Machine Learning in Chemistry@ML_Chem
Physical structure as an organizing principle for universal Hamiltonian learning #machinelearning #compchem
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Posts with #CompChem
#CompChem
Public posts on X that use #CompChem.
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#quantumcomputing See the @QZeitgeist post about our VULCAN work: "Layers of quantum operations compress circuit depth for chemistry".
Jean-Philip Piquemal @jppiquemThrilled to share our new #compchem preprint, introducing VULCAN: a hybrid quantum-classical framework that overcomes Variational Quantum Eigensolver (VQE) scalability bottlenecks in quantum chemistry. By combining layered ansatz growth, overlap-guided compression, and a GPU-accelerated perturbation correction, our method completely eliminates Hamiltonian gradient measurements. VULCAN can simultaneously reduce qubit counts and circuit depth while achieving chemical accuracy at just 5-20% of the measurement cost of batched ADAPT-VQE, paving a highly scalable pathway for near-term quantum computing for quantum chemistry. Great work by Hugo D. Nogueira and team. @qubit_pharma10190- Thrilled to share our new #compchem preprint, introducing VULCAN: a hybrid quantum-classical framework that overcomes Variational Quantum Eigensolver (VQE) scalability bottlenecks in quantum chemistry. By combining layered ansatz growth, overlap-guided compression, and a GPU-accelerated perturbation correction, our method completely eliminates Hamiltonian gradient measurements. VULCAN can simultaneously reduce qubit counts and circuit depth while achieving chemical accuracy at just 5-20% of the measurement cost of batched ADAPT-VQE, paving a highly scalable pathway for near-term quantum computing for quantum chemistry. Great work by Hugo D. Nogueira and team. @qubit_pharma
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@jppiquem@jppiquem @QZeitgeist Congrats, big step for near-term VQE. Dropping gradient measurements entirely while keeping chemical accuracy... I'll read the preprint this weekend. #compchem0012- Machine Learning in Chemistry@ML_ChemThe Complexity and Fuzziness of Conformational Chemistry: Theory and Applications #machinelearning #compchem003245
Can atomic tables tell us which molecular virtual directions to keep—without a preliminary correlation calculation? Tabulated atomic natural orbitals guide radial selection within prescribed angular layers. New preprint: #CompChem
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Very cool paper in Science by the Baker Lab at UW. Screening of 15M macrocycles is enabled by our AIMNet machine learning potential! #compchem Can you imagine what we can do with a 2nd generation of AIMNet2 💪🏻🦾43020618.9K
Ariane Nunes Alves@anunesalvesI have 3 open PhD positions in my lab @TUBerlin @UniSysCat! Please RT until this message reaches Mars! #compchem #chemjobs Applications from Germany and from abroad are welcome. Requirements: a master degree, experience with modeling and simulation of biological systems.
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David Mobley@davidlmobleyEver wondered if the details of molecular dynamics equilibration matter much? We ran across one system where equilibration protocol reliably makes the difference between ligand unbinding and stability. This changes what we’ll do in the group. #CompChem
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Hrant P. Hratchian @hphratchian.bsky.social@hphratchianWhat do we value as a community? I’ve joined John Herbert, Martin Head-Gordon, Teresa Head-Gordon, Rommie Amaro, Alán Aspuru-Guzik, Roald Hoffmann, Carol Parish, Christina Payne, & Troy Van Voorhis in @JPhysChem Letters to share our view. #compchem |62778- Machine Learning in Chemistry@ML_ChemMachine Learning-Guided Design of High Entropy Oxides for Photo-Electrocatalysis and Sustainable Energy Conversion #machinelearning #compchem019504
Volker Deringer@vl_deringerI'm absolutely delighted to share this #compchem work in @nature! We study the origins of structural transitions in disordered silicon – here shown under very high pressure – with atomistic #MachineLearning models. Read freely here: Thread below ⬇️
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JCIM & JCTC Journals@JCIM_JCTC#HighlightoftheWeek Speak to a Protein: An Interactive Multimodal Co-Scientist for Protein Analysis. #compchem012281.7K- New @ChemRxiv preprint: "Auto3D: Automatic Generation of the Low-energy 3D Structures with ANI Neural Network Potentials" Contnued collaboration with @adrian_roitberg #compchem #MachineLearning
337162 - Machine Learning in Chemistry@ML_ChemFrom Caffeine to Aspirin: Everyday Molecules as Triggers for Genetically Encoded Proximity Systems #machinelearning #compchem001285
- Machine Learning in Chemistry@ML_ChemFrom Stochastic Transport Models to High-Dimensional Bayesian Inference #machinelearning #compchem0210659
- Machine Learning in Chemistry@ML_ChemHybrid Joining Technologies for Composites: Mechanisms, Performance, Challenges, and Perspectives #machinelearning #compchem020246
- Machine Learning in Chemistry@ML_ChemMATCHERS: a ‘chemistry-infused’ representation bridging molecular graphs and strings #machinelearning #compchem028602
- Machine Learning in Chemistry@ML_ChemMesoporous‐Shell Monolayer Plasmonic Architecture Enables Quantitative and Decision‐Guided SERS #machinelearning #compchem013213
- JCIM & JCTC Journals@JCIM_JCTCA Simulation-Free Topological Basis for Building Compact Koopman Models of Protein Folding #JCIM Vol66 Issue18 #compchem015229
- Ariane Nunes Alves@anunesalvesI am happy to share that I will start a new position as a junior group leader @UniSysCat @TUBerlin! I will keep working on #compchem development and application of methods for ligand binding kinetics, and go in the direction of enzyme design / tunnel engineering.636278
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