jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maryam Astero | Anchen Li | Elena Casiraghi | Juho Rousu
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-14)
Field: Materials Science — Materials Chemistry

Structured Chemical Reaction Modeling with Multitask Graph Neural Networks Abstract Modeling chemical reactions requires connecting fine-grained atom–bond edits with broader semantic categories. Yet, most machine learning approaches model these aspects in isolation: atom mapping, reaction center identification, and reaction classification are treated as separate problems. This separation limits accuracy, interpretability, and generalization. In this work, we argue that multitask learning provides a natural and powerful framework for reaction modeling. By jointly predicting mappings, centers, and classes within a single graph neural network, models can leverage structural dependencies between tasks. We proposed MARCC (Mapping-Assisted Reaction Center and Classification), a multitask architecture that achieves state-of-the-art performance on the USPTO-50K benchmark. MARCC demonstrates that multitask supervision not only improves accuracy across all tasks but also provides a structured representation of reaction mechanisms. Leverage (statistics) ; Graph ; Artificial neural network ; Representation (politics) ; Multi-task learning ; Deep neural networks ; Deep learning Machine Learning in Materials Science ; Advanced Text Analysis Techniques ; Advanced Data Processing Techniques
Authors: Rui Su | Q LI | Pengfei 鹏飞 Guan 管
Source: Acta Physica Sinica (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Construction of neural network potential for uranium-niobium alloy and molecular dynamics of its low-temperature aging behaviors &lt;sec&gt;Uranium-niobium alloys exhibit complex crystal phases and unique mechanical behaviors under various thermodynamic states and external loads. However, due to the lack of accurate interatomic potentials, the atomic-scale phase behaviors and dynamical processes in this important alloy are still unclear. In recent years, the development of machine-learning-based force fields has provided a systematic way to generate accurate interatomic potentials on large and complex first-principle-based datasets. However, this crucial nuclear material has received limited attention from researchers in the field of machine-learning potentials.&lt;/sec&gt;&lt;sec&gt;In this work, based on our previous researches on the neural-network potential training and evaluation framework, which we called NNAP (neural-network atomic potential), a new neural network potential is constructed for the uranium-niobium alloy system. A combination of random structure search and active learning algorithms is utilized to enhance coverage of the chemical and structural space of the alloy system. Testing of the generated potential demonstrates high generalization performance and accuracy. On the testing set, the mean absolute error of the energy and the force are 5.6 meV/atom and 0.095 eV/Å, respectively. Further calculation results of crystal structure parameters, equation of state, and phonon dispersions coincide well with the results from the first-principle or experimental references.&lt;/sec&gt;&lt;sec&gt;The atomic-scale evolution of the spinodal decomposition process in the U-Nb alloys is investigated based on the newly trained potential. It is shown that the atom-swapping hybrid Monte Carlo can be a powerful tool to understand the thermodynamic evolution of the systems. By using the atom-swapping hybrid Monte Carlo method, the decrease of potential energy due to phase segregation is observed within 5000 steps, while no significant energy reduction is found after 3-ns MD simulation. Finally, the stress-strain curves under shear load for different initial states are obtained. It is found that the Nb precipitation generates strengthened phases in the alloy and the deformation behavior of U-Nb alloys is significantly changed, where a disorder shear band emerges in the deformation path of the &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ {\mathrm{\gamma }} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-phase alloys. Our work lays a foundation for understanding the mechanical processes in this important alloy system.&lt;/sec&gt; Niobium ; Alloy ; Uranium ; Materials science ; Molecular dynamics ; Dynamics (music) ; Metallurgy ; Chemistry ; Computational chemistry ; Physics Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Machine Learning in Materials Science
Authors: Daniel Julian | Jesús Pérez-Ríos
Source: ArXiv.org (preprint, 2025-07-02)
Field: Materials Science — Materials Chemistry

