About the International Year of Quantum Science and Technology https://www.unesco.org/en/quantum-science-technology/about (UNESCO, 2025).
Quantum mechanics 100 years on: an unfinished revolution. Nature 637, 251–252 (2025).
Feynman, R. P. Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982).
Google Scholar
Trabesinger, A. Quantum simulation. Nat. Phys. 8, 263 (2012).
Google Scholar
Pople, J. A. Nobel lecture: quantum chemical models. Rev. Mod. Phys. 71, 1267–1274 (1999).
Google Scholar
MacFarlane, A. G. J., Dowling, J. P. & Milburn, G. J. Quantum technology: the second quantum revolution. Philos. Trans. R. Soc. Lond. Ser. A 361, 1655–1674 (2003).
Google Scholar
Preskill, J. Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018).
Google Scholar
Venkatasubramanian, V. Celebrating the birth centenary of quantum mechanics: a historical perspective. Ind. Eng. Chem. Res. 64, 9443–9456 (2025).
Google Scholar
Schrödinger, E. An undulatory theory of the mechanics of atoms and molecules. Phys. Rev. 28, 1049–1070 (1926).
Google Scholar
Born, M. & Oppenheimer, R. Zur Quantentheorie der Molekeln. Ann. Phys. 389, 457–484 (1927).
Google Scholar
Hohenberg, P. & Kohn, W. Inhomogeneous electron gas. Phys. Rev. 136, B864–B871 (1964).
Google Scholar
Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133–A1138 (1965).
Google Scholar
Jensen, F. Introduction to Computational Chemistry (Wiley, 2017).
Mardirossian, N. & Head-Gordon, M. Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals. Mol. Phys. 115, 2315–2372 (2017).
Google Scholar
Keith, J. A. et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem. Rev. 121, 9816–9872 (2021).
Google Scholar
Jacobs, R. et al. A practical guide to machine learning interatomic potentials – status and future. Curr. Opin. Solid State Mater. Sci. 35, 101214 (2025).
Google Scholar
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
Google Scholar
Yang, X., Wang, Y., Byrne, R., Schneider, G. & Yang, S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 119, 10520–10594 (2019).
Google Scholar
Coley, C. W., Green, W. H. & Jensen, K. F. Machine learning in computer-aided synthesis planning. Acc. Chem. Res. 51, 1281–1289 (2018).
Google Scholar
Yang, K. et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59, 3370–3388 (2019).
Google Scholar
Kirkpatrick, J. et al. Pushing the frontiers of density functionals by solving the fractional electron problem. Science 374, 1385–1389 (2021).
Google Scholar
Hermann, J., Schätzle, Z. & Noé, F. Deep-neural-network solution of the electronic Schrödinger equation. Nat. Chem. 12, 891–897 (2020).
Google Scholar
Pfau, D., Spencer, J. S., Matthews, A. G. D. G. & Foulkes, W. M. C. Ab initio solution of the many-electron Schrödinger equation with deep neural networks. Phys. Rev. Res. 2, 033429 (2020).
Google Scholar
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Google Scholar
Ackley, D. H., Hinton, G. E. & Sejnowski, T. J. A learning algorithm for Boltzmann machines. Cogn. Sci. 9, 147–169 (1985).
Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).
Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Google Scholar
Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).
Google Scholar
Otto, M. & Hörchner, U. in Software Development in Chemistry 4 (ed. Gasteiger, J.) 377–384 (Springer, 1990); https://doi.org/10.1007/978-3-642-75430-2_39.
Curry, B. & Rumelhart, D. E. MSnet: a neural network which classifies mass spectra. Tetrahedron Comput. Methodol. 3, 213–237 (1990).
Google Scholar
Qian, N. & Sejnowski, T. J. Predicting the secondary structure of globular proteins using neural network models. J. Mol. Biol. 202, 865–884 (1988).
Google Scholar
Holley, L. H. & Karplus, M. Protein secondary structure prediction with a neural network. Proc. Natl Acad. Sci. USA 86, 152–156 (1989).
Google Scholar
Kireev, D. B. ChemNet: a novel neural network based method for graph/property mapping. J. Chem. Inf. Comput. Sci. 35, 175–180 (1995).
Google Scholar
Wu, Z. et al. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2021).
Google Scholar
Corso, G., Stark, H., Jegelka, S., Jaakkola, T. & Barzilay, R. Graph neural networks. Nat. Rev. Methods Prim. 4, 17 (2024).
