Multidisciplinary Optimization Strategies

Is optimization better driven by BO surrogate models, or by LLM-driven reasoning? This new preprint offers one of the most careful comparisons I have seen. Bayesian Optimization (BO) and LLM-driven optimization are two very different answers to the same experimental decision-making problem. BO builds a statistical surrogate model of the search space and uses acquisition functions to decide the most informative next step. LLM-driven approaches instead draw on prior knowledge, external tool output, and reasoning to propose new candidates. A new ChemRxiv preprint from Andrew Cooper et al. takes this task seriously and presents a thorough statistical comparison between BO-only, hybrid LLM+BO, and LLM-only optimization strategies, examining both performance and optimizer behavior. The authors benchmark these approaches on two demanding multi-variable optimization problems, with repeated evaluation runs under fixed experimental budgets. Rather than highlighting best-case outcomes, they examine convergence dynamics, variability, outliers, and how different optimizers explore the search space. Key LLM benefits: 🔹LLM-only optimization often outperforms both BO and hybrid LLM+BO approaches, delivering stronger results more consistently across runs. 🔹LLMs integrate prior knowledge and theory more naturally, learning higher-level generalization across variables instead of treating each parameter in isolation. 🔹LLMs show surprising robustness. When given deliberately wrong starting hypotheses, they recovered in 19/20 runs by reasoning from data. Key LLM limitations: 🔹LLM literature searches provided ambiguous net benefit, with helpful papers offset by misleading ones. 🔹LLM advantages are problem-dependent, varying with how well domain knowledge aligns with the task. 🔹LLM-driven optimization shows over-trust in prior literature, fixation on plausible but incorrect hypotheses, and sensitivity to prompt framing. For now, LLM-driven optimization appears to be a strong alternative that practitioners should seriously try. At the same time, its effectiveness is problem- and process-dependent as the authors clearly explained. Have you tried LLM-driven optimization, and how did it compare to BO? 📄 Can We Automate Scientific Reasoning in Closed-Loop Experiments using Large Language Models?, ChemRxiv, January 30, 2026 🔗 https://lnkd.in/e4_X5Fkj

Most labs are sitting on a goldmine of underused reaction data. Few are making the most of it. High-throughput experimentation (HTE) can screen thousands of reaction conditions every month - but in most organizations, that data isn’t feeding back into future optimization efforts. Multitask Bayesian optimization (MTBO) is one way to change that - by learning from past experiments to make future ones smarter. Here’s the idea: Traditional Bayesian optimization treats every new reaction like it’s starting from scratch. But what if we could learn from earlier experiments - even if they involved different substrates or conditions? That’s where MTBO comes in. It draws on prior data to accelerate the optimization of new reactions with similar mechanisms, even when the match isn’t exact. This is particularly relevant when carrying out synthesis with commonly used named reactions, such as Amide couplings and Buchwald-Hartwig couplings. MTBO builds a prior from historical datasets, then learns how the old and new reactions are correlated. That relationship helps guide what to test next - getting to better results, faster. By building systems that learn within reaction classes, we can: → Optimize faster → Run fewer experiments → Get better outcomes with less data This is the future of reaction optimization: not just automation, and not just machine learning - but smart, adaptive platforms that improve over time. At ReactWise, we’re making that future real with multitask Bayesian optimization. If you’ve got years of reaction data sitting in ELNs or spreadsheets, let’s talk.

🚀 Why have I started Gateway.AI? Over the last few years I’ve been in a lot of rooms where teams try to adopt ML/automation in the real world - labs, factories, data-heavy groups. The biggest shift is to start with the workflow, not the model. Day-one questions I ask now: - Map the workflow: What are the true bottlenecks to throughput, cost, or quality? - Fit for AI/automation: Which bottlenecks can tech actually relieve—and which might it worsen? - Watch for negative ROI: Could AI create more dashboards/paperwork without new value? - For experimentalists: If today’s best theory/simulation were free/instant, how would you change experiments on the scale of seconds → weeks? - Benchmarks that matter: How will you measure productivity gains from AI internally? - Downstream value: Who benefits next - can we define benchmarks for downstream impact? - Rewards & objectives: What’s the objective function of the experiment? - For theory/ML folks: What experimental footprint (time/samples/$) is required to falsify the hypothesis? 🔧 Which AI/optimization method should you use? Pick methods by the shape of your problem, not by hype. A quick picker: - Small search, fast feedback, clear objective → start simple: design of experiments (DoE), gradient/coordinate search, rules. - Low–mid dimensional, moderate cost, noisy objective → Bayesian optimization (single/multi-objective; add constraints if needed). - Structured proxies available (cheap early readouts) → multi-fidelity BO or active learning with surrogate models (Gaussian Process, deep kernel learning). - Huge or discrete spaces, many viable recipes, rich constraints → Genetic algorithms / evolutionary strategies (keep operators “manufacturable”). - High-frequency control with a plant model → model-predictive control (MPC). - Sequential decisions under uncertainty, sparse rewards → contextual bandits (short horizon) → RL (only if you truly need it). - Hard planning with known costs/heuristics → tree search (A*, MCTS) beats RL in many cases. Choose with four dials in mind: parameter-space complexity, data dimensionality, proxy availability, and feedback latency (seconds vs hours vs weeks). Your algorithm should match your budget (samples/time), respect constraints, and exploit any physics priors you have. These questions and choices keep projects anchored to outcomes, not demos. It’s why I started Gateway.AI: to translate ML/AE enthusiasm into measurable productivity and downstream value for materials science! If you’re deciding where to start - or whether to- let’s talk! https://lnkd.in/eNeUiADP #AI #Automation #Optimization #ActiveLearning #BayesianOptimization #GeneticAlgorithms #MPC #RL #Bandits #RDM #LabAutomation #MLOps #ExperimentalDesign #GatewayAI

