jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guo‐Jin Cao
Source: International Journal of Quantum Chemistry (article, 2025-03-19)
Field: Materials Science — Materials Chemistry

Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions ABSTRACT Machine learning has revolutionized computational chemistry by improving the accuracy of predicting thermodynamic and kinetic properties like activation energies and Gibbs free energies, accelerating materials discovery and optimizing reaction conditions in both academic and industrial applications. This review investigates the recent strides in applying advanced machine learning techniques, including transfer learning, for accurately predicting both activation energies and Gibbs free energies within complex chemical reactions. It thoroughly provides an extensive overview of the pivotal methods utilized in this domain, including sophisticated neural networks, Gaussian processes, and symbolic regression. Furthermore, the review prominently highlights commonly adopted machine learning frameworks, such as Chemprop, SchNet, and DeepMD, which have consistently demonstrated remarkable accuracy and exceptional efficiency in predicting both thermodynamic and kinetic properties. Moreover, it carefully explores numerous influential studies that have notably reported substantial successes, particularly focusing on predictive performance, diverse datasets, and innovative model architectures that have profoundly contributed to enhancing computational chemistry methodologies. Ultimately, this review clearly underscores the transformative potential of machine learning in significantly improving the predictive power for intricate chemical systems, bearing considerable implications for both cutting‐edge theoretical research and practical applications. Gibbs free energy ; Thermodynamics ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Tao Yang | Jie Liang | Yao Qin | Xiaokun Zhou | Jing He | Yuan Wang | Yuanqin Zhu | Zengxi Wei | Shuanglaing Zhao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Prediction of Ethylene Homopolymerization Reaction Performance: Fi Catalyst Activity, Polyethylene Molecular Weight, and Molecular Weight Distribution Ethylene ; Polyethylene ; Catalysis ; Molar mass distribution ; Distribution (mathematics) ; Materials science ; Chemical engineering ; Polymer chemistry ; Chemistry ; Organic chemistry ; Polymer ; Composite material ; Mathematics ; Engineering Machine Learning in Materials Science
Authors: Chengze Yang
Source: Intelligent Pharmacy (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Review of the latest progress of AI and machine learning methods in the free energy kinetics estimation and synthesis analysis for organic chemistry applications The accurate prediction of free energy, kinetics, and reaction outcomes in organic chemistry has long presented significant computational challenges, which are often limited by the prohibitive cost of high-precision ab initio methods for complex systems.(Chung and Green, 2024; Keith et al., 2021) 1-2 Artificial intelligence and machine learning have transformed computational chemistry through a data-driven approach in property prediction, kinetics, and synthetic design, offering an alternative option for addressing this challenge. This review examines the latest advancements and the potential of artificial intelligence (AI) and machine learning (ML) to overcome these limitations. We highlight groundbreaking progress across several key domains: Dataset-based machine learning and hybrid quantum mechanical/machine learning models that achieve superior accuracy in free energy and kinetics predictions with reduced computational costs. Machine learning models that enable rapid pK a predictions across a wide range of diverse solvents. The integration of thermodynamic principles into a machine learning model for accurate and consistent macro-micro pK a prediction. Graph-convolutional neural networks that demonstrate high accuracy in reaction outcome prediction with interpretable mechanisms. Neural-symbolic frameworks and Monte Carlo Tree Search (MCTS) integrated with deep neural networks that revolutionize retrosynthetic planning, generating expert-quality routes at unprecedented speeds. A machine learning model based on molecular orbital reaction theory for organic reaction outcome prediction with remarkable accuracy and generalizability. Finally, hierarchical neural networks that predict comprehensive reaction conditions interdependently with exceptional speed. These advancements in a data-driven approach enhance precision, efficiency, and scalability, but challenges such as data quality, stereochemical prediction, and explicit mechanistic incorporation persist. The convergence of these AI/ML capabilities is paving the way for fully automated chemical discovery, addressing critical global challenges in medicine, materials, and energy. 3,4 Artificial neural network ; Deep learning ; Range (aeronautics) ; Outcome (game theory) ; Tree (set theory) ; Computational learning theory ; Retrosynthetic analysis ; Energy (signal processing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Burak Gurlek | Shubham Sharma | Paolo Lazzaroni | Ángel Rubio | Mariana Rossi
Source: npj Computational Materials (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Accurate machine learning interatomic potentials for polyacene molecular crystals: application to single molecule host-guest systems Abstract 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 ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Bhupalee Kalita | R.I. Zubatyuk | Dylan M. Anstine | Maike Bergeler | Volker Settels | Conrad Stork | Sebastian Spicher | Olexandr Isayev
Source: Angewandte Chemie International Edition (article, 2025-12-16)
Field: Materials Science — Materials Chemistry

