Chemistry & Materials

It’s hard to believe that Periodic was only launched last September : One year later, it is considered one of the pre-eminent AI scientist labs, with dizzying talent density and astonishing progress in the autonomous lab buildout: Most people are familiar with the standard credentials of Liam and Dogus , but we found an incredible “talent slope” while learning more about Periodic, where each successive employee seems

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Predicting the dynamic polarizability tensor of organic molecules is essential for simulating light-matter interactions in optoelectronic devices, yet conventional quantum-chemical methods such as time-dependent density functional theory (TD-DFT) are computationally expensive and limit large-scale screening. We present an equivariant graph neural network architecture that predicts frequency-dependent, complex-valued

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Computational design of polycrystalline microstructures for enhanced mechanical performance requires repeated high-fidelity simulations over stochastic morphologies, rendering conventional crystal plasticity finite element (CPFE) approaches prohibitively expensive for optimization. Here, we develop a deep learning-driven framework for optimizing polycrystalline microstructures under competing quasi-static and dynamic

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AI Models & Platforms Lila Sciences’ AI Lab Uncovers Palladium Catalysts for Green Hydrogen Add Unite.AI to your preferred sources on Google Lila Sciences said on September 25, 2026, that its AI-directed laboratory proposed, synthesized and screened 2,942 catalysts for green hydrogen in three months, identifying six palladium-based material families for the acidic oxygen evolution reaction at the heart of hydrogen pr

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Nature Computational Science, Published online: 22 September 2026; doi:10.1038/s43588-026-01051-4 Embodied intelligence has enabled machines to act autonomously, yet intelligence remains largely confined to digital models, leaving robotic bodies as passive executors. In extreme environments, such as the human brain, where tissues are mechanically and thermally sensitive, conventional electronics can become limiting.

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Abstract Molecular descriptors play a crucial role in representing the structural features of molecules for machine learning-based physical property prediction. However, current descriptors either consider only local aspects of molecular structures or fail to effectively learn nonlocal structural features involving long-distance intramolecular interactions. Here, to address this issue, we present a descriptor named T

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AutoDiscovery, our AI agent for scientific research, analyzes datasets, proposes hypotheses, runs experiments to test them, and ranks the results by Bayesian surprise—the gap between what the model predicted and what the data showed. In a University of Washington classroom this spring, it surfaced a possible flaw in how battery aging is measured, an unexpected pattern in silicon polymers, and a recurring feature in p

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Nature Machine Intelligence, Published online: 10 September 2026; doi:10.1038/s42256-026-01298-6 Shi et al. demonstrate a dual architecture approach for materials research, integrating two lightweight large language models for collaborative reasoning and scientific tool execution. The method achieves competitive performance while remaining affordable and locally deployable.

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Modeling electrochemical interfaces under operational non-equilibrium conditions is vital for energy technologies but remains bottlenecked by the expensive cost of ab initio methods. Current machine learning potentials, largely designed for closed systems under homogeneous electric fields, are limited in open quantum transport applications. To overcome this, we present an E(3)-equivariant graph neural network that em

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The geometric morphology of deposited filaments can significantly influence the structural performance and stability of 3D concrete-printed (3DCP) structures. However, most finite element (FEM)-based approaches for buildability assessment represent printed layers as simplified rectangles, potentially limiting predictive accuracy. This study proposes a geometry-informed modelling framework that integrates the deep-lea

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We propose an unsupervised learning framework for calibrating a physics-augmented neural network (PANN) for small-strain viscoelasticity via full-field data. It only requires quantities that are directly accessible in real experiments for training, namely global reaction forces and surface displacements. The underlying PANN is embedded in the generalized standard materials theory, in which two scalar-valued potential

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Hi everyone, I just quickly wanted to share a paper I was working on for around a year now. I created this summary website with key results: https://flogrammer.github.io/moljepa/ TL;DR: its a multimodal JEPA model for molecules. There will be more work to do to improve performance and I would be happy about feedback and ideas :) submitted by /u/TerribleAntelope9348 [link] [comments]

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Artificial intelligence (AI) increasingly treats scientific literature as a data source for building databases, training predictive models, and guiding discovery. Yet literature-derived datasets often assume that reported experimental values are internally consistent and directly reusable. Here, we analyze this assumption using solid electrolyte (SE) conductivity data as a representative materials-science case. By tr

