Design space exploration ; Inverse ; Quantum chemical ; Bridge (graph theory) Machine Learning in Materials Science ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Siddharth Sonti | Ambarish Kulkarni
Source: Journal of the Indian Institute of Science (article, 2026-04-01)
Field: Chemistry — Inorganic Chemistry

Computer Simulations of Zeolite Formation Nanoporous ; Scalability ; Monte Carlo method ; Sampling (signal processing) ; Molecular dynamics ; Zeolite ; Characterization (materials science) ; Fraction (chemistry) Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Suyue Yuan | Stephen E. Weitzner | Wonseok Jeong | Shenli Zhang | Bo Wang | Longsheng Feng | Jonas L. Kaufman | Kwangnam Kim | Yue Qi | Liwen F. Wan
Source: Chemical Reviews (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation The performance of rechargeable batteries is fundamentally influenced by the physicochemical properties and microstructural features of their key material components. Recent experimental advancements have highlighted the potential of single-crystal (SC) morphologies to address inherent limitations of polycrystalline (PC) electrodes and solid-state electrolytes, offering tunable charge transport kinetics and improved cell cycling performance. This review examines how state-of-the-art computational modeling, from atomistic and mesoscale to continuum-level approaches, including machine learning methodologies, has been utilized to investigate the critical factors governing the electrochemical behavior of SC battery materials. We explore how predictive modeling can elucidate the processing-structure-property-performance relationships of SC cathodes, anodes, and solid-state electrolytes, with a focus on unique SC characteristics such as crystallographic anisotropy, size effects, and facet-dependent properties. Additionally, we identify limitations in commonly used modeling techniques and discuss strategies to address these challenges. By integrating high-fidelity simulations with experimental insights, this review aims to outline a clear path for the rational design and optimization of SC battery components, paving the way for accelerated advancements in energy storage technologies. Battery (electricity) ; Electrochemical energy storage ; Energy storage ; Multiscale modeling ; Key (lock) ; Mesoscale meteorology ; Computational model ; Path (computing) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Nina Andrejevic | Ming Du | Hemant Sharma | James P. Horwath | Aileen Luo | Xiangyu Yin | Michael Prince | Brian H. Toby | Mathew J. Cherukara
Source: arXiv (Cornell University) (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

AlphaDiffract: Automated Crystallographic Analysis of Powder X-ray Diffraction Data Materials identification and structural understanding from powder X-ray diffraction (PXRD) data is a long-standing challenge in materials science, fundamental to discovering and characterizing novel materials. A prerequisite for full structure solution is the accurate determination of the crystal lattice, including lattice parameters and crystallographic symmetries. Traditional methods for this are iterative and typically require expert input, and while existing deep learning approaches have shown promise, a robust, single-shot method for comprehensive lattice determination from experimental data remains a key goal. Here, we introduce AlphaDiffract, a deep learning framework that achieves state-of-the-art performance in predicting the crystal system, space group, and lattice parameters directly from PXRD patterns. AlphaDiffract utilizes a 1D adaptation of the ConvNeXt architecture, a modern convolutional neural network that integrates key design principles from transformers, coupled with dedicated prediction heads for each crystallographic property. The model is trained on the largest-to-date physics-based dataset of over 31 million simulated diffraction patterns, generated by augmenting 312,267 curated structures from the ICSD and Materials Project databases. Crucially, it demonstrates strong generalization to experimental data, achieving 81.7% crystal system accuracy and 66.2% space group accuracy on the RRUFF dataset while additionally predicting all six lattice parameters. By providing a unified model for rapid and accurate lattice determination from PXRD data, AlphaDiffract represents a significant step forward in leveraging deep learning for high-throughput materials discovery. Diffraction ; Powder diffraction ; Lattice (music) ; Crystal structure ; Lattice constant ; Convolutional neural network ; Artificial neural network ; Reciprocal lattice X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Nuclear Physics and Applications
Authors: Woshitha Sasindu Hinget | Bhagya W.K. Piyushani | Galhenage Asha Sewvandi | Pradeep K. W. Abeygunawardhana
Source: Open MIND (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Comprehensive Screening Protocol for Generating Machine Learning-ready Datasets of Perovskite Materials This data-driven study presents a systematic sequential multiphase screening framework incorporating compositional and symmetry-based filtering, charge neutrality and geometric stability criteria to identify structurally and chemically stable oxide and halide perovskite candidates. The screening sequence effectively narrows down the scope of an extensive initial compositional space to a refined dataset consisting of 698 111 unique perovskites, including 1 154 single perovskites with numerically discovered O,F,Cl,Br and I-based compositions. This curated dataset provides a standardized dataset appropriate for downstream ML property predictions and experimental studies, to fast track the novel perovskite materials discovery for energy and optoelectronic applications. Perovskite (structure) ; Stability (learning theory) ; Protocol (science) ; Property (philosophy) ; Scope (computer science) ; Energy (signal processing) ; Sequence (biology) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Jia Zhao | Sen Zhai | jinku yu | Kenjiro Sugio | Gen Sasaki
Source: Physica B Condensed Matter (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

