jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Brandon Schoener | Yuting Hu | Pasit Wanlapha | Akshay Rengarajan | Ian Moog | Michael Wang | Zhang, Peihong | Jinjun Xiong | Hao Zeng
Source: ArXiv.org (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training data, as many properties are too complicated to calculate with high-throughput first principles techniques. To address this, recent research has created experimental databases from information extracted from scientific literature. However, most existing experimental databases do not provide full atomic coordinate information, which prevents them from supporting advanced ML architectures such as Graph Neural Networks (GNNs). In this work, we propose to bridge this gap through an alignment process between experimental databases and Crystallographic Information Files (CIF) from the Inorganic Crystal Structure Database (ICSD). Our approach enables the creation of a database that can fully leverage state-of-the-art model architectures for material property prediction. It also opens the door to utilizing transfer learning to improve prediction accuracy. To validate our approach, we align NEMAD with the ICSD and compare models trained on the resulting database to those trained on NEMAD originally. We demonstrate significant improvements in both Mean Absolute Error (MAE) and Correct Classification Rate (CCR) in predicting the ordering temperatures and magnetic ground states of magnetic materials, respectively. Workflow ; Bridging (networking) ; Leverage (statistics) ; Artificial neural network ; Graph ; Transfer of learning ; Graph database Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Shu‐Wen Li | Shan Chen | João C. A. Oliveira | Shimin Zhang | Prof. Dr. Lutz Ackermann | Xin Hong
Source: Angewandte Chemie International Edition (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization Optimizing reaction conditions in high-dimensional chemical spaces remains a central challenge in modern synthesis. In this context, we developed and evaluated a staged diversity-constrained machine learning framework that efficiently balances exploration and exploitation during condition optimization. At each stage, a within-batch diversity constraint promotes broad chemical coverage, while the constraint is progressively relaxed to focus on promising subspaces. Systematic evaluation across large-scale palladium-catalyzed C─C and C─N coupling datasets revealed that the number of stages, rather than the exploration portion, was the dominant factor governing optimization efficiency. A comparison with Bayesian optimization (BO) methods shows a dimension-dependent performance trend. Here, the staged diversity-constrained strategy was shown to be more advantageous in higher-dimensional reaction spaces, whereas BO performed better in lower-dimensional settings. Moreover, we developed a user-friendly software tool making the herein developed framework readily accessible for experimental chemists. Our strategy was further applied to challenging ruthenium-catalyzed meta-C─H functionalization involving 11,880 possible conditions, only 44 experiments were required to identify the optimal setup (91% yield). This work provides a validated and practical framework for accelerating high-dimensional reaction condition optimization, bridging data-driven modeling with experimental synthesis. Bayesian optimization ; Bridging (networking) ; Focus (optics) ; Constraint (computer-aided design) ; Bayesian probability ; Software ; Optimization problem ; Reaction conditions Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods ; Advanced Graph Neural Networks
Authors: Brandon Schoener | Yuting Hu | Pasit Wanlapha | Akshay Rengarajan | Ian Moog | Michael Wang | Zhang, Peihong | Jinjun Xiong | Hao Zeng
Source: arXiv (Cornell University) (preprint, 2026-01-31)
Field: Materials Science — Materials Chemistry

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training data, as many properties are too complicated to calculate with high-throughput first principles techniques. To address this, recent research has created experimental databases from information extracted from scientific literature. However, most existing experimental databases do not provide full atomic coordinate information, which prevents them from supporting advanced ML architectures such as Graph Neural Networks (GNNs). In this work, we propose to bridge this gap through an alignment process between experimental databases and Crystallographic Information Files (CIF) from the Inorganic Crystal Structure Database (ICSD). Our approach enables the creation of a database that can fully leverage state-of-the-art model architectures for material property prediction. It also opens the door to utilizing transfer learning to improve prediction accuracy. To validate our approach, we align NEMAD with the ICSD and compare models trained on the resulting database to those trained on NEMAD originally. We demonstrate significant improvements in both Mean Absolute Error (MAE) and Correct Classification Rate (CCR) in predicting the ordering temperatures and magnetic ground states of magnetic materials, respectively. Workflow ; Bridging (networking) ; Leverage (statistics) ; Artificial neural network ; Graph ; Transfer of learning ; Graph database Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Ziduo Yang | Wei Zhuo | Huiqiang Xie | Xiaoqing Liu | Lei Shen
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