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Classical constitutive modeling of path-dependent inelastic materials relies on internal state variables whose evolution equations must be postulated based on domain knowledge and calibrated against experimental data. However, in many practical settings, the relevant internal variables are typically not measurable in experiments, and the constitutive response must be inferred entirely from measured strain-stress data

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Polymeric materials are central to modern technologies, with applications ranging from energy to health and transportation. Although AI has made significant advances in materials discovery, the hierarchical structure of polymers across multiple length scales makes them inherently difficult to represent in a unified and physically meaningful way. Here we introduce HiPoly, a polymer-native AI framework that processes c

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Equimolar NaCa(BH$_4$)$_3$ offers a theoretical hydrogen capacity of 11.24 wt.% and a decomposition enthalpy expected to fall between those of NaBH$_4$ and Ca(BH$_4$)$_2$, but it has never been prepared and no crystal structure has been reported. Predictions for it have so far been built within the perovskite family that its heavier homologues adopt. Here that assumption is removed. Six candidate frameworks were asse

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Electron-phonon interactions govern carrier dynamics, transport, and many optical and quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-symmetry, and disordered materials. We introduce MLLocP, a machine learning strategy that learns the sel

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Machine learning interatomic potentials bridge the gap between quantum chemical precision and classical computational speed, enabling molecular dynamics simulations with first-principles accuracy. Their reliability is often improved through active learning, which iteratively expands the training set by identifying uncertain, out-of-distribution configurations. Existing uncertainty-quantification methods often involve

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Powder X-ray diffraction (XRD) is central to materials characterization, yet reliable end-to-end automation remains challenging. An XRD agent must interpret diffraction evidence, operate refinement software, manage coupled parameters in a defensible order, and distinguish numerical improvement from physical validity. In this paper, we propose AutoXRD, an autonomous large language model (LLM) agent framework that orga

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Crystalline materials discovery is essential for energy, electronics, and catalysis, but the vast chemical and structural space makes exhaustive screening infeasible. Existing voxel-based methods are limited by the local receptive fields of three-dimensional convolutional neural networks and the posterior collapse of high-dimensional variational autoencoders. Here, we develop a Fourier Neural Operator (FNO)-based cry

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Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separating the two places a second model in the loop. We introduce the render ceiling, a model-free reference for benchmarks built by rendering known objects: inverting the frozen cameras and re-solving cross-view correspondence recovers exactly the answer the images support. W

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This work provides an overview of the different strategies that can be used to evaluate the performance of AI models and agents based on large language models (LLMs) for materials synthesis. After providing a brief overview of the key technologies behind the current generation of AI agents based on LLMs, we summarize the different approaches to evaluating these models in the context of materials science and in partic

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Ultralarge virtual screenings (ULVSs) evaluate billions of molecules for drug discovery but face cost, flexibility and scalability limits. We introduce AdaptiveFlow, an open-source platform that makes ULVSs more accessible, scalable and efficient and supports artificial intelligence (AI) and machine learning (ML) method development. AdaptiveFlow provides a screening-ready version of the Enamine REAL Space, to our kno

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Functionally graded lattice structures enable lightweight designs with spatially tunable stiffness and density, but their use in multiscale topology optimization is limited by the cost of repeated computational homogenization. This work presents a machine learning-assisted multiscale optimization framework for regular superimposed lattice structures. The unit cell is formed by combining body-centered cubic, face-cent

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Materials prediction depends critically on how scientific knowledge is represented, yet many governing considerations exist only as natural-language heuristics that conventional learners cannot use. We introduce CRISP, a large language model-assisted framework that treats representation construction as a rule-space exploration and compilation problem: it repeatedly samples target-relevant chemical rules without acces

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Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and consumer electronics. Conventional BPHM approaches, including physics-based models and task-centric deep learning methods, face challenges in computational efficiency and parameterization, cross-domain generalization, dependence on extensiv

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Machine learning interatomic potentials (MLIPs) have emerged as a powerful alternative to density functional theory (DFT) for molecular dynamics simulations, offering near-DFT accuracy at a fraction of the computational cost. However, many state-of-the-art MLIPs remain computationally demanding and act as black boxes, limiting physical interpretability. In this work, we evaluate the ultra-fast force field (UF$^3$) po

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Nature Machine Intelligence, Published online: 28 August 2026; doi:10.1038/s42256-026-01283-z Although language models can be helpful in molecular design, they are not typically calibrated for uncertainty. Rankovic and colleagues present a method to train language models while taking into account the uncertainty of the data.