Machine learning prediction of a chemical reaction over 8 decades of energy Recent progress in machine learning has sparked increased interest in utilizing this technology to predict the outcomes of chemical reactions. The ultimate aim of such endeavors is to develop a universal model that can predict products for any chemical reaction given reactants and physical conditions. In pursuit of ever more universal chemical predictors, machine learning models for atom-diatom and diatom-diatom reactions have been developed, yet no such models exist for termolecular reactions. Accordingly, we introduce neural networks trained to predict opacity functions of atom recombination reactions. Our models predict the recombination of Sr$^+$ + Cs + Cs $\rightarrow$ SrCs$^+$ + Cs and Sr$^+$ + Cs + Cs $\rightarrow$ Cs$_2$ + Sr$^+$ over multiple orders of magnitude of energy, yielding overall results with a relative error $\lesssim 10\%$. Even far beyond the range of energies seen during training, our models predict the atom recombination reaction rate accurately. As a result, the machine is capable of learning the physics behind the atom recombination reaction dynamics. Artificial neural network ; Range (aeronautics) ; Atom (system on chip) ; Chemical reaction ; Energy (signal processing) ; Recombination ; Chemical energy Machine Learning in Materials Science ; History and advancements in chemistry ; Algal biology and biofuel production
Authors: Vinayak Bhat | Chad Risko
Source: ChemRxiv (preprint, 2025-06-11)
Field: Materials Science — Materials Chemistry

A Machine Learning Potential for the in silico Design of Functional Organic Molecular Crystals The design and discovery of functional molecular materials can be greatly accelerated through in silico approaches. Machine learning (ML) models, in particular, demonstrate significant promise for the rapid analysis and manipulation (and re-analysis) of both molecular and crystal structures, essential steps to hasten materials design and discovery. A critical element of these approaches, however, is that robust ML potentials be developed that allow for accurate predictions of energies and forces acting on atoms within a given crystal. Here, we present an ML potential trained on 15,000 molecular crystal structures and more than 2.5 million crystal geometries. The ML potential can predict the energies and forces on atoms in a crystal structure comprised of π-conjugated organic molecules with a mean average error (MAE) of 0.022 eV/atom for energy and 0.056 eV/Å for force when compared with density functional theory (DFT) calculations. To enable the use of the ML potential for in silico discovery of functional molecular crystals, we develop workflows for analyzing crystal surfaces and morphologies, and manipulating and relaxing crystal structures. Furthermore, we deploy these workflows on an open-access web-based tool, OCELOT XtalTransform, that bridges the gap between sophisticated simulations and user-friendly interfaces. By offering these capabilities, OCELOT XtalTransform aims to democratize access to advanced ML potentials for functional molecular crystals, thereby providing new mechanisms for materials design and discovery to the broader scientific community. In silico ; Computer science ; Biochemical engineering ; Artificial intelligence ; Machine learning ; Chemistry ; Nanotechnology ; Materials science ; Engineering ; Biochemistry Machine Learning in Materials Science
Authors: Li Yan Anthony Choong | Zhong Chen | Man‐Fai Ng
Source: ACS Applied Energy Materials (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