Google Scholar
Blank, T. B., Brown, S. D., Calhoun, A. W. & Doren, D. J. Neural network models of potential energy surfaces. J. Chem. Phys. 103, 4129–4137 (1995).
Google Scholar
Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).
Google Scholar
Deringer, V. L., Caro, M. A. & Csányi, G. Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31, 1902765 (2019).
Google Scholar
Zhang, Y.-W. et al. Roadmap for the development of machine learning-based interatomic potentials. Model. Simul. Mater. Sci. Eng. 33, 023301 (2025).
Google Scholar
Peterson, K. A., Feller, D. & Dixon, D. A. Chemical accuracy in ab initio thermochemistry and spectroscopy: current strategies and future challenges. Theor. Chem. Acc. 131, 1079 (2012).
Google Scholar
Martin, J. M. L. & Santra, G. Empirical double-hybrid density functional theory: a ‘third way’ in between WFT and DFT. Isr. J. Chem. 60, 787–804 (2020).
Google Scholar
Raghavachari, K., Trucks, G. W., Pople, J. A. & Head-Gordon, M. A fifth-order perturbation comparison of electron correlation theories. Chem. Phys. Lett. 157, 479–483 (1989).
Google Scholar
Feller, D., Peterson, K. A. & Grant Hill, J. On the effectiveness of CCSD(T) complete basis set extrapolations for atomization energies. J. Chem. Phys. 135, 044102 (2011).
Google Scholar
Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192–3203 (2017).
Google Scholar
Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1, a data set of 20 million calculated off-equilibrium conformations for organic molecules. Sci. Data 4, 170193 (2017).
Google Scholar
Devereux, C. et al. Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens. J. Chem. Theory Comput. 16, 4192–4202 (2020).
Google Scholar
Bronstein, M. M., Bruna, J., Cohen, T. & Veličković, P. Geometric deep learning: grids, groups, graphs, geodesics and gauges. Preprint at https://arxiv.org/abs/2104.13478 (2021).
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning, Vol. 70, 1263–1272 (PMLR, 2017).
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. SchNet – a deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).
Google Scholar
Gasteiger, J., Groß, J. & Günnemann, S. Directional message passing for molecular graphs. Preprint at https://arxiv.org/abs/2003.03123 (2022).
Gasteiger, J., Becker, F. & Günnemann, S. GemNet: universal directional graph neural networks for molecules. In Advances in Neural Information Processing Systems, Vol. 34, 6790–6802 (Curran Associates, Inc., 2021).
Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R. & Tkatchenko, A. sGDML: constructing accurate and data efficient molecular force fields using machine learning. Comput. Phys. Commun. 240, 38–45 (2019).
Google Scholar
Lubbers, N., Smith, J. S. & Barros, K. Hierarchical modeling of molecular energies using a deep neural network. J. Chem. Phys. 148, 241715 (2018).
Google Scholar
Kondor, R., Son, H. T., Pan, H., Anderson, B. & Trivedi, S. Covariant compositional networks for learning graphs. Preprint at https://arxiv.org/abs/1801.02144 (2018).
Thomas, N. et al. Tensor field networks: rotation- and translation-equivariant neural networks for 3D point clouds. Preprint at https://arxiv.org/abs/1802.08219 (2018).
Geiger, M. & Smidt, T. E3nn: euclidean neural networks. Preprint at https://arxiv.org/abs/2207.09453 (2022).
Haghighatlari, M. et al. NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces. Digit. Discov. 1, 333–343 (2022).
Google Scholar
Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).
Google Scholar
Batatia, I., Kovács, D. P., Simm, G. N. C., Ortner, C. & Csányi, G. MACE: higher order equivariant message passing neural networks for fast and accurate force fields. In Advances in Neural Information Processing Systems, Vol. 35, 11423–11436 (Curran Associates, Inc., 2022).
Schütt, K. T., Unke, O. T. & Gastegger, M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In Proceedings of the 38th International Conference on Machine Learning, Vol. 139, 9377–9388 (PMLR, 2021).
Musaelian, A. et al. Learning local equivariant representations for large-scale atomistic dynamics. Nat. Commun. 14, 579 (2023).
Google Scholar
Kovács, D. P. et al. MACE-OFF: short-range transferable machine learning force fields for organic molecules. J. Am. Chem. Soc. 147, 17598–1761 (2025).