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Synthesis planning for inorganic materials requires predicting both synthesizability and viable routes by linking microscopic thermodynamic stability with macroscopic synthesis methods, precursors, and processing conditions. Here, we develop an interpretable Physics-Informed Retrieval-Augmented Generation Language Model (PIRAG-LM) for end-to-end inorganic crystal synthesis planning. We construct a material-centered S

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Kohn--Sham density functional theory (DFT) underpins electronic-structure simulations, but repeated orbital diagonalizations lead to cubic scaling, restricting quantum calculations to modest scales only. Eliminating these auxiliary orbitals while retaining Kohn--Sham accuracy is the central goal of orbital-free DFT, but both analytical and machine-learning methods have so far fallen short. Prior learning approaches e

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Molecular embedding models can serve as foundational infrastructure for computational chemistry and drug discovery, where reusable vector representations support property prediction, virtual screening, and retrieval. Most molecular encoders are specialist models built around a single molecular view, producing unconditional vectors with no language interface for varying the representation. We ask whether multimodal la

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Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet many existing approaches overlook essential chemical constraints, such as oxidation-state balance, which can lead to chemically invalid structures. Here we introduce 'crystal generator with valence-constrained design' (CrysVCD), a modular framework that integrates chemical rules directly into the generative process

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Next AI Breakthrough Could Come From Physics, Says Max Welling Max Welling, co-founder of CuspAI, discusses how physics principles could unlock the next AI breakthrough, accelerating material discovery and informing AI architectures. 5 min read Visual TL;DR Max Welling proposes physics principles as the next significant leap for AI From the article 8 mentionsThe conventional wisdom in artificial intelligence research

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Polyethylene (PE) is one of the most commonly used synthetic polymers. While the synthesis and processing protocols for PE are well established, precise experimental assignment of microscopic structures at atomistic resolution (i.e., the position of each atom) remains largely limited to highly crystalline systems. This gap is often addressed via computer simulations using empirical interatomic potentials, which use a

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We develop a method for analyzing grain boundary (GB) microstructures that identifies distinct interfacial phases and the dislocation line defects separating them. Similar to bulk materials, GBs can adopt multiple distinct interfacial phases and undergo first-order phase transitions. When these phases coexist, their spatial arrangement and phase junctions constitute a GB microstructure, characterized by variations in

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Developing physical neural network (PNN) hardwares is important to next generation artificial intelligence systems. Phononic devices-using heat current to encode and process information-is one of the solutions to neuromorphic computing. In this work, we back map an artificial neural network (ANN) into a PNN simulation model using polymer networked nanoparticles (PNNPs). Our atomistic simulation results demonstrate th

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The rapid advancement of large language models (LLMs) has led to increasing interest in their application to scientific domains such as chemistry. However, existing chemistry benchmarks often provide only a narrow view of model capability, focusing on limited task sets while overlooking robustness to variations in problem formulation and chemical representation. As a result, reported performance may overestimate a mo

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Molecular Hessians have a key role in describing molecular vibrations, trajectories and optimization paths. An explicit calculation of them through standard quantum mechanical methods can be computationally expensive for medium-large systems, and in many applications not even needed. Machine learning methods can be a shortcut to tackle efficiently the computational difficulties. This paper will present a ML model abl

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The coordination of multi-scale tasks is an effective strategy for computational materials discovery, yet the repeated application of diverse algorithms and tools renders it challenging. We report MAESTRO, a large language model (LLM) agent system capable of executing the entire screening pipeline for metal-organic frameworks (MOFs). It processes a large body of MOF literature, links relevant publications to their cr

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Early thinking in the field of high entropy oxides (HEOs) emphasized their likely abundance, with combinatorial arguments hinting at a myriad of new materials. The experimental reality has proven more challenging: the stability of HEOs cannot be straightforwardly predicted based on ionic radii, lattice geometry, and charge-balancing considerations alone. In this work, we employ machine learning interatomic potentials

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A new artificial intelligence model designed to improve one of quantum chemistry’s most difficult calculations is now available through the CP2K software ecosystem, potentially allowing researchers to simulate larger molecular systems without sacrificing as much accuracy. Developed by Microsoft Research AI for Science, the model, known as Skala, replaces part of the conventional mathematical machinery used in density