AIMNet2‐NSE: A Transferable Reactive Neural Network Potential for Open‐Shell Chemistry 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. Artificial neural network ; Generalizability theory ; Scaling ; Quantum chemical ; Molecule ; Training set ; Quantum chemistry ; Spin (aerodynamics) ; Chemical reaction Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Radical Photochemical Reactions
Authors: Guangyao Chen | Zhilong Wang | Fengqi You
Source: Journal of Chemical Information and Modeling (article, 2025-09-29)
Field: Materials Science — Materials Chemistry

PSCG-Net: A Multiscale Crystal Graph Neural Network for Accelerated Materials Discovery The discovery of new materials is crucial for progress in energy, electronics, and sustainable technology. Traditional machine learning approaches, including graph neural networks (GNNs), often fall short because they cannot capture long-range interactions in crystalline materials due to fixed cutoff radii. To overcome this limitation, a pair-scalable crystal graph neural network (PSCG-Net) is proposed. This framework incorporates multiscale structural representations inspired by the pair distribution function and uses graphs with various cutoff distances to account for both short-range and long-range atomic interactions. Tested on over 150,000 crystal structures, PSCG-Net outperforms the baseline Crystal Graph Convolutional Neural Network model by achieving a mean absolute error of 0.065 eV in formation energy prediction. The model's effectiveness is further supported by consistent results across six diverse data sets and confirmed by first-principles calculations using hybrid functionals in band gap-type predictions. Additionally, PSCG-Net is demonstrated to be practical for screening high-performance materials in photovoltaics, dielectrics, and superconductors. By accurately capturing hierarchical atomic interactions, this approach accelerates the design and discovery of materials and offers a versatile framework applicable to multiscale challenges in various scientific disciplines. This framework not only enhances predictive accuracy but also paves the way for breakthroughs in materials science research and technological innovation. Graph ; Cutoff ; Convolutional neural network ; Artificial neural network ; Crystal (programming language) ; Computation ; Function (biology) ; Crystal structure prediction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Wanying Zhang | Xiaoyuan Wang | Yibo Ai | Weidong Zhang
Source: Progress in Natural Science Materials International (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Intelligent screening model for uranium alloy corrosion substitute alloys based on machine learning Materials science ; Alloy ; Corrosion ; Metallurgy ; Uranium Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Amer Marwan El-Samman | Stijn De Baerdemacker
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

“Amide − amine + alcohol = carboxylic acid.” chemical reactions as linear algebraic analogies in graph neural networks In deep learning methods, especially in the context of chemistry, there is an increasing urgency to uncover the hidden learning mechanisms often dubbed as "black box." In this work, we show that graph models built on computational chemical data behave similar to natural language processing (NLP) models built on text data. Crucially, we show that atom-embeddings, a.k.a atom-parsed graph neural activation patterns, exhibit arithmetic properties that represent valid reaction formulas. This is very similar to how word-embeddings can be combined to make word analogies, thus preserving the semantic meaning behind the words, as in the famous example "King" - "Man" + "Woman" = "Queen." For instance, we show how the reaction from an alcohol to a carbonyl is represented by a constant vector in the embedding space, implicitly representing "-H<sub>2</sub>." This vector is independent from the particular carbonyl reactant and alcohol product and represents a consistent chemical transformation. Other directions in the embedding space are synonymous with distinct chemical changes (ex. the tautomerization direction). In contrast to natural language processing, we can explain the observed chemical analogies using algebraic manipulations on the local chemical composition that surrounds each atom-embedding. Furthermore, the observations find applications in transfer learning, for instance in the formal structure and prediction of atomistic properties, such as <sup>1</sup>H-NMR and <sup>13</sup>C-NMR. This work is in line with the recent push for interpretable explanations to graph neural network modeling of chemistry and uncovers a latent model of chemistry that is highly structured, consistent, and analogous to chemical syntax. Amine gas treating ; Amide ; Alcohol ; Algebraic number ; Chemistry ; Organic chemistry ; Graph ; Mathematics ; Discrete mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Rohit Prajapati | Jitendra Chaudhari | Parikshit Paredi | Daksh Vyawhare | Nao Tsunoji | Rajib Bandyopadhyay | Krupa Shah | Mahuya Bandyopadhyay
Source: ChemPhysChem (article, 2025-05-09)
Field: Chemistry — Inorganic Chemistry