Machine Learning–Assisted Prediction of formation energies in double-doped aluminum alloys Aluminium ; Energy (signal processing) ; Work (physics) ; Aluminum oxide Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Titanium Alloys Microstructure and Properties
Authors: Woshitha Sasindu Hinget | Bhagya W.K. Piyushani | Galhenage Asha Sewvandi | Pradeep K. W. Abeygunawardhana
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Comprehensive Screening Protocol for Generating Machine Learning-ready Datasets of Perovskite Materials This data-driven study presents a systematic sequential multiphase screening framework incorporating compositional and symmetry-based filtering, charge neutrality and geometric stability criteria to identify structurally and chemically stable oxide and halide perovskite candidates. The screening sequence effectively narrows down the scope of an extensive initial compositional space to a refined dataset consisting of 698 111 unique perovskites, including 1 154 single perovskites with numerically discovered O,F,Cl,Br and I-based compositions. This curated dataset provides a standardized dataset appropriate for downstream ML property predictions and experimental studies, to fast track the novel perovskite materials discovery for energy and optoelectronic applications. Perovskite (structure) ; Stability (learning theory) ; Protocol (science) ; Property (philosophy) ; Scope (computer science) ; Energy (signal processing) ; Sequence (biology) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Inorganic Chemistry and Materials
Authors: Deepak Saini | Bhanu Partap | Ajay Singh | Nisha Sharma
Source: Journal of Mountain Research (article, 2026-03-10)
Field: Materials Science — Polymers and Plastics

Computational Framework Powered by AI for the Development of Self-Healing Intelligent Materials New avenues for the creation of intelligent materials that can heal themselves have been made possible by the combination of computational modelling and artificial intelligence (AI). These materials have enormous potential for use in robotics, biomedical devices, civil engineering, and aerospace since they can self-heal damage and restore performance. In order to maximize the creation, simulation, and prediction modelling of self-healing materials, this study introduces a novel computational framework driven by artificial intelligence. The suggested methodology makes use of data-driven simulations, deep learning networks, and machine learning algorithms to precisely forecast self-healing behaviors under a range of mechanical and environmental circumstances. Effective material design, real-time damage detection, and self-repair process optimization are made possible by the framework's integration of cuttingedge AI techniques including reinforcement learning and neural networks. To improve forecast accuracy, multi-scale modeling techniques are also incorporated to model material behavior at all scales, from the molecular to the macroscopic. The framework's ability to forecast self-healing efficiency, optimize material attributes, and save experimental costs is illustrated through case examples. The findings show that AI-driven computational techniques offer a data-centric approach to material design and greatly increase the accuracy and efficiency of self-healing material production. This study transforms the fields of structural engineering and smart materials by opening the door for intelligent materials with improved sustainability, durability, and self-healing properties. Aerospace ; Process (computing) ; Reinforcement learning ; Computational model ; Artificial neural network ; Computational intelligence ; Range (aeronautics) ; Deep learning Polymer composites and self-healing ; Machine Learning in Materials Science ; Topology Optimization in Engineering
Authors: Constantin Richard | Paul-Adrien Pichon | Jaroslava Nováková | Neda Irannejad Najafabadi | Jacinto Sá
Source: Discover Nano (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Application of machine learning to nanomaterial design with silver nanoprisms Achieving reproducible synthesis of nanomaterials with tunable optical and morphological properties remains a central challenge in materials design. Conventional trial-and-error strategies struggle with the nonlinear transitions governing nanoparticle growth, often limiting control over plasmonic responses. Here, we introduce a convolutional neural network (CNN) framework that couples in situ time-resolved UV-Vis spectroscopy with the synthesis of silver nanoprisms, extracting predictive rules for morphology and optical behavior. By leveraging transient spectral dynamics rather than endpoint data alone, the model captures hidden growth pathways and accurately predicts final size distributions and plasmonic signatures from a modest experimental dataset. This machine-learning-assisted methodology integrates directly into synthesis workflows, reducing experimental burden while enhancing reproducibility. Beyond silver nanoprisms, the strategy provides a transferable route for rational design of nanomaterials with tailored optical functionalities, advancing the broader goal of data-driven materials design. Nanomaterials ; Plasmon ; Convolutional neural network ; Nanoparticle ; Artificial neural network ; Limiting ; Plasmonic nanoparticles Machine Learning in Materials Science ; Gold and Silver Nanoparticles Synthesis and Applications ; Laser-Ablation Synthesis of Nanoparticles
Authors: Tarvir Anjum Aditto | Vivek Chowdhury | Hafiz Imtiaz | Ahmed Zubair
Source: Materials Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-enabled prediction of the electronic band-edge shapes and properties of 2D transition metal dichalcogenide alloys A data-driven extra trees model predicts conduction and valence band structures of 2D TMD alloys directly from composition, achieving near-DFT accuracy while dramatically reducing computational cost. Density functional theory ; Ab initio ; Transition metal ; Electronic structure ; Computational learning theory 2D Materials and Applications ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Matthew Dominik
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

