# chemistry Published articles for chemistry. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026 DevFeed: [IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-11T16:25:47Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [ibm](), [Quantum Computing](), [Supercomputing](), [Cloud](), [Algorithms](), [Optimization](), [Simulation and Design](), [Hardware]() Tags: [2026](), [ai](), [algorithms](), [amd](), [chemistry](), [cloud](), [collaboration](), [core](), [enterprise](), [financial-services](), [hardware](), [hub](), [ibm](), [infrastructure](), [manufacturing](), [optimization](), [processors](), [quantum](), [quantum-computing](), [science](), [simulation](), [supercomputing]() ### AI overview IBM and Lockheed Martin are establishing a quantum innovation hub at ETH Zurich, centered on an IBM Quantum System Two planned for installation at the Swiss National Supercomputing Centre by the end of 2026. The system will use IBM's 120-qubit Nighthawk r2 processor and support research in areas including chemistry, materials science, optimization, and financial services. ### Source excerpt IBM and Lockheed Martin are setting up a quantum innovation hub at ETH Zurich, and its core is Switzerland's first IBM Quantum System Two, to be installed at the Swiss National Supercomputing Centre (CSCS) in Lugano by the end of 2026. The hub comes out of an offset agreement with armasuisse, Switzerland's Federal Office for The post IBM Quantum System Two Heads to Switzerland: 120-Qubit Nighthawk r2 at CSCS by End of 2026 appeared first on StorageReview.com. ## Cleveland Clinic, RIKEN, IBM named Gordon Bell finalists DevFeed: [Cleveland Clinic, RIKEN, IBM named Gordon Bell finalists]() Original publisher: [Read original article]() Published: 2026-09-09T04:00:00Z Content type: news Language: en Sources: [IBM Research]() Topics: [High-Performance Computing](), [ibm](), [Supercomputing](), [Benchmark](), [data]() Tags: [automated](), [chemistry](), [gpu](), [hpc](), [ibm](), [news](), [quantum](), [quantum-community](), [quantum-network](), [quantum-research](), [recognition](), [research](), [supercomputing](), [workflows]() ### AI overview Cleveland Clinic, RIKEN, and IBM were named finalists for the 2026 ACM Gordon Bell Prize for quantum-HPC chemistry research. The collaboration simulated a 12,635-atom protein system and reported an automated workflow that reduces coordination and data movement across quantum and classical computing resources. ### Source excerpt Finalist recognition for one of supercomputing's top prizes arrives as researchers report new progress in automated quantum-HPC chemistry workflows. ## AI helps design new materials that work in the real world DevFeed: [AI helps design new materials that work in the real world]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-26T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Framework](), [Simulation and Design](), [Crystal]() Tags: [ai](), [artificial-intelligence](), [bowen-yu](), [chemical-engineering](), [chemistry](), [computer-chips](), [computer-science-and-technology](), [crystal](), [crysvcd](), [data-centers](), [department-of-energy-doe](), [diffusion-models](), [dmse](), [hao-tang](), [heather-kulik](), [ju-li](), [machine-learning](), [materials-design](), [materials-discovery](), [materials-science-and-engineering](), [mingda-li](), [mouyang-cheng](), [national-science-foundation-nsf](), [nuclear-science-and-engineering](), [paper](), [physics](), [research](), [school-of-engineering](), [school-of-science](), [semiconductors](), [weiliang-luo](), [weiwei-xie](), [yongqiang-cheng]() ### AI overview MIT researchers developed CrysVCD, a framework that applies chemistry-based valence constraints before material generation to improve the stability of generated designs. In tests, it achieved high lattice-dynamics stability in nearly 70 percent of computational material generations and supported targeting properties such as high thermal conductivity and high dielectric constant. ### Source excerpt The "CrysVCD" tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs. ## Paving the way for greener ammonia production DevFeed: [Paving the way for greener ammonia production]() Original publisher: [Read original article]() Author: David L. Chandler | Department of Materials Science and Engineering Published: 2026-08-20T18:45:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Materials science and engineering](), [acid]() Tags: [agriculture](), [ai-for-materials-science](), [artificial-intelligence](), [bilge-yildiz](), [catalysts](), [chemistry](), [cleaner-fertilizer](), [cleaner-industry](), [computational-materials-science](), [computer-modeling](), [dmse](), [electrochemical-ammonia-production](), [emissions](), [energy](), [fertilizer-production](), [food](), [fossil-fuel](), [green-ammonia](), [greener-fertilizer](), [haber-bosch-process](), [industry](), [machine-learning](), [materials-science-and-engineering](), [mit-dmse](), [nitrogen-dissociation](), [nuclear-science-and-engineering](), [pollution](), [research](), [school-of-engineering](), [school-of-science](), [science](), [sustainability](), [transition-metal-nitrides]() ### AI