Structural Investigation and Enriched Catalysis of Cu‐Complex‐Encapsulated Microporous Catalyst with Pragmatic Modeling for Prediction of Activity by Using Machine Learning Silicoaluminophosphates (SAPOs) are structurally diverse materials widely used in separation, catalysis, and environmental applications. In this study, a simple post-synthetic method is used to create a hybrid porous material by immobilizing a copper(II) complex onto base-functionalized SAPO molecular sieves. The copper complex, synthesized using 2,9-dimethyl-1,10-phenanthroline and copper nitrate, is structurally confirmed through single-crystal X-ray diffraction. The effective activity in ring-opening reaction of epoxide is achieved when this complex is anchored on amine-functionalized SAPO materials. Characterization techniques such as powder X-ray diffraction, N<sub>2</sub> adsorption-desorption, Fourier transform infrared spectroscopy, nuclear magnetic resonance, scanning electron microscope, and thermogravimetric analysis confirm the structural integrity, surface properties, and thermal stability of the materials. High conversion efficiencies of 90% and 88% are achieved using copper-complex-immobilized SAPO-34 and SAPO-5, respectively. To enhance industrial applicability, machine learning techniques are applied to predict product conversion and selectivity. Methods such as linear regression, support vector machine (SVM), and k-nearest neighbors (kNN) are evaluated, with SVM and kNN showing strong predictive performance. Error metrics like mean-squared error, mean absolute percentage error, and R score validate the model accuracy. This work highlights the effective integration of functionalized SAPOs with ML tools for catalytic optimization and industrial-scale applications. Catalysis ; Microporous material ; Selectivity ; Support vector machine ; Copper ; Mean absolute percentage error ; Chemistry ; Materials science ; Chemical engineering ; Machine learning ; Computer science ; Organic chemistry ; Artificial neural network Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications ; Chemical Synthesis and Reactions
Authors: Yong-Uk Shin | Gihoon Kwon | Hocheol Song
Source: Journal of Water Process Engineering (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

Advanced deep learning-based hybrid framework for predicting reaction rate constants in oxidation systems with char catalysts synthesized via pyrolysis Catalysis ; Char ; Pyrolysis ; Artificial neural network ; Convolutional neural network ; Process (computing) ; Noble metal ; Deep learning Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Zikai Xiong | Shengjun Huo | Wanglai Cen | Jikun Feng | Jiahao Zeng | Meiling Hou | Yu Liu
Source: Molecular Catalysis (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Accelerating the screening of catalysts for hydrogen evolution reaction in graphene materials based on DFT calculations and machine learning Catalysis ; Graphene ; Hydrogen ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: M T Vasumathi | V Asha | N S Sukanya | B. Kumar | Ayush Raj | Basavaraj Basavaraj
Source:  (article, 2025-04-07)
Field: Materials Science — Biomaterials

