jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew Ball | Felix A. Faber | Cassie Pratley | Thierry Kogej | Dragos Horvath | Alexandre Varnek | Giulia Bergonzini | Christof M. Jäger | Mikhail Kabeshov
Source: ChemRxiv (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Expert-Informed Contrastive Learning of Condition Space for Amide Coupling Reactions The selection of appropriate reaction conditions is paramount in chemical synthesis and can greatly impact the success of experimental campaigns. Machine learning models for reaction outcome prediction have focused primarily on reactant representations, whereas the representation of reaction conditions (the chemical components defining the reaction environment, e.g. activators, bases or solvents) has received comparatively less attention. Here, we present a novel machine learning framework designed to generate chemically relevant representations for sets of conditions. We evaluate these representations using literature Amide Coupling High-Throughput Experimentation (HTE) datasets, testing whether distances in embedding space reflect local yield similarity, physical-property trends, and experimentally useful condition diversity. Our results demonstrate that the learnt representations capture reactivity-relevant trends and support downstream tasks including condition visualization, diversity-aware HTE plate design, and selection of alternative conditions. Embedding ; Representation (politics) ; Yield (engineering) ; Space (punctuation) ; Selection (genetic algorithm) ; Coupling (piping) ; Outcome (game theory) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Vandana Subhash Lakade | Priyanka Manohar Thorat
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Automated spectral interpretation using artificial intelligence teachniques Abstract Spectral interpretation is an essential task in analytical chemistry used to determine molecular structures and chemical compositions. Traditional interpretation methods rely heavily on expert knowledge and manual analysis, which can be time-consuming and error-prone. With the rapid growth of data generated by spectroscopic instruments, artificial intelligence (AI) techniques such as machine learning and deep learning have emerged as powerful tools for automated spectral analysis. AI models can learn patterns from large spectral datasets and automatically predict molecular structures, classify compounds, and detect anomalies. This paper reviews the application of AI techniques in automated spectral interpretation, discusses commonly used algorithms, and highlights future research directions in this domain. Interpretation (philosophy) ; Task (project management) ; Expert system ; Applications of artificial intelligence ; Artificial neural network ; Deep learning Machine Learning in Materials Science ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: K. Okada | Kazushi Okada | Kei-ichi Okazaki | Toshifumi Mori | Kang Kim | Nobuyuki Matubayasi
Source: The Journal of Chemical Physics (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

Deep learning of committor for ion dissociation and interpretable analysis of solvent effects using atom-centered symmetry functions The association and dissociation of ion pairs in water are fundamental to physical chemistry, yet their reaction coordinates are complex, involving not only interionic distance but also solvent-mediated hydration structures. These processes are often represented by free-energy landscapes constructed from collective variables (CVs), such as interionic distance and water-bridging structures; however, it remains uncertain whether such representations reliably capture the transition pathways between the associated and dissociated states. In this study, we employ deep learning to identify reaction coordinates for NaCl ion pair association and dissociation in water, using the committor as a quantitative measure of progress along the transition pathway through the transition state. The solvent environment surrounding the ions is encoded through descriptors based on atom-centered symmetry functions (ACSFs), which serve as input variables for the neural network. In addition, SHapley Additive exPlanations analysis, as an explainable artificial intelligence technique, is applied to identify ACSFs that contribute to the reaction coordinate. A comparative analysis of their correlation with CVs representing water-bridging structures, such as interionic water density and the number of water molecules coordinating both ions, further provides a molecular-level interpretation of the ion association-dissociation mechanism in water. Dissociation (chemistry) ; Ion ; Molecule ; Artificial neural network ; Symmetry (geometry) ; Transition state ; Ion-association ; Solvent effects Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Zherui Chen | Jiayu Zhang | Yang Tian | Zhoulin Liu | Sining Dai | Yanghui Li | Cong Chen | Dingyuan Tang | Yajun Deng | Qingxia Liu
Source: ArXiv.org (article, 2026-04-07)
Field: Materials Science — Materials Chemistry