overview MIT researchers developed an approach to predict promising catalyst materials for electrochemical ammonia production. The method could speed the search for alloys that may help make this lower-emissions process more competitive with the fossil-fuel-dependent Haber-Bosch process. ### Source excerpt New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products. ## Broadening access to Skala creates a faster path to predictive DFT DevFeed: [Broadening access to Skala creates a faster path to predictive DFT]() Original publisher: [Read original article]() Author: Sebastian Ehlert, Stefano Battaglia, Thijs Vogels, Jan Hermann, Jens Wehner, Giulia Luise, Klaas Giesbertz, Chin-Wei Huang, Aaron Kaplan, Kate Milton, Stephanie Marisa Lanius, Derk Kooi, P. Bernát Sza Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [Microsoft Research]() Topics: [Deep neural networks](), [Benchmark](), [Microsoft](), [data](), [Software]() Tags: [benchmark](), [chemistry](), [data](), [deep-learning](), [integration](), [microsoft](), [performance](), [research](), [research-blog](), [software](), [workflows]() ### AI overview Microsoft Research presents Skala 1.1, a deep-learning exchange-correlation functional that improves accuracy for molecular simulations while expanding access through integrations with major electronic-structure software. A living benchmark will track the computational performance of future releases. ### Source excerpt Skala 1.1, the updated deep-learning exchange-correlation functional from Microsoft Research, provides greater accuracy, expanded accessibility across the computational chemistry ecosystem, and a living benchmark to track computational performance. The post Broadening access to Skala creates a faster path to predictive DFT appeared first on Microsoft Research. ## A better way to model the behavior of metal alloys DevFeed: [A better way to model the behavior of metal alloys]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-06-19T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Simulation](), [Machine learning](), [datasets]() Tags: [artificial-intelligence](), [chemical-engineering](), [chemistry](), [computer-modeling](), [computer-science-and-technology](), [daniel-xiao](), [datasets](), [dmse](), [killian-sheriff](), [lewis-r-owen](), [machine-learning](), [materials-science-and-engineering](), [mit-materials-science-and-engineering](), [models](), [phase-diagrams](), [predicting-new-materials](), [predictions](), [research](), [rodrigo-freitas](), [school-of-engineering](), [simulation](), [yifan-cao]() ### AI overview MIT researchers developed a machine-learning approach that uses diverse training datasets to model chemically complex metal alloys and predict their material properties more accurately across different conditions. ### Source excerpt MIT researchers' approach captures subtle atomic patterns, improving predictions of material properties. ## A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry DevFeed: [A near-autonomous AI chemist improves a challenging reaction in medicinal chemistry]() Original publisher: [Read original article]() Published: 2026-06-17T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [data]() Tags: [agentic](), [ai](), [autonomous](), [chemistry](), [data](), [drug-discovery](), [gpt](), [models](), [openai](), [research](), [science](), [workflows]() ### AI overview OpenAI and Molecule.one describe a near-autonomous AI chemist using GPT-5.4 and the Maria laboratory system to improve Chan-Lam coupling. The system generated proposals, designed and ran experiments, analyzed data, and achieved higher measured yields for most tested substrates, with humans providing oversight and validation. ### Source excerpt OpenAI and Molecule.one show how a near-autonomous AI chemist using GPT-5.4 improved a key drug-making reaction, advancing medicinal chemistry research. ## Introducing new capabilities to GPT-Rosalind DevFeed: [Introducing new capabilities to GPT-Rosalind]() Original publisher: [Read original article]() Published: 2026-06-03T13:15:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Benchmark](), [multimodal](), [Optimization](), [coding](), [data]() Tags: [analysis](), [benchmark](), [benchmarks](), [chemistry](), [coding](), [data](), [drug-discovery](), [gpt](), [life](), [model](), [models](), [multimodal](), [optimization](), [product](), [reasoning](), [research](), [validation]() ### AI overview GPT-Rosalind is a