jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tomoaki Yazaki | Keisuke Arimoto | Junji Yamanaka | Kosuke O. Hara
Source: Figshare (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Computational material screening for electrode materials of BaSi<sub>2</sub> solar cells In this study, we developed a computational material screening workflow for metallic electrodes of BaSi 2 solar cells. Elemental and binary metallic materials in the Materials Project database were screened for four device models with different charge transport layer materials contacting the electrode. The screening criteria included chemical stability, melting point, and work function. In contrast to conventional screening approaches that rely solely on materials descriptors, the present workflow explicitly incorporates device-level performance constraints by using the relationship between the work function and the simulated power conversion efficiency. For melting point evaluation, a linear regression estimation from the cohesive energy and a machine learning model were compared to assess their accuracy, which revealed the higher accuracy of the machine learning model. For work function evaluation, first-principles calculation and another machine learning model were compared, which showed similar accuracies. Considering the computational cost, the machine learning model was used for screening. The threshold of work function screening was determined by device simulations. As a result, promising materials for metallic electrodes were successfully identified. Moreover, the developed screening workflow with high versatility will be applicable to material discovery for other solar cells and semiconductor devices. We present a high-throughput screening workflow integrating device simulations and property computations, enabling rapid identification of promising electrode materials for BaSi<sub>2</sub> and broader photovoltaic technologies. Workflow ; Photovoltaic system ; Electrode ; Work function ; Work (physics) ; Identification (biology) ; Characterization (materials science) ; Function (biology) ; Organic solar cell Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Silicon and Solar Cell Technologies
Authors: Sema Atasever | Fatma Zehra Koçak | Ayhan Abdullah Ceyhan | Ömer Şahi̇n
Source: Neural Computing and Applications (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Predicting the production efficiency of barium metaborate using machine learning Abstract Barium metaborate (BBO) is a crucial additive that enhances the resistance of materials against detrimental effects. BBO has unique antimicrobial, radiation shielding and flame-retardant properties. The β form of barium metaborate is of great significance in producing single crystals, the development of functional glasses, and the advancement of laser technology. Previously, we reported the synthesis of barium metaborate crystals from barium chloride and sodium metaborate precursor solutions using a chemical precipitation method. In this following study, the production of barium metaborate was modelled using machine learning (ML) tools. The optimum procedure conditions were investigated using the response surface optimization (RSM) method. The study tested five different ML methods, and the random forest (RF) method giving the most successful results was used for more detailed analysis. The RF model achieved accuracy scores of 0.91 for yield1 and 0.93 for yield2, with corresponding MSE values of 12.58 and 7.33, and RMSE values of 3.55 and 2.71, respectively. In addition, the significant yield increase at 80 °C was highlighted particularly evident in its effectiveness at enhancing the production efficiency of BBO in industries. When the 49 experimental data results were examined, it was seen that the higher and more stable yields were obtained when NaOH was used as a solvent in comparison to HCl. The experimental results were found to be in close agreement with those obtained using RSM, thereby validating the findings of response surface optimization. Barium ; Response surface methodology ; Barium chloride ; Barium sulfate ; Yield (engineering) ; Electromagnetic shielding ; Barium borate ; Precipitation Machine Learning in Materials Science ; Pigment Synthesis and Properties ; Boron and Carbon Nanomaterials Research
Authors: Mingwei Yang | Yupeng Zheng | Muhammad Shoaib | Wei Wu | Jinshuai Song
Source: Communications in Computational Chemistry (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