An Efficient High-Degree, High-Order Equivariant Graph Neural Network for Direct Crystal Structure Optimization Crystal structure optimization is fundamental to materials modeling but remains computationally expensive when performed with density-functional theory (DFT). Machine-learning (ML) approaches offer substantial acceleration, yet existing methods face three key limitations: (i) most models operate solely on atoms and treat lattice vectors implicitly, despite their central role in structural optimization; (ii) they lack efficient mechanisms to capture high-degree angular information and higher-order geometric correlations simultaneously, which are essential for distinguishing subtle structural differences; and (iii) many pipelines are multi-stage or iterative rather than truly end-to-end, making them prone to error accumulation and limiting scalability. Here we present E$^{3}$Relax-H$^{2}$, an end-to-end high-degree, high-order equivariant graph neural network that maps an initial crystal directly to its relaxed structure. The key idea is to promote both atoms and lattice vectors to graph nodes, enabling a unified and symmetry-consistent representation of structural degrees of freedom. Building on this formulation, E$^{3}$Relax-H$^{2}$ introduces two message-passing mechanisms: (i) a high-degree, high-order message-passing module that efficiently captures high-degree angular representations and high-order many-body correlations; and (ii) a lattice-atom message-passing module that explicitly models the bidirectional coupling between lattice deformation and atomic displacement. In addition, we propose a differentiable periodicity-aware Cartesian displacement loss tailored for one-shot structure prediction under periodic boundary conditions. Lattice (music) ; Equivariant map ; Differentiable function ; Topology (electrical circuits) ; Cartesian coordinate system ; Graph ; Data structure ; Artificial neural network Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Brandon Schoener | Yuting Hu | Pasit Wanlapha | Akshay Rengarajan | Ian Moog | Michael Yu Wang | Peihong Zhang | Jinjun Xiong | Hao Zeng
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks Workflow ; Bridging (networking) ; Leverage (statistics) ; Artificial neural network ; Graph ; Transfer of learning ; Graph database Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Srikant Sagireddy | Nikhil Rampal | Stephen Weitzner | Liwen Wan
Source: EES batteries. (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Integrated machine learning-molecular dynamics framework for electrolyte property prediction Electrochemical stability windows determine electrolyte operating limits, yet stability arises from statistical ensembles of local solvation environments rather than isolated structures. We capture this by coupling MD with solvation-aware GNNs. Stability (learning theory) ; Property (philosophy) ; Range (aeronautics) ; Dynamics (music) ; Battery (electricity) ; Work (physics) Machine Learning in Materials Science ; Advanced battery technologies research ; Advanced Battery Technologies Research
Authors: Duotian Chen | Shinya Mine | Guanghui Wang | Ichigaku Takigawa | Akihiko Anzai | Ken-ichi Shimizu | Takashi Toyao
Source: ACS Catalysis (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Machine-Learning-Guided Discovery and Statistical Analysis of Multielemental Reverse Water–Gas Shift Catalysts Machine-learning (ML)-guided experimentation enabled the discovery of a reverse water–gas shift (RWGS) catalyst with low-loading precious group metals (PGMs; Pt, Pd, Ir, Ru, Rh, and Au) that surpasses both a commercial benchmark and the best design from our earlier high-PGM work (Pt(3)/Rb(1)–Ba(1)–Mo(0.6)–Nb(0.2)/TiO2_P25; numbers in parentheses denote wt %). By optimizing PGM and additive compositions as well as support under ≤1 wt % PGM loading and iterating between prediction and experiment over 35 cycles, the workflow identified the best catalyst, Pt(0.8)–Ru(0.1)–Pd(0.1)/Mo(0.8)–Ba(1)–Re(1)–Ho(1)–Na(0.4)/TiO2_STR100N. Comparative post hoc statistical analysis of the 3 wt % dataset from our previous study revealed both common beneficial factors and features specific to the low-PGM regime. Collectively, these findings not only accelerate catalyst discovery but also deepen fundamental understanding of the RWGS reaction and provide chemically interpretable design principles for future catalyst development. Catalysis ; Workflow ; Benchmark (surveying) ; Statistical analysis ; Work (physics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Jaehoon Kim | Minseok Moon | Hyunsung Cho | Hyeon-Deuk Kim | Rokyeon Kim | Gyehyun Park | Seungwu Han | Youngho Kang
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomic-Scale Mechanisms of SiO$_2$ Plasma-Enhanced Chemical Vapor Deposition Revealed by Molecular Dynamics with a Machine-Learning Interatomic Potential Plasma-enhanced chemical vapor deposition (PECVD) of silicon dioxide (SiO$_2$) is widely used for low-temperature fabrication of dielectric thin films, yet its atomic-scale growth mechanisms remain incompletely understood. In this work, we investigate SiO$_2$ PECVD using silane and N$_2$O as source gases via molecular dynamics simulations driven by a machine-learning interatomic potential. By systematically varying the oxidant-to-silane-derived species ratio $r$, we elucidate the evolution of film stoichiometry, density, and hydrogen content. Formation of the Si-O-Si network primarily proceeds via oxidation of surface Si-H groups to form Si-OH species, followed by condensation of neighboring Si-OH groups that produces H$_2$O as the