Discovery of Effective Halide Solid Electrolytes for Solid-State Rechargeable Batteries via Machine Learning and DFT Calculations Halide solid electrolytes (SEs) are a strong candidate for next-generation lithium-based solid-state batteries for their potential to possess a balance of key properties including ionic conductivity, mechanical properties, and electrochemical stability window (ESW) and can be synthesized using environmentally friendly processes. However, there is a lack of halides simultaneously fulfilling all the mentioned key properties, and searching for the right candidates via experiments is proven challenging. In this work, we develop a computational approach combining machine learning (ML) and DFT calculations, to discover promising halide SEs that satisfy several bulk properties via multiproperty predictions. Various ML and deep learning (DL) models are compared to predict ionic conductivity, bulk and shear moduli, and ESW. The CatBoost, Light Gradient Boosting (LGBM), and Skorch Neural Network (NN) models are found to yield high prediction accuracies for the mentioned properties, with minimum average classification accuracies and average R2 scores exceeding 80% and 0.70, respectively. DFT verifications are performed on Rb2LiBiCl6, LiHF2, and Rb2LiAlF6, with the results suggesting Rb2LiAlF6 as a promising candidate for high voltage battery applications. Overall, we demonstrate that the current ML + DFT approach is useful in screening potential halide solid-state electrolytes that can satisfy several key SE properties. Halide ; Electrolyte ; Boosting (machine learning) ; Ionic bonding ; Key (lock) ; Artificial neural network ; Electrochemistry ; Stability (learning theory) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Virginio Torlao | Estefania Fajardo
Source: Materials Research Express (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Formation energy prediction of material crystal structures using deep learning Abstract Determining the stability of chemical compounds is essential for advancing material discovery. In this study, we introduce a novel deep neural network model designed to predict a crystal’s formation energy, which identifies its stability property. Our model leverages elemental fractions derived from material composition and incorporates the crystallographic symmetry (space group) and stability labels (ground state(stable), metastable, unstable) as an additional input feature. The materials’ symmetry classifications represent the crystal polymorphs and are crucial for understanding phase transitions in materials. The inclusion of symmetry information markedly enhanced the model’s predictive accuracy. At the same time, the addition of stability labels further improved performance, demonstrating that combining compositional, structural, and thermodynamic descriptors yields a more robust prediction framework. These findings underscore the crucial role of crystallographic symmetry and stability information in enhancing the accuracy and interpretability of deep learning models for predicting materials stability. Interpretability ; Stability (learning theory) ; Crystal structure prediction ; Symmetry (geometry) ; Deep learning ; Chemical stability ; Crystal structure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Zhihao Zhang | Xiaoming Cao
Source: ArXiv.org (preprint, 2025-07-28)
Field: Materials Science — Materials Chemistry

Orbital-interaction-aware deep learning model for efficient surface chemistry simulations Deep learning has advanced efficient chemical process simulations on the surfaces, accelerating high-throughput materials screening and rational design in heterogeneous catalysis, energy storage and conversion, and gas separation. However, the accuracy of the deep learning model generally depends on the quality of the training data. Unfortunately, precise experimental data in surface chemistry, such as adsorption energies, are scarce, while accurate quantum chemistry simulations remain computationally prohibitive for large-scale studies. Herein, we present a deep learning model of DOS Transformer for Adsorption (DOTA) for efficient surface chemistry simulations with chemical accuracy. It enables the alignment of experimental data and multi-fidelity quantum chemistry calculation data by capturing latent orbital interaction patterns based on the map between local density of states (LDOS) and adsorption energy. This minimizes the reliance on scarce high-precision training data in surface chemistry to accomplish efficient prediction of adsorption energies rivaling the high-precision experimental data, resolving the long-standing challenge of "CO puzzle". It provides a robust framework for efficient materials screening, effectively bridging the gap between computational and experimental data. Adsorption ; Density functional theory ; Bridging (networking) ; Experimental data ; Training set ; Efficient energy use ; Feature (linguistics) ; Chemical process Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Bhupalee Kalita | R.I. Zubatyuk | Dylan M. Anstine | Maike Bergeler | Volker Settels | Conrad Stork | Sebastian Spicher | Olexandr Isayev
Source: Angewandte Chemie (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry Abstract Open‐shell systems such as radical intermediates are central to radical polymerization (RP), combustion, catalysis, and many other chemical and industrial processes, yet their accurate modeling presents significant computational challenges. Most of the current machine learning interatomic potentials do not distinguish between different spin states, making them unsuitable for open‐shell reactive chemistry. Here we present AIMNet2‐NSE (neural spin‐charge equilibration), a neural network potential that incorporates spin‐charge equilibration for accurate treatment of molecules and reactions with arbitrary charge and spin multiplicities. Built upon the AIMNet2 framework, AIMNet2‐NSE is trained on an extensive dataset comprising 20 million closed‐shell neutral and charged molecules, 13 million open‐shell radical configurations, and 200K radical reaction profiles. With explicit handling of spin charges, AIMNet2‐NSE enables prediction of spin‐resolved properties with near‐DFT accuracy while maintaining a favorable linear scaling compared to the polynomial scaling of electronic structure methods. The predictive capabilities and generalizability of our model are confirmed by evaluations on large‐scale radical test sets, the industrially relevant BASChem19 benchmark, and RP reactions. Overall, AIMNet2‐NSE represents a significant advancement in machine learning interatomic potentials, allowing efficient exploration of complex open‐shell systems, and significantly advancing our ability to model radical reaction pathways and reactive intermediates in chemical processes where traditional quantum mechanical methods are computationally prohibitive. Generalizability theory ; Artificial neural network ; Scaling ; Molecule ; Quantum chemical ; Quantum chemistry ; Reactive intermediate ; Electronic structure ; Spin (aerodynamics) Machine Learning in Materials Science ; Radical Photochemical Reactions ; Advanced Chemical Physics Studies
Authors: Chen Shuan | K. Park | Taewan Kim | S. Han | Yousung Jung
Source: ArXiv.org (preprint, 2025-03-13)
Field: Materials Science — Materials Chemistry