VBData: A Valence Bond Property Dataset of Single Bonds for Organic Compounds The properties of valence bonds are fundamental to understand and predict the reactivity of chemical reactions. This article presents a comprehensive dataset derived from quantum chemical calculations performed at a consistent computational level for more than 40,000 organic compounds (containing only C, H, N, and O atoms) and over 400,000 non-cyclic single bonds within them. Key bond properties include resonance energies and structural weights. The valence bond computation was performed using XMVB program at the VBSCF level, based on the equilibrium geometries optimized at the B3LYP/def2-SVP level. This dataset is anticipated to serve as a new standard benchmark for bond properties in organic chemistry and to provide a foundation for developing machine learning models. Valence bond theory ; Single bond ; Valence (chemistry) ; Chemical bond ; Quantum chemical ; Modern valence bond theory ; Generalized valence bond ; Bond ; Bond order Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Martin Schwade | Shaoming Zhang | Frederik Vonhoff | Frederico P. Delgado | David A. Egger
Source: Nature Communications (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction Predicting optoelectronic properties of large-scale atomistic systems under realistic conditions is crucial for rational materials design, yet computationally prohibitive with first-principles simulations. Recent neural network models have shown promise in overcoming these challenges, but typically require large datasets and lack physical interpretability. Physics-inspired approximate models offer greater data efficiency and intuitive understanding, but often sacrifice accuracy and transferability. Here we present HAMSTER, a physics-informed machine learning framework for predicting the quantum-mechanical Hamiltonian of complex chemical systems. Starting from an approximate model encoding essential physical effects, HAMSTER captures the critical influence of dynamic environments on Hamiltonians using only few explicit first-principles calculations. We demonstrate our approach on halide perovskites, achieving accurate prediction of optoelectronic properties across temperature and compositional variations, and scalability to systems containing tens of thousands of atoms. This work highlights the power of physics-informed Hamiltonian learning for accurate and interpretable optoelectronic property prediction in large, complex systems. Scalability ; Hamiltonian (control theory) ; Property (philosophy) ; Physical system ; Artificial neural network ; Deep learning ; Experimental data ; Dynamical systems theory Machine Learning in Materials Science ; Perovskite Materials and Applications ; Quantum many-body systems
Authors: Tomoaki Yazaki | Keisuke Arimoto | Junji Yamanaka | Kosuke O. Hara
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Computational material screening for electrode materials of BaSi<sub>2</sub> solar cells In this study, we developed a computational material screening workflow for metallic electrodes of BaSi 2 solar cells. Elemental and binary metallic materials in the Materials Project database were screened for four device models with different charge transport layer materials contacting the electrode. The screening criteria included chemical stability, melting point, and work function. In contrast to conventional screening approaches that rely solely on materials descriptors, the present workflow explicitly incorporates device-level performance constraints by using the relationship between the work function and the simulated power conversion efficiency. For melting point evaluation, a linear regression estimation from the cohesive energy and a machine learning model were compared to assess their accuracy, which revealed the higher accuracy of the machine learning model. For work function evaluation, first-principles calculation and another machine learning model were compared, which showed similar accuracies. Considering the computational cost, the machine learning model was used for screening. The threshold of work function screening was determined by device simulations. As a result, promising materials for metallic electrodes were successfully identified. Moreover, the developed screening workflow with high versatility will be applicable to material discovery for other solar cells and semiconductor devices. We present a high-throughput screening workflow integrating device simulations and property computations, enabling rapid identification of promising electrode materials for BaSi<sub>2</sub> and broader photovoltaic technologies. Workflow ; Photovoltaic system ; Electrode ; Work function ; Work (physics) ; Identification (biology) ; Characterization (materials science) ; Function (biology) ; Organic solar cell Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Silicon and Solar Cell Technologies
Authors: Dounia Shaaban Kabakibo | Félix Therrien | Yoshua Bengio | Michel Côté | Hongyu Guo | Homin Shin | Alex Hernandez-Garcia
Source: arXiv (Cornell University) (preprint, 2026-03-30)
Field: Materials Science — Materials Chemistry