Comprehensive Mine Design: Optimizing Resources with Technical Precision and Sustainability. 🔍 Introduction to Mine Design Mine design is a multidisciplinary process that transforms mineral resources into economic reserves. Combining geological, geotechnical, hydrogeological, environmental, and economic considerations, it ensures optimal extraction while safeguarding environmental and social integrity. 📊 1️⃣ Ore Body Analysis: The Foundation Mine design begins with a 3D block model, capturing tonnage, grade distribution, and geometry. Geological features such as faults, dykes, and rock strength guide decisions on mining methods. Parameters like tonnes-per-vertical-metre are analyzed to estimate productive capacity and mining feasibility. 📐 2️⃣ Mine Planning: Maximizing Resource Value Strategic planning defines cut-off grades, economic depths, and production rates to optimize the deposit’s net present value (NPV). Mining methods (caving, bulk, or selective) are shortlisted based on deposit geometry, host rock properties, and geotechnical constraints. For open-pit designs, pit optimization ensures cost-effective material movement with a focus on strip ratios and operational depth. ⚙️ 3️⃣ Technical Mine Design: Aligning Efficiency with Practicality Mining method selection aligns with depth, geology, and operational scale. Infrastructure includes ore and waste handling, ventilation systems, water management, tailings storage, and electrical networks. Planning incorporates open-pit-to-underground transitions where applicable. 📈 4️⃣ Life-of-Mine Planning and Economic Modeling Development of detailed production schedules and manpower assessments. Advanced software like Whittle, Vulcan, Surpac, Datamine, and Minemax is used for precise resource modeling, scheduling, and cost analysis. 🌍 5️⃣ Environmental and Social Integration: Beyond Compliance Environmental Stewardship: Adherence to international and local standards through effective water use, pollution control, and biodiversity protection. Community Inclusion: Enhancing local livelihoods by creating jobs, providing training, and developing infrastructure like schools, clinics, and housing. Regular engagement with local stakeholders ensures transparency and trust. 📊 6️⃣ Market and Legal Preparedness: Ensuring Project Viability Market analysis aligns production with commodity cycles, reducing exposure to price volatility. Legal agreements address royalties, taxes, and partnerships, ensuring smooth project implementation. 📌 Final Thoughts Effective mine design is a blend of technical precision, economic foresight, and social responsibility. By integrating advanced tools, sustainable practices, and local engagement, modern mines can deliver long-term value while respecting the environment and communities they impact. #MineDesign #Geology #MiningOptimization #Sustainability #ResourceManagement #MineralExploration #EnvironmentalResponsibility #CommunityEngagement

Generative AI Is Revolutionizing the Manufacturing Design 💡 💡 Generative AI optimizes manufacturing design by swiftly generating iterations based on specified parameters, accelerating product development and yielding lightweight, efficient designs that might challenge human engineers. Here's how AI is contributing to design optimization: 👉 Generative Design: ⚪ Exploration of Design Space: Generative design algorithms explore a vast design space by considering numerous variables and constraints. This allows for the generation of design alternatives that human designers might not have considered. ⚪ Optimization of Parameters: AI algorithms optimize design parameters such as material usage, weight distribution, and structural integrity. This leads to the creation of designs that are not only efficient but often innovative in ways that may be challenging for traditional design methods. ⚪ Iterative Processes: AI facilitates rapid iteration by quickly generating and evaluating multiple design options. Designers can then focus on refining the most promising concepts, saving time and resources in the design phase. 👉 Performance Prediction: ⚪ Simulation and Analysis: AI enables advanced simulation and analysis of designs. It predicts how different design configurations will perform under various conditions, considering factors like stress, heat, and fluid dynamics. This ensures that the final design meets performance requirements. ⚪ Real-time Feedback: During the design process, AI provides real-time feedback. Designers can instantly see how modifications impact performance, enabling quick and informed decision-making. 👉 Multidisciplinary Optimization: ⚪ Integration of Multiple Disciplines: AI-driven optimization considers multiple disciplines simultaneously, such as mechanical, thermal, and fluid dynamics. This holistic approach ensures that designs are optimized across various parameters. ⚪ Trade-off Analysis: AI helps in analyzing trade-offs between conflicting design objectives. For instance, a design might need to balance factors like weight, cost, and strength. AI assists in finding the optimal compromise among these conflicting requirements. 👉 Customization and Personalization: ⚪ Tailored Solutions: AI allows for the creation of highly customized designs based on specific user requirements. This is particularly relevant in industries like automotive and aerospace, where components can be optimized for individual preferences or operational conditions. 👉 Design Speed: ⚪ Acceleration of Innovation: AI expedites the design process by automating repetitive tasks and handling complex calculations. This acceleration allows for more time to be spent on creative and innovative aspects of design. #DigitalTranformation #Innovation #Industry4 #Automation #Manufacturing ____________________________________ Follow hashtag #neerajmittra to stay connected on Digital Transformation concepts and its practical execution.

#Composites use is growing in Type 3, 4 and linerless Type 5 #pressurevessels to store compressed/renewable natural gas (CNG/RNG) and #hydrogen as part of the global energy transition. Multidisciplinary, multi-scale #modeling can help optimize designs for storage, weight and cost, but also avoid manufacturing issues. This article explores topics like: - Layer-by-layer #compaction #simulation - Inline measurement of resin uptake during wet winding - Inline #monitoring of fiber angle, overlaps, gaps - Closing the loop between manufacturing and design with "as-built" data Read more: https://lnkd.in/eRTYsPUb