Google Scholar
Fu, X. et al. Learning smooth and expressive interatomic potentials for physical property prediction. In Proceedings of the 42nd International Conference on Machine Learning, Vol. 267, 17875–17893 (PMLR, 2025).
Wood, B. M. et al. UMA: a family of universal models for atoms. Preprint at https://arxiv.org/abs/2506.23971 (2025).
Jacobs, R. A., Jordan, M. I. & Barto, A. G. Task decomposition through competition in a modular connectionist architecture: the what and where vision tasks. Cogn. Sci. 15, 219–250 (1991).
Google Scholar
Goerigk, L. et al. A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions. Phys. Chem. Chem. Phys. 19, 32184–32215 (2017).
Google Scholar
Gould, T. & Dale, S. G. Poisoning density functional theory with benchmark sets of difficult systems. Phys. Chem. Chem. Phys. 24, 6398–6403 (2022).
Google Scholar
Burke, K. Perspective on density functional theory. J. Chem. Phys. 136, 150901 (2012).
Google Scholar
Cohen, A. J., Mori-Sánchez, P. & Yang, W. Challenges for density functional theory. Chem. Rev. 112, 289–320 (2012).
Google Scholar
Wang, T. Y., Neville, S. P. & Schuurman, M. S. Machine learning seams of conical intersection: a characteristic polynomial approach. J. Phys. Chem. Lett. 14, 7780–7786 (2023).
Google Scholar
Smith, J. S. et al. The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules. Sci. Data 7, 134 (2020).
Google Scholar
Yang, Y., Eldred, M. S., Zádor, J. & Najm, H. N. Multifidelity neural network formulations for prediction of reactive molecular potential energy surfaces. J. Chem. Inf. Model. 63, 2281–2295 (2023).
Google Scholar
Zheng, P., Zubatyuk, R., Wu, W., Isayev, O. & Dral, P. O. Artificial intelligence-enhanced quantum chemical method with broad applicability. Nat. Commun. 12, 7022 (2021).
Google Scholar
Chen, Y. & Dral, P. O. AIQM2: organic reaction simulations beyond DFT. Chem. Sci. 16, 15901–15912 (2025).
Google Scholar
Thaler, S., Gabellini, C., Shenoy, N. & Tossou, P. Implicit delta learning of high fidelity neural network potentials. Preprint at https://arxiv.org/abs/2412.06064 (2024).
Smith, J. S. et al. Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nat. Commun. 10, 2903 (2019).
Google Scholar
Buterez, D., Janet, J. P., Kiddle, S. J., Oglic, D. & Lió, P. Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting. Nat. Commun. 15, 1517 (2024).
Google Scholar
Allen, A. E. A. et al. Learning together: towards foundation models for machine learning interatomic potentials with meta-learning. NPJ Comput. Mater. 10, 154 (2024).
Google Scholar
Messerly, M. et al. Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need. Mach. Learn.: Sci. Technol. 6, 035066 (2025).
Zubatyuk, R., Smith, J. S., Leszczynski, J. & Isayev, O. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci. Adv. 5, eaav6490 (2019).
Google Scholar
Anstine, D. M., Zubatyuk, R. & Isayev, O. AIMNet2: a neural network potential to meet your neutral, charged, organic and elemental-organic needs. Chem. Sci. 16, 10228–10244 (2025).
Google Scholar
Yao, K., Herr, J. E., Toth, D. W., Mckintyre, R. & Parkhill, J. The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. Chem. Sci. 9, 2261–2269 (2018).
Google Scholar
Karwounopoulos, J. et al. Evaluation of machine learning/molecular mechanics end-state corrections with mechanical embedding to calculate relative protein-ligand binding free energies. J. Chem. Theory Comput. 21, 967–977 (2025).
Google Scholar
Levine, D. S. et al. The Open Molecules 2025 (OMol25) dataset, evaluations and models. Preprint at https://arxiv.org/abs/2505.08762 (2025).
Thölke, P. & Fabritiis, G. D. TorchMD-NET: equivariant transformers for neural network based molecular potentials. Preprint at https://arxiv.org/abs/2202.02541 (2022).
Vaswani, A. et al. Attention is all you need. In Proc. Advances in Neural Information Processing Systems, Vol. 30, 5998–6008 (Curran Associates, Inc., 2017).
Tay, Y., Dehghani, M., Bahri, D. & Metzler, D. Efficient transformers: a survey. ACM Comput. Surv. 55, 1–28 (2023).