dominant byproduct. At low $r$, H$_2$ formation via reactions between Si-H and Si-OH groups also contributes to the network formation. Increasing oxidant supply promotes the network formation through oxidation of residual Si-H species, suppressing hydrogen incorporation and leading to saturation of the Si/O ratio. Rapid chemisorption of silane-derived species, together with steric hindrance from pre-deposited species, results in localized growth and surface roughness. We further show that high-kinetic-energy plasma species can etch SiO$_2$ films, which potentially limits growth rates and enhances surface roughness under high RF-power conditions. These results provide atomic-scale insight into PECVD growth and guidance for optimizing film composition and quality. Plasma-enhanced chemical vapor deposition ; Chemical vapor deposition ; Molecular dynamics ; Silane ; Hydrogen ; Sticking coefficient ; Thin film ; Chemisorption ; Condensation Copper Interconnects and Reliability ; Semiconductor materials and devices ; Thin-Film Transistor Technologies
Authors: Bidhan Chandra Garain | Max Pinheiro | Matheus de Oliveira Bispo | Mario Barbatti
Source: Chemistry - A European Journal (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms-deep evidential regression (DER) and deep ensembles-on the QM9 and WS22 datasets, with a specific emphasis on the role of post hoc calibration. Raw uncertainties from both methods were systematically miscalibrated: DER produced uncertainty estimates where data noise and model uncertainty were not cleanly separated, while ensembles produced sharper yet underconfident estimates. Applying calibration techniques such as isotonic regression (ISR), standard scaling, and GP-Normal corrected these deficiencies, aligning predicted variances with observed errors. On QM9, calibration enabled DER to filter high-confidence predictions more effectively than ensembles. On WS22, calibrated ensembles not only improved statistical reliability but also delivered substantial computational savings in active learning, reducing redundant ab initio evaluations by more than 20%. These results demonstrate that post hoc calibration is essential to transform uncertainty estimates from descriptive metrics into actionable signals, ensuring both trustworthy predictions and resource-efficient molecular modeling. Calibration ; Reliability (semiconductor) ; Uncertainty quantification ; Noise (video) ; Raw data ; Filter (signal processing) ; Experimental data ; Measurement uncertainty ; Regression Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Silvan Käser | Debasish Koner | Markus Meuwly
Source: Precision Chemistry (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Compact Kernel/Neural Network Representation for Accurate, Fast, and Global Reactive Molecular Potential Energy Surfaces Atomistic simulations are a powerful tool for studying the dynamics of molecules, proteins, and materials on wide time and length scales. Their reliability and predictive power depend directly on the accuracy of the underlying potential energy surface (PES). This work introduces KerNN, a combined kernel- and neural network-based approach to represent molecular PESs. Compared to state-of-the-art neural network PESs, the number of learnable parameters of KerNN is significantly reduced. This speeds up training and evaluation times by several orders of magnitude, while retaining high prediction accuracy. Importantly, using kernels as the features also improves the extrapolation capabilities of KerNN far beyond the coverage provided by the training data, which solves a fundamental problem of neural-network-based PESs. KerNN applied to spectroscopy and reaction dynamics shows excellent performance on observables, including vibrational bands computed from classical and quantum simulations. Extrapolation ; Reliability (semiconductor) ; Representation (politics) ; Artificial neural network ; Molecular dynamics ; Work (physics) ; Energy (signal processing) ; Potential energy Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Srinivas Beduru | Enoch Asimbisa | Ivan M. Novitskiy | Andrei G. Kutateladze
Source: The Journal of Organic Chemistry (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Reaction Products Revised and Mechanisms Revisited with Machine Learning-Augmented Computational NMR DU8ML, a fast and accurate machine learning-augmented DFT method for computing NMR spectra, has proven effective for high-throughput revision of misassigned natural products. In this paper, we continue with another important underexplored challenge: revision of incorrect product structures of reported organic reactions, often necessitating correction of reaction mechanisms. Product (mathematics) ; Yield (engineering) ; Nuclear magnetic resonance spectroscopy ; Work (physics) Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Ilya Balabin | Thomas M. Kaiser