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Microsoft Research's Skala 1.1 demonstrates the continuous improvement of their deep-learning DFT approach. Trained on 2.5× more data than its predecessor, it delivers substantially higher accuracy across key molecular simulation challenges, including thermochemistry, reaction kinetics, and molecular structure prediction. Skala is now available in CP2K and is being integrated into Psi4, FHI-aims, ORCA, and VASP, brin

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Monroe is a new molecular foundation model (MFM) pretrained on over 81 million molecules from the PM6 quantum chemistry dataset. It incorporates innovations including an improved graph representation of stereochemistry, improved training losses (conformer denoising and embedding decorrelation), improved multi-task learning, and uses a prior-data-fitted network (TabPFN) for downstream in-context prediction. Across est

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Machine-learned models are replacing first-principles calculations across materials discovery, and physical symmetry is the central guarantee built into them. The debate over how much symmetry to hard-wire rather than learn has run on rotations, where a symmetry error is an approximation error. Some constraints are exact: symmetry forces certain property tensors to exactly zero, so a nonzero prediction is physically

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Rare-earth transition-metal borides offer critical structural motifs for permanent-magnet design; however, the manganese-rich regions within these compositional phase spaces remain largely unexplored. In this work, we develop an advanced machine-learning-assisted discovery framework to explore Y-Mn-B ternary system. Starting from over one million hypothetical structures generated from known structures in databases, w

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Bulk materials, as opposed to nanomaterials, require molecular dynamics (MD) simulations on a large spatial scale (~10^9 atoms or more) to adequately capture their atomic-scale physical properties. Previously, the introduction of machine-learning interatomic potentials (MLIPs) has extended MD to this scale, but even single-component bulk systems require tens of thousands of GPUs on high-end supercomputers. However, m

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Automated structure elucidation from routine spectroscopy remains a major challenge. Researchers introduce MACROS, a multi-agent system that emulates expert iterative hypothesis testing. Trained on 100 million simulated and 1.6 million experimental spectra-molecule pairs, it natively supports arbitrary combinations of routine spectroscopic techniques and achieves unprecedented zero-shot generalization to diverse real

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AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environmental costs. While reporting Floating Point Operations (FLOPs) is a traditional approach for assessing computational costs, the relationship between FLOPs and execution time is not straightforward, as layers with the same number of FLOPs may not have the same execution time because

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Objective: Erythropoietic protoporphyria (EPP) is a rare photodermatosis marked by multi-year diagnostic delays. We developed and externally validated machine learning models to identify patients with EPP earlier from longitudinal electronic health record (EHR) data and estimate undiagnosed disease burden. Materials and Methods: In a retrospective case-control study at two San Francisco health systems, an academic re

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Generative artificial intelligence is accelerating molecular design, yet the relative suitability of available models for different targets and stages of preclinical drug discovery remains unclear. Here we benchmarked 12 molecular generation and optimization methods across 176 curated protein ligand systems spanning diverse therapeutic target classes, with experimentally validated ligands providing reference chemical

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Scientific discovery often involves optimizing expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and computer programs. Generative models like large language models (LLMs) provide expressive priors, but their likelihoods and self-assessments are unreliable for objectives and calibrated epistemic uncertainty, especially for novel candidates.

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Codon optimization uses synonymous sequence changes to improve the expression and therapeutic performance of nucleic acid-based medicines. Masked language models (MLMs) have recently been proposed as alternatives to traditional, frequency-based codon optimization approaches, yet whether they offer a meaningful advantage over such simpler methods remains unclear. Here we benchmark three prominent MLMs --- CaLM, EnCodo

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WorldRover is a data engine for generating richly annotated, long-range explorations of artist-built environments. At its core, WorldRover-Engine is an Unreal Engine pipeline that executes and offline-renders minute-scale routes while preserving their full trajectories and scene geometry. The same exploration can be replayed from first-person, third-person, and 360 panoramic cameras under different environmental stat

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Drug discovery (DD) is a complex, time-consuming, and resource-intensive process that involves the identification of therapeutic targets, selection of bioactive compounds, and extensive experimental validation. The discovery of promising therapeutic compounds from large libraries of phytochemicals and synthetic molecules remains a major challenge in modern drug development. Screening millions of compounds through con