specialized model for life sciences research that combines agentic coding and tool use with capabilities in medicinal chemistry, genomics, biological reasoning, and experimental workflows. The article describes its global availability to eligible organizations, performance evaluations, the LifeSciBench benchmark, and MedChemBench's assessment of realistic medicinal chemistry tasks. ### Source excerpt GPT-Rosalind advances life sciences research with enhanced biological reasoning, medicinal chemistry expertise, genomics analysis, and experimental workflow capabilities. ## Introducing GPT-Rosalind for life sciences research DevFeed: [Introducing GPT-Rosalind for life sciences research]() Original publisher: [Read original article]() Published: 2026-04-16T01:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat]() Tags: [ai](), [api](), [chatgpt](), [chemistry](), [codex](), [drug-discovery](), [gpt](), [model](), [openai](), [reasoning](), [research](), [science](), [tool](), [tools](), [workflows]() ### AI overview OpenAI introduces GPT-Rosalind, a frontier reasoning model for life sciences research, including biology, drug discovery, translational medicine, chemistry, protein engineering, and genomics. It is designed to support evidence synthesis, hypothesis generation, experimental planning, and other scientific workflows. ### Source excerpt OpenAI introduces GPT-Rosalind, a frontier reasoning model built to accelerate drug discovery, genomics analysis, protein reasoning, and scientific research workflows. ## Gemini 3 Deep Think: Advancing science, research and engineering DevFeed: [Gemini 3 Deep Think: Advancing science, research and engineering]() Original publisher: [Read original article]() Author: The Deep Think team Published: 2026-02-12T16:15:09Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [Google AI](), [Artificial Intelligence](), [Benchmark](), [API](), [Programming](), [Math and Logic]() Tags: [ai](), [api](), [benchmark](), [benchmarks](), [chemistry](), [gemini](), [google](), [math](), [none](), [physics](), [programming](), [reasoning]() ### AI overview Google DeepMind announces an upgraded Gemini 3 Deep Think reasoning mode for challenging science, research, and engineering problems. The article describes its availability in the Gemini app and Gemini API, along with reported results across mathematical, programming, chemistry, physics, and other academic benchmarks. ### Source excerpt Our most specialized reasoning mode is now updated to solve modern science, research and engineering challenges. ## Evaluating AI's ability to perform scientific research tasks DevFeed: [Evaluating AI's ability to perform scientific research tasks]() Original publisher: [Read original article]() Published: 2025-12-16T09:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Benchmark](), [OpenAI](), [math]() Tags: [ai](), [benchmark](), [benchmarks](), [chemistry](), [math](), [model](), [models](), [openai](), [physics](), [reasoning](), [research](), [science]() ### AI overview OpenAI introduces FrontierScience, a benchmark designed to evaluate whether AI can perform expert-level scientific reasoning in physics, chemistry, and biology. It includes Olympiad and Research tracks with difficult, original questions created and verified by experts, addressing gaps in existing benchmarks and supporting measurement of AI progress toward scientific research. ### Source excerpt OpenAI introduces FrontierScience, a benchmark testing AI reasoning in physics, chemistry, and biology to measure progress toward real scientific research. ## LeMaterial: an open source initiative to accelerate materials discovery and research DevFeed: [LeMaterial: an open source initiative to accelerate materials discovery and research]() Original publisher: [Read original article]() Author: Alexandre Duval; Lucile Ritchie; Martin Siron; Inel DJAFAR; Etienne du Fayet; Amandine Rossello; Ali Ramlaoui; JB D.; Leandro von Werra; Thomas Wolf Published: 2024-12-10T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Open Source](), [datasets](), [Machine learning](), [AI Research](), [AI, ML & Data Engineering](), [Training AI Models](), [hugging face]() Tags: [artificial-intelligence](), [chemistry](), [community](), [data](), [datasets](), [machine-learning](), [models](), [open-source](), [projects](), [research](), [science](), [training]() ### AI overview LeMaterial is an open-source collaborative initiative led by Entalpic and Hugging Face to accelerate materials research with machine learning. Its initial release provides a harmonized dataset with 6.7 million entries and seven materials properties, combining prominent materials datasets to support materials discovery, chemical-space exploration, and high-throughput research. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science.