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Inferential Mechanics Part 1: Causal Mechanistic Theories of Machine Learning in Chemical Biology with Implications Machine learning techniques are now routinely encountered in research laboratories across the globe. Impressive progress has been made through ML and AI techniques with regards to large data set processing. This progress has increased the ability of the experimenter to digest data and make novel predictions regarding phenomena of interest. However, machine learning predictors generated from data sets taken from the natural sciences are often treated as black boxes which are used broadly and generally without detailed consideration of the causal structure of the data set of interest. Work has been attempted to bring causality into discussions of machine learning models of natural phenomena; however, a firm and unified theoretical treatment is lacking. This series of three papers explores the union of chemical theory, biological theory, probability theory and causality that will correct current causal flaws of machine learning in the natural sciences. This paper, Part 1 of the series, provides the formal framework of the foundational causal structure of phenomena in chemical biology and is extended to machine learning through the novel concept of focus, defined here as the ability of a machine learning algorithm to narrow down to a hidden underpinning mechanism in large data sets. Initial proof of these principles on a family of Akt inhibitors is also provided. The second paper containing Part 2 will provide a formal exploration of chemical similarity, and Part 3 will present extensive experimental evidence of how hidden causal structures weaken all machine learning in chemical biology. This series serves to establish for chemical biology a new kind of mathematical framework for modeling mechanisms in Nature without the need for the tools of reductionism: inferential mechanics. Causal inference ; Set (abstract data type) ; Causality (physics) ; Algorithmic learning theory ; Mechanism (biology) ; Computational learning theory ; Natural (archaeology) ; Causal model Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Gene Regulatory Network Analysis
Authors: Zhaoxiang Shen | Raúl I. Sosa | Jakub Lengiewicz | Alexandre Tkatchenko | Stéphane P.A. Bordas
Source: Machine Learning Science and Technology (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Machine learning surrogate models of many-body dispersion interactions in polymer melts Abstract Accurate prediction of many-body dispersion (MBD) interactions is essential for understanding the van der Waals forces that govern the behavior of many complex molecular systems. However, the high computational cost of MBD calculations limits their direct application in large-scale simulations. In this work, we introduce a machine learning surrogate model specifically designed to predict MBD forces in polymer melts, a system that demands accurate MBD description and offers structural advantages for machine learning approaches. Our model is based on a trimmed SchNet architecture that selectively retains the most relevant atomic connections and incorporates trainable radial basis functions for geometric encoding. We validate our surrogate model on datasets from polyethylene, polypropylene, and polyvinyl chloride melts, demonstrating high predictive accuracy and robust generalization across diverse polymer systems. In addition, the model captures key physical features, such as the characteristic decay behavior of MBD interactions, providing valuable insights for optimizing cutoff strategies. Characterized by high computational efficiency, our surrogate model enables practical incorporation of MBD effects into large-scale molecular simulations. van der Waals force ; Surrogate model ; Generalization ; Dispersion (optics) ; Key (lock) ; Computational model ; Cutoff ; Polymer Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Ergen Bao | Wenjing Xu | Hui Ma | Yueqi Si | Ijaz Shahid | Yutong Huo | Peitao Liu | Yan Sun | Xing-Qiu Chen
Source: npj Computational Materials (article, 2026-03-30)
Field: Materials Science — Metals and Alloys

Microkinetic modeling of acidic corrosion from first principles and machine-learning molecular dynamics Abstract Acidic corrosion undermines infrastructure and energy systems, motivating quantitative models for corrosion prediction. However, existing models often lack accurate activation energies, neglect potential-dependent coverages, and oversimplify anodic dissolution as a concerted multi-electron event. Accordingly, we present a corrosion-prediction framework integrating first-principles calculations with machine-learning molecular dynamics. Our approach computes free-energy barriers at solid–liquid interfaces, incorporates competitive adsorption to determine potential-dependent surface coverages, and resolves Butler–Volmer kinetics of dissolution and hydrogen evolution. Applied to bcc-Fe(110), our model determines a 0.76 eV rate-determining barrier for anodic dissolution via an adsorbed FeOH intermediate, and identifies hydrogen evolution as Volmer-controlled. Predicted apparent activation energy, exchange current densities, corrosion potential, and corrosion current agree with experiment. Furthermore, the model successfully captures alloying effects, with Mn lowering both anodic and cathodic barriers and accelerating corrosion. Transferable and mechanism-based, our model offers a powerful tool for predicting corrosion across metals and guiding corrosion-resistant alloy design. Corrosion ; Dissolution ; Cathodic protection ; Anode ; Adsorption ; Molecular dynamics ; Kinetics Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: Mojahed Abushawish | G. Baulieu | J. Dudouet | O. Stézowski
Source: The European Physical Journal A (preprint, 2026-02-04)
Field: Materials Science — Materials Chemistry

Neural networks for 3D characterisation of AGATA crystals Position (finance) ; Artificial neural network ; Function (biology) ; Tracking (education) ; Pulse (music) ; SIGNAL (programming language) ; Experimental data Crystallization and Solubility Studies ; Mineral Processing and Grinding