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UNIVERSITY PARK, Pa. — Artificial intelligence could dramatically accelerate the search for technologies that transform carbon dioxide into useful fuels, but a new multi-laboratory study shows that even the most advanced model can be undermined by something as simple as inconsistent stirring. Researchers from four U.S. laboratories have demonstrated that experimental variability can substantially alter measurements o

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Transition states (TSs) govern the rates and outcomes of chemical reactions, making their accurate prediction a central challenge in computational chemistry. Although recent machine learning models achieve near-chemical accuracy in the prediction of TS structures and the associated reaction barriers for small organic reactions, their ability to generalize beyond the training domain remains largely unexplored. Here we

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A study published in Nature Computational Science introduces InfoMetaGen, a universal generative model that provides an inverse design strategy for subwavelength meta-atoms and inhomogeneous metamaterial distributions. The model holds potential to accelerate the design process of metamaterials.

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Artificial intelligence has significantly expanded candidate generation in drug discovery, but selecting optimal compounds under real-world experimental constraints remains a major bottleneck. This review examines quantum annealing as a potential downstream optimization layer within hybrid computational workflows. By focusing on constraint-dominated decision tasks formulated as quadratic unconstrained binary optimiza

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Nature Machine Intelligence, Published online: 03 August 2026; doi:10.1038/s42256-026-01282-0 Generative machine learning methods have led to progress in crystal discovery, but cannot fully explore the space of material candidates that are both novel and useful. A reinforcement learning-based method steers candidate generation to these areas, enabling the design of novel functional materials.

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Abstract Elucidating reaction mechanisms requires efficient generation of transition states (TSs) and products. Existing diffusion and sequence-based models accelerate parts of this process over traditional string-based methods, but typically still require manual enumeration of either TSs or products, and stochastic diffusion dynamics can be inefficient and hard to control. We introduce MolGEN, a conditional flow-mat

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On 31 March, the Alan Turing Institute hosted the AI for Science conference at the Royal Society, exploring how AI is changing the nature of scientific discovery. Recordings of the talks are now available on YouTube. The lineup includes Anna Scaife on foundation models for data-intensive astronomy, Aron Walsh on materials on demand, Scott Hosking on AI for climate and national resilience, Lorenzo Zanisi on putting th

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SemiEpi connects a physics model, in-situ sensing and immediate feedback to control molecular-beam epitaxy.

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A generative system performs inverse design for information metamaterials, linking learned representations to physical objects.

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Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evaluation has been limited to computational benchmarks that may not reflect real-world performance. Here we introduce UniFFBench, a comprehensive evaluation framework featuring the MinX dataset-a diverse collection of 1,500+ mineral systems span

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In a 25-dimensional, 528-run closed-loop experiment, expert chemists and LLMs were compared directly, producing a useful mixed result for AI-scientist claims.

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Herein, we combine Bayesian Optimization and mechanochemistry to develop a rapid, solvent-free protocol for the Johnson–Corey–Chaykovsky (JCC) cyclopropanation and epoxidation. By prioritizing efficiency and sustainability, Machine Learning quickly identified a new set of reaction conditions for this venerable reaction that diverge from established paradigms (explosive NaH/DMSO system), revealing that these transform

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In this episode, Alex Wiltschko, founder and CEO of Osmo, discusses his goal of giving computers a sense of smell and what it takes to build olfactory intelligence. He explains how graph neural networks and advanced embedding spaces allow AI to capture the multi-dimensional structure of scents, grouping them into perceptual neighborhoods, and creating a machine learning representation that predicts how molecules smel

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NEP89 unifies inorganic and organic systems while targeting simulation speeds orders of magnitude beyond first-principles methods.

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Science Jun 5, 2026 We’re working with world-class synthetic, computational, and analytical chemists to make Claude better at chemistry. In this post, we share our first work as part of this effort, in which Anthropic chemist, David Kamber, examines how Claude performs on a chemist’s most common analytical input, an NMR spectrum. When working with molecules, chemists move between hand-drawn structures on a whiteboard

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Computational X-ray absorption near-edge structure (XANES) is widely used to interpret local coordination environments, oxidation states, and electronic structure, but large computational campaigns are often limited by workflow complexity. We present ChemGraph-XANES, a large language model (LLM)-based agentic framework that combines documentation-grounded parameter retrieval via retrieval-augmented generation (RAG),

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