A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes Accurate prediction of ionic conductivity is critical for the design of high-performance solid-state electrolytes in next-generation batteries. We benchmark molecular dynamics (MD) approaches for computing ionic conductivity in 21 lithium solid electrolytes for which experimental ionic conductivity has been previously reported in the literature. In particular, we compare simulations driven by density functional theory (DFT) and by universal machine-learning interatomic potentials (uMLIPs), namely a MACE foundation model. We find comparable performance between DFT and MACE, despite MACE on one GPU more than 350 times faster than DFT on a 64-CPU node. The framework developed here is designed to enable systematic comparisons with additional uMLIPs and fine-tuned models in future work. Ionic conductivity ; Molecular dynamics ; Conductivity ; Electrolyte ; Density functional theory ; Ionic bonding ; Fast ion conductor Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Luyang Jia | Zhan‐Yun Zhang | Bin‐Bin Xie | Lin Shen | Wei‐Hai Fang
Source: Chemistry - Methods (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

A Two‐Stage Machine Learning Framework With Physically Inspired Descriptors for Efficient Prediction of Hydration Free Energies Hydration free energy is essential to understanding stability and reactivity in aqueous environments. Recently, we reported a machine learning model Mole2Solv to predict experimental hydration free energies of molecules ( J. Phys. Chem. Lett. , 2023, 14, 1877). Physically inspired descriptors such as electrostatic potential distribution around solute molecules were proposed instead of chemistry‐specific input features, thus improving transferability of the prediction model when it meets new compounds. However, most of these descriptors require ab initio calculations on solute molecules in vacuum, which seriously hinders its application on large‐scale molecular solvation modeling and high‐throughput screening. To address this limitation, we propose a two‐stage framework that combines Mole2Solv with DeepMoleNet, a multilevel attention‐based graph neural network developed by Ma and coworkers ( J. Chem. Inf. Model. , 2021, 61, 1066). Using the DeepMoleNet, 13 physically inspired descriptors can be predicted based on chemical structures of solutes and then applied to the subsequent Mole2Solv regression model. Expensive electronic structure calculation is no longer necessary for generating descriptors. In comparison with our previous model, the proposed two‐stage framework keeps the prediction accuracy while significantly reduces the computational cost. Additionally, the transferability of physically inspired descriptors to unseen chemical structures can be well preserved within the two‐stage framework. Solvation ; Transferability ; Artificial neural network ; Stability (learning theory) ; Ab initio ; Molecular descriptor ; Graph ; Cheminformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Gomathy Velmayil
Source: Open MIND (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

. Next-Generation Hybrid Multi-Material Surface Plasmon Resonance Biosensor for Non-Invasive Glucose Detection with Machine Learning Surface plasmon resonance ; Biosensor ; Feature (linguistics) ; Pattern recognition (psychology) Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Plasmonic and Surface Plasmon Research
Authors: Veerupaksh Singla | Brett Savoie
Source: ACS Applied Polymer Materials (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

More Bang for Your Bond: Small-Molecule Kinetics as Predictors of Polymer Stability Thermally stable polymers are essential for many high-performance applications, yet traditional experimental methods for studying stability are material-intensive, while machine learning models remain limited by the scarcity of reliable degradation data. We hypothesized that computational small-molecule kinetics could bridge this gap owing to the local character of degradation initiation and the ease of collecting data for molecular systems. To test this hypothesis, we curated computational kinetic data and developed models trained exclusively on small molecules to predict the relative thermal stability rankings of polymers containing C, H, O, N, F, and Cl. Experimental degradation data from 41 common polymers and ∼7k small molecules were used to benchmark the ability of small-molecule kinetics to predict polymer behavior without additional training data. All of the resulting models show remarkable transferability between the small-molecule results and polymer degradation metrics. We find that, while chemically diverse training data generally improve predictions, the accuracy of the underlying small-molecule kinetics is also crucial for maximizing transferability to polymer predictions. Notably, models trained on higher-quality alkane data with consistent kinetic parameters perform comparably or better than those trained on more diverse but incomplete data sets for certain polymer predictions. This suggests that accurately capturing fundamental degradation mechanisms in simple systems may be more valuable than incorporating diverse but potentially inconsistent chemical data. These results underscore significant opportunities for training accurate stability models that can be used for early-stage material design. Polymer ; Stability (learning theory) ; Kinetics ; Degradation (telecommunications) ; Polymer degradation ; Transferability ; Experimental data Thermal and Kinetic Analysis ; Machine Learning in Materials Science ; Polymer crystallization and properties