ORION: Unifying Top-Down and Bottom-Up Chemical Space Sampling for a Universal Organic Force Field Empirical force fields remain the primary tool for large-scale molecular simulation, yet their limited flexibility and transferability often hinder predictive modeling in chemically complex condensed-phase systems. Here we present ORION, a universal machine-learning force field for C, H, O, N, S, and P systems developed within the Neuroevolution Potential (NEP) framework. To enhance transferability across diverse chemical environments, ORION was trained on a chemically rich dataset constructed through an integrated top-down and bottom-up strategy, enabling accurate descriptions of complex organic configurations, reactive intermediates, and weak intermolecular interactions. ORION achieves near-density-functional-theory accuracy while retaining the efficiency required for large-scale molecular dynamics simulations. On the test set, it predicts atomic forces with substantially higher accuracy than ReaxFF while running 215.5 times faster under identical hardware conditions, making simulations on the hundreds-of-nanoseconds timescale readily accessible. The model provides a balanced description of bond breaking and formation, aromatic growth, hydrogen bonding, van der Waals interactions, and π-stacking, demonstrating strong transferability across both reactive and nonreactive systems. These results establish ORION as a practical and general force field for predictive simulations in chemistry and materials science, and provide an effective route toward universal machine-learning force fields with both high accuracy and broad applicability. Force field (fiction) ; Transferability ; ReaxFF ; Molecular dynamics ; van der Waals force ; Intermolecular force ; Field (mathematics) ; Computation Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Susan R. Atlas
Source: arXiv (Cornell University) (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Latent space design of interatomic potentials The advent of neural-network-based deep learning techniques has led to the emergence of increasingly sophisticated numerical interatomic potentials, including graph neural networks and large language-motivated foundation models. Parameterized to reproduce large, precomputed quantum mechanical training datasets for molecules and materials, models can be fine-tuned for greater accuracy on specific problems. Despite notable successes, machine learning (ML) models of potentials still face intrinsic challenges due to the combinatoric complexity of the underlying quantum chemical interactions, the existence of as-yet-undiscovered but potentially relevant bonding motifs absent from training datasets, and the need for post-prediction interpretability analysis. Drawing inspiration from autoencoder methods, we propose a constructive approach to interatomic potential design. In standard autoencoder architectures, a ML model self-organizes numerical training data in an unsupervised manner to discover underlying patterns and construct a compressed representation or latent space embedding model of the data, which is then used for prediction and inference. In the present work, we describe how latent space patterns and associated quantum embeddings can be constructed using first-principles methods based on theorems of density functional theory (DFT) and known, analytic constraints. This enables a parsimonious, physics-based representation of energies and densities, formally coupling the electronic and atomic length scales through the electron density, and linking ground, excited, and charge-transfer states of the interacting atoms. We describe the complete set of latent space components providing the foundation for a recently-proposed ensemble charge-transfer potential, and discuss opportunities for synergy in the design and explainability of contemporary machine-learned interatomic potentials. Autoencoder ; Interpretability ; Isosurface ; Representation (politics) ; Embedding ; Parameterized complexity ; Space (punctuation) ; Constructive ; Quantum Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Connor P. Jackson
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Design of Metal–Ligand Complexes for Photocatalysis The efficiency of photocatalytic reactions depends critically on the ability of a catalyst to absorb photons with energies that match the activation barrier of the desired chemical process. However, identifying metal–ligand complexes that can absorb a specific photon energy (Ea) remains a major challenge due to the vast diversity of possible combinations and the nonlinear relationship between molecular structure and electronic excitation energy. In this work, we present a machine learning framework that performs inverse design of photoactive complexes—accepting a target energy input (Ea) and predicting metal–ligand combinations capable of absorbing photons with that energy to catalyze a specified transformation.The model leverages patterns learned from computational and experimental data to identify how structural and electronic features influence photon absorption. By reversing this relationship, the algorithm can suggest promising complexes tailored to the energy requirements of a given reaction. This approach enables a rational, data-driven route to photocatalyst discovery, reducing reliance on empirical screening and expensive quantum chemical calculations. Beyond identifying suitable complexes for specific activation energies, the framework offers a foundation for designing light-responsive materials across a range of catalytic and energy-conversion processes. It represents a step toward fully automated materials discovery, where machine learning can translate energetic requirements directly into molecular design strategies. Photocatalysis ; Photon ; Energy (signal processing) ; Efficient energy use ; Range (aeronautics) ; Quantum Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Asymmetric Hydrogenation and Catalysis