Google Scholar
Frank, J. T., Unke, O. T. & Müller, K.-R. So3krates: equivariant attention for interactions on arbitrary length-scales in molecular systems. In Advances in Neural Information Processing Systems, Vol. 35, 29400–29413 (Curran Associates, Inc., 2022)
Qu, E. & Krishnapriyan, A. S. The importance of being scalable: improving the speed and accuracy of neural network interatomic potentials across chemical domains. In Advances in Neural Information Processing Systems, Vol. 37, 139030–139053 (Curran Associates, Inc., 2024).
Leimeroth, N., Erhard, L. C., Albe, K. & Rohrer, J. Machine-learning interatomic potentials from a users perspective: a comparison of accuracy, speed and data efficiency. Preprint at https://arxiv.org/abs/2505.02503 (2025).
Park, Y., Kim, J., Hwang, S. & Han, S. Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations. J. Chem. Theory Comput. 20, 4857–4868 (2024).
Google Scholar
Zubatyuk, R. et al. AQuaRef: machine learning accelerated quantum refinement of protein structures. Nat. Commun. 16, 9224 (2025).
Google Scholar
Accelerate Drug and Material Discovery with New Math Library NVIDIA cuEquivariance. NVIDIA Technical Blog (18 November 2024); https://developer.nvidia.com/blog/accelerate-drug-and-material-discovery-with-new-math-library-nvidia-cuequivariance/
Amin, I., Raja, S. & Krishnapriyan, A. Towards fast, specialized machine learning force fields: distilling foundation models via energy hessians. Preprint at https://arxiv.org/abs/2501.09009 (2025).
Matin, S. et al. Ensemble knowledge distillation for machine learning interatomic potentials. Preprint at https://arxiv.org/abs/2503.14293 (2025).
Senn, H. M. & Thiel, W. QM/MM methods for biomolecular systems. Angew. Chem. Int. Ed. 48, 1198–1229 (2009).
Google Scholar
Lahey, S.-L. J. & Rowley, C. N. Simulating protein-ligand binding with neural network potentials. Chem. Sci. 11, 2362–2368 (2020).
Google Scholar
Gastegger, M., Schütt, K. T. & Müller, K.-R. Machine learning of solvent effects on molecular spectra and reactions. Chem. Sci. 12, 11473–11483 (2021).
Google Scholar
Sabanés Zariquiey, F. et al. Enhancing protein-ligand binding affinity predictions using neural network potentials. J. Chem. Inf. Model. 64, 1481–1485 (2024).
Google Scholar
Nováček, M. & Řezáč, J. PM6-ML: the synergy of semiempirical quantum chemistry and machine learning transformed into a practical computational method. J. Chem. Theory Comput. 21, 678–690 (2025).
Google Scholar
Valsson, Í et al. Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data. Commun. Chem. 8, 41 (2025).
Google Scholar
Galvelis, R., Doerr, S., Damas, J. M., Harvey, M. J. & De Fabritiis, G. A scalable molecular force field parameterization method based on density functional theory and quantum-level machine learning. J. Chem. Inf. Model. 59, 3485–3493 (2019).
Google Scholar
Tayfuroglu, O., Zengin, I. N., Koca, M. S. & Kocak, A. DeepConf: leveraging ANI-ML potentials for exploring local minima with application to bioactive conformations. J. Chem. Inf. Model. 65, 2818–2833 (2025).
Google Scholar
Baillif, B., Cole, J., Giangreco, I., McCabe, P. & Bender, A. Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations. J. Cheminformatics 15, 124 (2023).
Google Scholar
Pan, X. et al. MolTaut: a tool for the rapid generation of favorable tautomer in aqueous solution. J. Chem. Inf. Model. 63, 1833–1840 (2023).
Google Scholar
Han, F. et al. Distribution of bound conformations in conformational ensembles for X-ray ligands predicted by the ANI-2X machine learning potential. J. Chem. Inf. Model. 63, 6608–6618 (2023).
Google Scholar
Berenger, F. & Tsuda, K. An ANI-2 enabled open-source protocol to estimate ligand strain after docking. J. Comput. Chem. 46, e27478 (2025).