Predicting Chemical Reaction Outcomes Based on Electron Movements Using Machine Learning Accurately predicting chemical reaction outcomes and potential byproducts is a fundamental task of modern chemistry, enabling the efficient design of synthetic pathways and driving progress in chemical science. Reaction mechanism, which tracks electron movements during chemical reactions, is critical for understanding reaction kinetics and identifying unexpected products. Here, we present Reactron, the first electron-based machine learning model for general reaction prediction. Reactron integrates electron movement into its predictions, generating detailed arrow-pushing diagrams that elucidate each mechanistic step leading to product formation. We demonstrate the high predictive performance of Reactron over existing product-only models by a large-scale reaction outcome prediction benchmark, and the adaptability of the model to learn new reactivity upon providing a few examples. Furthermore, it explores combinatorial reaction spaces, uncovering novel reactivities beyond its training data. With robust performance in both in- and out-of-distribution predictions, Reactron embodies human-like reasoning in chemistry and opens new frontiers in reaction discovery and synthesis design. Adaptability ; Task (project management) ; Chemical reaction ; Outcome (game theory) ; Reactivity (psychology) ; Chemical process ; Product (mathematics) ; Training set Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Yang Huang | Shih-Han Wang | Sanjie Cao | Luke E. K. Achenie | Hongliang Xin
Source: ArXiv.org (preprint, 2025-06-03)
Field: Materials Science — Materials Chemistry

Decoding the Stability of Transition-Metal Alloys with Theory-infused Deep Learning We introduce an interpretable deep learning framework that predicts the cohesive energy of transition-metal alloys (TMAs) by embedding cohesion theory within graph neural networks (GNNs). Beyond accurate prediction of cohesive energy, a key indicator of thermodynamic stability, the model offers mechanistic insights by disentangling energy contributions into physically meaningful components. These data-driven interpretations reveal periodic trends and stability principles governing transition metals. We apply the model to single-atom alloys (SAAs) to assess their thermodynamic resilience against two destabilizing processes: agglomeration (adatom clustering) and segregation (migration into the subsurface). Our analysis shows that these phenomena are governed by distinct physical factors-agglomeration is primarily influenced by localized d-orbital coupling, while segregation is dictated by delocalized effects such as wavefunction renormalization. This model thus serves as an explainable AI tool for understanding and guiding the design of stable TMAs, with implications for catalysis and materials discovery. Stability (learning theory) ; Deep learning ; Delocalized electron ; Cohesion (chemistry) ; Embedding ; Artificial neural network ; Energy (signal processing) ; Graph Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Quantum many-body systems
Authors: Burak Gurlek | Sharma, Shubham | Lazzaroni, Paolo | Ángel Rubio | Mariana Rossi
Source: ArXiv.org (preprint, 2025-04-15)
Field: Materials Science — Materials Chemistry

Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems Emerging machine learning interatomic potentials (MLIPs) offer a promising solution for large-scale accurate material simulations, but stringent tests related to the description of vibrational dynamics in molecular crystals remain scarce. Here, we develop a general MLIP by leveraging the graph neural network-based MACE architecture and active-learning strategies to accurately capture vibrational dynamics across a range of polyacene-based molecular crystals, namely naphthalene, anthracene, tetracene and pentacene. Through careful error propagation, we show that these potentials are accurate and enable the study of anharmonic vibrational features, vibrational lifetimes, and vibrational coupling. In particular, we investigate large-scale host-guest systems based on these molecular crystals, showing the capacity of molecular-dynamics-based techniques to explain and quantify vibrational coupling between host and guest nuclear motion. Our results establish a framework for understanding vibrational signatures in large-scale complex molecular systems and thus represent an important step for engineering vibrational interactions in molecular environments. Anharmonicity ; Molecular vibration ; Tetracene ; Molecular dynamics ; Coupling (piping) ; Rotational–vibrational coupling ; Infrared ; Molecule Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Crystallography and molecular interactions
Authors: Bita Yarahmadi | Seyed Majid Hashemianzadeh
Source: Preprints.org (review, 2025-10-14)
Field: Materials Science — Materials Chemistry

Recent Advances in Polymer Design through Machine Learning: A Short Review Polymers are widely used across diverse industries, including medicine, energy storage, construction, aerospace, agriculture, transportation, and electronics. However, the complexity and variability of polymer chemical compositions and structures present significant challenges for their development. The integration of machine learning (ML) algorithms with large datasets has opened new avenues for advancements in polymer science. Polymer informatics focuses on predicting polymer performance and optimizing synthesis conditions using ML models. With the growing availability of comprehensive datasets and ongoing improvements in ML techniques, polymer informatics can now be applied more effectively and systematically. This study provides a concise overview of the application of various ML approaches, including supervised learning, unsupervised learning, and artificial neural networks (ANNs), for predicting and classifying the properties of different polymers. Materials informatics ; Polymer ; Informatics ; Artificial neural network ; Deep learning Machine Learning in Materials Science
Authors: Souad Tayane | Jaafar Gaber
Source: ChemRxiv (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

ChemAI: A Symbolically Informed Neural Network for Physicochemical Modeling of Reaction Systems The modeling of chemical reactions using artificial intelligence is rapidly advancing but still heavily relies on abundant and costly experimental data. In the context of computational chemistry, we present ChemAI, a chemically informed neural network capable of simulating the dynamic evolution of reactions from their symbolic representation alone. This approach is based on the automatic translation of chemical equations into a differential system governed by fundamental physicochemical laws. The core of ChemAI lies in its loss functions, designed to enforce the principles of kinetics (via ordinary differential equations), thermodynamic laws, mass conservation, positivity of concentrations, and adherence to initial conditions. Unlike conventional PINN- or Transformer-based approaches, this formulation ensures the physical consistency of the model while enhancing its chemical interpretability. ChemAI combines symbolic precision with the predictive power of deep learning. It reliably predicts conversions, yields, and reaction profiles, while eliminating the need for experimental supervision. Thus, this AI based computational chemistry paradigm provides symbolic and explainable method for intelligent simulation, process optimization, and advanced reaction design in virtual environments. Artificial neural network ; Computer science ; Cognitive science ; Neural system ; Biological system ; Artificial intelligence ; Psychology ; Neuroscience ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Data Processing Techniques