Authors: Taha Etem
Source: JOM (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Prediction of Perforation Damage in Composite Materials Using Synthetic Data Augmentation Abstract This study presents a predictive system for perforation outcomes in composite materials by integrating two data augmentation strategies: synthetic minority over-sampling technique (SMOTE) and conditional Wasserstein generative adversarial networks (cWGAN). SMOTE is first used to balance the highly imbalanced perforation dataset, and cWGAN is subsequently applied to generate more diverse and realistic synthetic samples. naïve Bayes, k-nearest neighbors (kNN), and support vector machine (SVM) classifiers are trained and evaluated under two augmentation settings (SMOTE-only versus SMOTE + cWGAN). Under SMOTE-only, the cubic SVM achieved 98.88% test accuracy, while cubic kNN reached 98.88% test accuracy. After introducing SMOTE + cWGAN, cubic kNN produced the best generalization with a perfect 100% test accuracy (97.61% validation), whereas both kernel naïve Bayes and cubic SVM achieved 98.21% test accuracy. These numerical comparisons demonstrate that adding cWGAN after SMOTE improves minority-class representation and strengthens model generalization, particularly for kNN. The proposed dual-augmentation pipeline provides practical predictive support for perforation outcomes, complementing nondestructive testing workflows by enabling faster screening and supporting design decisions in safety-critical applications. Support vector machine ; Perforation ; Pipeline (software) ; Representation (politics) ; Generalization ; Kernel (algebra) ; Synthetic data ; Benchmark (surveying) Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Structural Integrity and Reliability Analysis
Authors: Rafiuzzaman Pritom | Md Mahbubul Islam
Source: Journal of Chemical Information and Modeling (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Generative AI-Driven Discovery of Next-Generation Electrolytes for Alkali Metal Batteries Recent advances in artificial intelligence (AI) are revolutionizing materials science by unlocking unprecedented capabilities in designing novel compounds and accurately predicting their properties. Among these, graph-based machine learning (ML) algorithms have garnered significant attention for their ability to capture complex atomic interactions and use them as effective descriptors. In this study, we integrated state-of-the-art generative AI (Gen AI) and ML techniques with quantum mechanical calculations to discover novel next-generation electrolytes for alkali metal batteries. We developed a Generative Adversarial Network (GAN) framework incorporating a graph-based generator and discriminator models to generate novel electrolyte candidates. The GAN model was trained on a subset of approximately 1 million molecules from the GDB-11 database, which enabled the generation of 30,000 unique and chemically valid molecules. Concurrently, a Message Passing Neural Network (MPNN) model was trained for property prediction by utilizing the QM9 dataset. Using the trained MPNN model, we predicted the properties of the newly generated molecules and screened the candidates based on the criteria of negative standard enthalpy of formation and a wide HOMO-LUMO gap. First-principles density functional theory (DFT) calculations were conducted for additional screening and to evaluate key thermodynamic and electrochemical properties, including standard enthalpy of formation, oxidation potential, and reduction potentials. Finally, a set of 26 promising candidates was acquired with outstanding electrochemical characteristics. Our findings demonstrate the potential of AI-driven approaches to discover high-performance, stable, and efficient electrolytes as promising alternatives to conventional organic electrolytes for next-generation energy storage systems. Discriminator ; Electrolyte ; Generative grammar ; Artificial neural network ; Quantum chemical ; Generator (circuit theory) ; Set (abstract data type) ; Alkali metal ; Electrochemistry Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Leon Schlosser | Nils Rendel | Julius Gemen | Frank Glorius | Kjell Jorner
Source: Journal of the American Chemical Society (article, 2026-02-06)
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 the 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 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, 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 control of 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) ; Inverse ; Design space exploration ; Identification (biology) ; Space exploration ; Quantum chemical ; Bridge (graph theory) ; Space (punctuation) Machine Learning in Materials Science ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Amith Adoor Cheenady | Krishna Rajan
Source: International Journal of Applied Ceramic Technology (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