Constraint-Conditioned Closure Filtering for Reaction Pathway Prediction Chemical reactions emerge from the interplay between quantum-mechanically admissible molecular pathways and contextual constraint regimes. This work reframes reaction prediction by separating substrate-defined possibility from context-conditioned realization. A two-stage filtering architecture—accessibility and closure compatibility—is introduced to efficiently prune combinatorial reaction spaces prior to computationally expensive electronic-structure calculations. Accessibility filtering evaluates environmental admissibility, while closure compatibility quantifies dynamic viability through four interacting factors: intermediate persistence, sequential continuity, resistance to competing dissipation, and basin stabilization. A toy branching model demonstrates the separation between accessibility and realizability, and a case study on CO hydrogenation over Ni versus TiO₂₋ₓ/Ni catalysts shows how constraint regimes reorganize closure-compatible pathways, shifting selectivity toward C₂₊ hydrocarbons in agreement with experimental observations. The framework provides a scalable, context-aware pre-pruning layer compatible with quantum chemistry, microkinetic modeling, and machine-learning-based reaction prediction, advancing toward efficient and realistic computational chemistry workflows. Closure (psychology) ; Constraint (computer-aided design) ; Work (physics) ; Branching (polymer chemistry) ; Selectivity ; Compatibility (geochemistry) ; Catalysis ; Quantum Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Asymmetric Hydrogenation and Catalysis
Authors: Nikita I. Kolomoets | Daniil A. Boiko | Leonid V. Romashov | Kirill S. Kozlov | Evgeniy G. Gordeev | Alexey S. Galushko | Valentine P. Ananikov
Source: Angewandte Chemie International Edition (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Reaction Discovery Involving Digital co‐Expert with a Practical Application in Atom‐Economic Cycloaddition The discovery of new chemical transformations is central to advancing modern chemistry, yet conventional approaches often require months or years of extensive experimental screening. Here, we present a machine-learning-assisted and expert-guided pipeline for reaction discovery applied to the search for atom-economic cycloaddition reactions. Candidate reactions were generated from publicly available quantum chemical data, filtered through unsupervised machine learning, and clustered to reduce redundancy. A digital co-expert then enabled rapid prioritization, after which human expertise provided final selection and experimental validation. This hybrid workflow is fully compatible with current laboratory infrastructure and addresses the most time-consuming stage of reaction discovery, accelerating the expert screening bottleneck by approximately 180-fold (from > 1200 days to 7 days). Within ∼1 week, two novel cycloaddition reactions were identified and experimentally confirmed, yielding previously undescribed products. While fully autonomous robotic platforms represent a long-term vision, their high cost and limited availability restrict immediate application. In contrast, our approach demonstrates the practicality of human-AI collaboration for reaction discovery, combining computational screening, machine learning and expert knowledge to efficiently expand the accessible chemical space. Workflow ; Bottleneck ; Cycloaddition ; Pipeline (software) ; Selection (genetic algorithm) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Anonymous | Gui Wang | Shaogang Xu | Yiguo Xu | Da Li | Menglin Huang | Lars Samuelson | Xiao Wei Sun
Source: Physical Review Applied (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted density functional theory for predicting transparent conducting materials Density functional theory ; Time-dependent density functional theory ; Functional theory ; Work (physics) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Inorganic Chemistry and Materials
Authors: Rakshit Kumar Singh | Aryan Amit Barsainyan | Bharath Ramsundar
Source: Open MIND (preprint, 2026-02-17)
Field: Materials Science — Materials Chemistry

A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems Density Functional Theory (DFT) is widely used for first-principles simulations in chemistry and materials science, but its computational cost remains a key limitation for large systems. Motivated by recent advances in ML-based exchange-correlation (XC) functionals, this paper introduces a differentiable framework that integrates machine learning models into density functional theory (DFT) for solids and other periodic systems. The framework defines a clean API for neural network models that can act as drop in replacements for conventional exchange-correlation (XC) functionals and enables gradients to flow through the full self-consistent DFT workflow. The framework is implemented in Python using a PyTorch backend, making it fully differentiable and easy to use with standard deep learning tools. We integrate the implementation with the DeepChem library to promote the reuse of established models and to lower the barrier for experimentation. In initial benchmarks against established electronic structure packages (GPAW and PySCF), our models achieve relative errors on the order of 5-10%. Differentiable function ; Python (programming language) ; Artificial neural network ; Density functional theory ; Reuse ; Key (lock) ; Deep learning Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Quantum many-body systems