Google Scholar
Maestro (Schrödinger); https://www.schrodinger.com/platform/products/maestro/
Accelerate your chemistry & materials research (SCM); https://www.scm.com/
BIOVIA (Dassault Systèmes); https://www.3ds.com/products/biovia
Dral, P. O. et al. MLatom 3: a platform for machine learning-enhanced computational chemistry simulations and workflows. J. Chem. Theory Comput. 20, 1193–1213 (2024).
Google Scholar
Zhao, Q. et al. Comprehensive exploration of graphically defined reaction spaces. Sci. Data 10, 145 (2023).
Google Scholar
Liu, Z., Moroz, Y. S. & Isayev, O. The challenge of balancing model sensitivity and robustness in predicting yields: a benchmarking study of amide coupling reactions. Chem. Sci. 14, 10835–10846 (2023).
Google Scholar
Revolutionizing AI-Driven Material Discovery Using NVIDIA ALCHEMI. NVIDIA Technical Blog (18 November 2025); https://developer.nvidia.com/blog/revolutionizing-ai-driven-material-discovery-using-nvidia-alchemi
Spotlight: Shell Accelerates CO2 Storage Modeling 100,000x Using NVIDIA PhysicsNeMo. NVIDIA Technical Blog (9 September 2024); https://developer.nvidia.com/blog/spotlight-shell-accelerates-co2-storage-modeling-100000x-using-nvidia-physicsnemo
St. John, P. S. et al. BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery. Preprint at https://arxiv.org/abs/2411.10548 (2024).
Boiko, D. A., Reschützegger, T., Sanchez-Lengeling, B., Blau, S. M. & Gomes, G. Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs. Nat. Mach. Intell. 7, 771–781 (2025).
Google Scholar
Qiao, Z., Welborn, M., Anandkumar, A., Manby, F. R. & Miller, T. F. III OrbNet: deep learning for quantum chemistry using symmetry-adapted atomic-orbital features. J. Chem. Phys. 153, 124111 (2020).
Google Scholar
Qiao, Z. et al. Informing geometric deep learning with electronic interactions to accelerate quantum chemistry. Proc. Natl Acad. Sci. USA 119, e2205221119 (2022).
Google Scholar
Kang, B. S. et al. OrbitAll: a unified quantum mechanical representation deep learning framework for all molecular systems. Preprint at https://arxiv.org/abs/2507.03853 (2025).
Kabylda, A. et al. Molecular simulations with a pretrained neural network and universal pairwise force fields. J. Am. Chem. Soc. 147, 33723–33734 (2025).
Google Scholar
Releases · ACEsuit/mace. GitHub https://github.com/ACEsuit/mace/releases (accessed 17 September 2025).
Unke, O. T. et al. SpookyNet: learning force fields with electronic degrees of freedom and nonlocal effects. Nat. Commun. 12, 7273 (2021).
Google Scholar
Kalita, B. et al. AIMNet2-NSE: a transferable reactive neural network potential for open-shell chemistry. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2025-kdg6n (2025).
Zubatyuk, R., Smith, J. S., Nebgen, B. T., Tretiak, S. & Isayev, O. Teaching a neural network to attach and detach electrons from molecules. Nat. Commun. 12, 4870 (2021).
Google Scholar
Gelžinytė, E., Öeren, M., Segall, M. D. & Csányi, G. Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecules. J. Chem. Theory Comput. 20, 164–177 (2024).
Google Scholar
Yang, Y., Zhang, S., Ranasinghe, K. D., Isayev, O. & Roitberg, A. E. Machine learning of reactive potentials. Annu. Rev. Phys. Chem. 75, 371–395 (2024).
Google Scholar
Wang, L.-P. et al. Discovering chemistry with an ab initio nanoreactor. Nat. Chem. 6, 1044–1048 (2014).
Google Scholar
Chen, B. W. J., Zhang, X. & Zhang, J. Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials. Chem. Sci. 14, 8338–8354 (2023).
Google Scholar
Unke, O. T. & Meuwly, M. PhysNet: a neural network for predicting energies, forces, dipole moments and partial charges. J. Chem. Theory Comput. 15, 3678–3693 (2019).
Google Scholar
Yu, H., Xu, Z., Qian, X., Qian, X. & Ji, S. Efficient and equivariant graph networks for predicting quantum Hamiltonian. In Proceedings of the 40th International Conference on Machine Learning, Vol. 202, 40412–40424 (PMLR, 2023).
Luise, G. et al. Accurate and scalable exchange-correlation with deep learning. Preprint at https://arxiv.org/abs/2506.14665 (2025).