Authors: Ubaid Ur Rehman | Kashaf Ul Sahar | Chun-Ming Wang
Source: Sustainable materials and technologies (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Lead-free SnTe thermoelectrics: Materials design, device engineering, and sustainable energy perspectives Thermoelectric effect ; Thermoelectric materials ; Phonon scattering ; Thermal conductivity ; Figure of merit ; Phonon ; Band gap ; Context (archaeology) Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Topological Materials and Phenomena
Authors: Haibin Wen | Zhe Zhao | Fanfu Wang | Tianyi Xu | Hao Zhang | Chao Yang | Ye Wei
Source: arXiv (Cornell University) (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

Logos: An evolvable reasoning engine for rational molecular design The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine learning have accelerated molecular property prediction and candidate generation, existing models tend to excel either in physical fidelity without transparent reasoning, or in flexible reasoning without guarantees of chemical validity. This imbalance limits the reliability of artificial intelligence systems in real scientific design workflows.Here we present Logos, a compact molecular reasoning model that integrates multi-step logical reasoning with strict chemical consistency. Logos is trained using a staged strategy that first exposes the model to explicit reasoning examples linking molecular descriptions to structural decisions, and then progressively aligns these reasoning patterns with molecular representations. In a final training phase, chemical rules and invariants are incorporated directly into the optimization objective, guiding the model toward chemically valid outputs. Across multiple benchmark datasets, Logos achieves strong performance in both structural accuracy and chemical validity, matching or surpassing substantially larger general-purpose language models while operating with a fraction of their parameters. Beyond benchmark evaluation, the model exhibits stable behaviour in molecular optimization tasks involving multiple, potentially conflicting constraints. By explicitly exposing intermediate reasoning steps, Logos enables human inspection and assessment of the design logic underlying each generated structure. These results indicate that jointly optimizing for reasoning structure and physical consistency offers a practical pathway toward reliable and interpretable AI systems for molecular science, supporting closer integration of artificial intelligence into scientific discovery processes. Benchmark (surveying) ; Consistency (knowledge bases) ; Matching (statistics) ; Property (philosophy) ; Model-based reasoning ; Fidelity ; Reasoning system ; Automated reasoning ; Scientific reasoning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Weiran Zhang | Hao Pan | Shubo Wang
Source: ArXiv.org (article, 2026-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning assisted inverse design of nonreciprocal multilayer photonic structures Nonreciprocal structures play an important role in optical physics and applications. Conventional approaches for designing nonreciprocal optical structures rely heavily on extensive numerical simulation and parameter tuning, leading to high computational cost and low efficiency. Here, we apply deep learning to the design of nonreciprocal multilayer photonic structures. Three neural-network models-a forward neural network (FNN), an inverse design network (IDN), and a variational autoencoder (VAE)-are employed to learn the complex mapping between structural/material parameters and nonreciprocal spectral characteristics. We show that the FNN can rapidly and accurately predict the nonreciprocal electromagnetic response of a given structure, while the IDN can directly generate suitable structural parameters for target spectral responses. Both approaches substantially reduce computational cost and design time while improving nonreciprocal performance. Furthermore, the VAE can generate band-limited inverse design under practical performance constraints, facilitating efficient exploration of multiple feasible structures that meet different threshold requirements within specified frequency bands. Our work highlights the potential of deep learning for the advanced design of nonreciprocal optical structures and devices. Autoencoder ; Artificial neural network ; Inverse ; Deep learning ; Photonics ; Inverse problem ; Network planning and design Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Electromagnetic Simulation and Numerical Methods
Authors: Zherui Chen | Jiayu Zhang | Yang Tian | Zhoulin Liu | Sining Dai | Yanghui Li | Cong Chen | Dingyuan Tang | Yajun Deng | Qingxia Liu
Source: arXiv (Cornell University) (preprint, 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