Machine Learning-Driven Design of Smart Materials with Adaptive Properties for Advanced Applications Machine Learning (ML) is revolutionizing the design of smart materials by allowing fast discovery and optimization of adaptive materials, supplanting slow, conventional trial-and-error processes. ML methods such as deep learning and reinforcement learning make data-driven predictions of material properties, speeding innovation in shape memory alloys, self-healing polymers, piezoelectric materials, and metamaterials. Case studies highlight how machine learning (ML) is being used to build innovations in the fields of energy harvesting, medicinal implants, aerospace, and AR/VR. There are still challenges with experimental verification, interpretability of models, and the lack of data. These have to be addressed through interdisciplinary collaboration and merging of AI-based methods with physics-based models. Potential to revolutionize fields such as electronics, healthcare, and aerospace into next-generation materials with improved performance and efficiency, the future of machine learning (ML)-based smart materials is highly promising Computer science ; Computer architecture ; Human–computer interaction biodegradable polymer synthesis and properties
Authors: Wenjie Yan | Xing Lai | Yicheng Chen | Wenhao Zhang | Jianming Wu | Xin Xu
Source: Journal of the American Chemical Society (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

General Framework for Geometric Deep Learning on Tensorial Properties of Molecules and Crystals Response properties of molecules and crystals are naturally described by tensors that obey specific equivariance and symmetry constraints. However, directly predicting these tensorial quantities remains challenging for machine learning models. We present a general-purpose output module for equivariant graph neural networks that enables end-to-end prediction of tensors of arbitrary order with prescribed permutation (fundamental) symmetry. Coupled with the <i>SE</i>(3)-equivariant XPaiNN architecture, our framework attains accuracy comparable to that of first-principles calculations. It also supports atomic-level properties─such as chemical shielding tensors and Born effective charges─in an all-in-one model. Moreover, the method handles higher-order tensors, including molecular hyperpolarizability and the elastic tensor (stiffness matrix) of crystalline materials, thereby enabling the derivation and analysis of rich anisotropic information and facilitating AI-assisted discovery and design of functional molecules and materials. Tensor (intrinsic definition) ; Equivariant map ; Anisotropy ; Molecule ; Symmetry (geometry) ; Permutation (music) ; Artificial neural network ; Hyperpolarizability Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Synthesis and Properties of Aromatic Compounds
Authors: Zhouxiang Wang | Hai-Cheng Yi | Zhuhong You | Qiangguo Jin
Source: Engineering Applications of Artificial Intelligence (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Three-dimensional geometric deep learning for reaction prediction with equivariant graph transformer Pairwise comparison ; Deep learning ; Equivariant map ; Transformer ; Embedding ; Graph ; Benchmark (surveying) ; Artificial neural network Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Graph Theory and Algorithms
Authors: Guilherme M. Arantes | Jan Řezáč
Source: Journal of Chemical Theory and Computation (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Benchmark of Approximate Quantum Chemical and Machine Learning Potentials for Biochemical Proton Transfer Reactions Proton transfer reactions are among the most common chemical transformations and are central to enzymatic catalysis and bioenergetic processes. Their mechanisms are often investigated using DFT or approximate quantum chemical methods, whose accuracy directly impacts the reliability of the simulations. Here, a comprehensive set of semiempirical molecular orbital and tight-binding DFT approaches, along with recently developed machine learning (ML) potentials, are benchmarked against high-level MP2 reference data for a curated set of proton transfer reactions representative of biochemical systems. Relative energies, geometries, and dipole moments are evaluated for isolated reactions. Microsolvated reactions are also simulated using a hybrid QM/MM partition. Traditional DFT methods offer high accuracy in general but show markedly larger deviations for proton transfers involving nitrogen-containing groups. Among approximate models, RM1, PM6, PM7, DFTB2-NH, DFTB3, and GFN2-xTB show reasonable accuracy across properties, though their performance varies by chemical group. The ML-corrected (Δ-learning) model PM6-ML improves accuracy for all properties and chemical groups and transfers well to QM/MM simulations. Conversely, standalone ML potentials perform poorly for most reactions. These results provide a basis for evaluating approximate methods and selecting potentials for proton transfer simulations in complex environments. Benchmark (surveying) ; Proton ; Quantum chemical ; Computer science ; Quantum ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics ; Organic chemistry ; Molecule Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Molecular Junctions and Nanostructures
Authors: Pranav Roy | Brandon C. Bukowski
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Interfacial Fluxionality in Ni-Supported Metal Nitride Ammonia Synthesis Catalysts Catalysis ; Metal ; Ammonia production ; Ammonia ; Materials science ; Nitride ; Chemical engineering ; Metallurgy ; Chemistry ; Nanotechnology ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Machine Learning in Materials Science