AI‐Driven Mapping of Grain Boundary Characteristics to Fracture Resistance in Battery Electrolytes ABSTRACT Intergranular fracture due to the penetration of metal dendrites is an important failure mode in inorganic solid electrolytes (ISEs). Adequate fracture resistance at ISE grain boundaries (GBs) is thus critical for realizing practical solid‐state batteries (SSBs). Using a machine learning (ML) technique, this work connects attributes of ISE GBs to their fracture resistance and demonstrates that GBs in distinct materials, by virtue of their local structure and composition, can behave in similar ways. Altering interfacial characteristics is thus suggested as a potential method for tuning material properties without changing crystal chemistry. We train a mixture‐of‐experts (MoE) based ML model for estimating work of adhesion ( W ad ), the thermodynamic threshold energy for fracture, at ISE GBs and construct structure maps using latent features extracted from the model. A global picture of GB W ad across ISE chemistries is thus presented and complex relationships between crystallographic attributes of GBs, their local non‐stoichiometry, and W ad are revealed. While this ML‐assisted technique may provide useful insights for processing ISEs with higher fracture‐resistance by tuning GB chemistry, it also highlights the value in analyzing latent variables from ML models for unraveling complex structure‐property relationships. Grain boundary ; Electrolyte ; Fracture (geology) ; Work (physics) ; Intergranular fracture ; Intergranular corrosion ; Crystal structure ; Battery (electricity) ; Mode (computer interface) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Juhyun Lee | Hyojin Ban | HyunIl Seo | HangKen Lee | Fiza Arshad | Dongwook Kim
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Structure-guided machine learning for efficiency prediction of organic photovoltaics using experimentally informed molecular descriptors Machine learning prediction combining structural descriptors and experimental information accurately models ternary organic solar cells (D1 : D2 : A). An XGBoost model achieves an R 2 of 0.849 for data-driven discovery of organic photovoltaics. Organic solar cell ; Photovoltaics ; Pattern recognition (psychology) ; Support vector machine ; Photovoltaic system ; Organic chemicals Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Photovoltaic System Optimization Techniques
Authors: Bertran, Michael Alejandro Hernandez | Davide Tisi | Federico Grasselli | Michele Ceriotti | Elisa Molinari | Deborah Prezzi
Source: Open MIND (preprint, 2026-02-26)
Field: Materials Science — Materials Chemistry

Tracking the Lithiation State of Li$_x$Si from Machine-Learned XPS Binding Energies X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that form during operation and dominate electrochemical cycling. Silicon based anodes represent a paradigmatic example in Li batteries, as (de)lithiation proceeds through the formation of strongly disordered Li$_x$Si phases and crystal-amorphous transformations that are hard to characterize. Here, we introduce a computational framework that combines machine-learning (ML) prediction of core-level binding energies to large-scale atomistic simulations -- Grand Canonical Monte Carlo (GCMC) complemented with molecular dynamics (MD), driven by a ML potential -- for a systematic sampling of lithiation states and local atomic environments. This approach yields stoichiometry maps that match the characteristic experimental trends observed in operando and ex situ XPS measurements, including the distinctive Si $2p$ spectroscopic signatures associated with the crystal-to-amorphous disordering driving early delithiation. X-ray photoelectron spectroscopy ; Monte Carlo method ; Binding energy ; Stoichiometry ; Anode ; Tracking (education) ; Silicon ; Molecular dynamics ; Spectroscopy Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Ryan Trice | Mintyu Yu | Eric Welp | Morgan Applegate | Wesley Reinhart | Law, Stephanie
Source: arXiv (Cornell University) (preprint, 2026-01-19)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Polymorph Selection in Molecular Beam Epitaxy of In2Se3 Indium selenide (In2Se3), a layered chalcogenide with multiple polymorphs, is a promising material for optoelectronic and ferroelectric applications. However, achieving polymorph-pure thin films remains a major challenge due to the complex growth space. In this work, Bayesian Optimization (BO) is successfully leveraged to guide the molecular beam epitaxy (MBE) growth of In2Se3 on Al2O3 substrates. By training a predictive Gaussian Process Regressor with sequential learning, we efficiently explored substrate temperature, indium flux, selenium flux, and cracker temperature, reducing experimental trials required for successful synthesis. A γ-In2Se3 film with 91% phase purity was achieved after fewer than ten trials. Attempts to isolate α-In2Se3 were limited by amorphous film formation at low temperatures, indicating that single-step co-deposition is unsuitable for crystalline α-In2Se3 on Al2O3. Overall, this study validates Bayesian Optimization as a powerful approach for phase-selective growth in complex materials systems. Bayesian optimization ; Molecular beam epitaxy ; Substrate (aquarium) ; Epitaxy ; Indium ; Chalcogenide ; Amorphous solid ; Thin film 2D Materials and Applications ; Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films