Froitzheim, T., Müller, M., Hansen, A. & Grimme, S. G-xTB: a general-purpose extended tight-binding electronic structure method for the elements H to Lr (Z = 1–103). Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2025-bjxvt (2025).
Bannwarth, C., Ehlert, S. & Grimme, S. GFN2-xTB—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions. J. Chem. Theory Comput. 15, 1652–1671 (2019).
Google Scholar
Choi, J., Nam, G., Choi, J. & Jung, Y. A perspective on foundation models in chemistry. JACS Au 5, 1499–1518 (2025).
Google Scholar
Eastman, P., Pritchard, B. P., Chodera, J. D. & Markland, T. E. Nutmeg and SPICE: models and data for biomolecular machine learning. J. Chem. Theory Comput. 20, 8583–8593 (2024).
Google Scholar
Schreiner, M., Bhowmik, A., Vegge, T., Busk, J. & Winther, O. Transition1x—a dataset for building generalizable reactive machine learning potentials. Sci. Data 9, 779 (2022).
Google Scholar
Plé, T. et al. A foundation model for accurate atomistic simulations in drug design. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2025-f1hgn-v3 (2025).
Chiang, Y. et al. MLIP Arena: advancing fairness and transparency in machine learning interatomic potentials through an open and accessible benchmark platform. Preprint at https://arxiv.org/abs/2509.20630 (2025).
FAIR Chemistry Leaderboard—a Hugging Face Space by Facebook https://huggingface.co/spaces/facebook/fairchem_leaderboard (accessed 17 September 2025).
Schaaf, L., Fako, E., De, S., Schäfer, A. & Csányi, G. Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields. npj Comput Mater 9, 180 (2023).
Google Scholar
Kouw, W. M. & Loog, M. An introduction to domain adaptation and transfer learning. Preprint at https://arxiv.org/abs/1812.11806 (2019).
Pfeiffer, J., Ruder, S., Vulić, I. & Ponti, E. M. Modular deep learning. Preprint at https://arxiv.org/abs/2302.11529 (2024).
Chen, X., Wang, S., Fu, B., Long, M. & Wang, J. Catastrophic forgetting meets negative transfer: batch spectral shrinkage for safe transfer learning. In Proc. Advances in Neural Information Processing Systems, Vol. 32, 1908–1918 (Curran Associates, Inc., 2019).
Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Proc. Natl Acad. Sci. USA 114, 3521–3526 (2017).
Google Scholar
Hüllermeier, E. & Waegeman, W. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110, 457–506 (2021).
Google Scholar
Kulichenko, M. et al. Data generation for machine learning interatomic potentials and beyond. Chem. Rev. 124, 13681–13714 (2024).
Google Scholar
Kulichenko, M. et al. Uncertainty-driven dynamics for active learning of interatomic potentials. Nat. Comput. Sci. 3, 230–239 (2023).
Google Scholar
Glavatskikh, M., Leguy, J., Hunault, G., Cauchy, T. & Da Mota, B. Dataset’s chemical diversity limits the generalizability of machine learning predictions. J. Cheminformatics 11, 69 (2019).
Google Scholar
Korth, M. & Grimme, S. Mindless’ DFT benchmarking. J. Chem. Theory Comput. 5, 993–1003 (2009).
Google Scholar
Gould, T., Chan, B., Dale, S. G. & Vuckovic, S. Identifying and embedding transferability in data-driven representations of chemical space. Chem. Sci. 15, 11122–11133 (2024).
Google Scholar
Bolhuis, P. G., Chandler, D., Dellago, C. & Geissler, P. L. TRANSITION PATH SAMPLING: throwing ropes over rough mountain passes, in the dark. Annu. Rev. Phys. Chem. 53, 291–318 (2002).
Google Scholar
Jung, H., Okazaki, K. & Hummer, G. Transition path sampling of rare events by shooting from the top. J. Chem. Phys. 147, 152716 (2017).
Google Scholar
Anstine, D. M. et al. AIMNet2-Rxn: a machine learned potential for generalized reaction modeling on a millions-of-pathways scale. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv-2025-hpdmg (2025).
Poongavanam, V. et al. Conformational sampling of macrocyclic drugs in different environments: can we find the relevant conformations?. ACS Omega 3, 11742–11757 (2018).
Google Scholar
Witek, J. et al. Kinetic models of cyclosporin A in polar and apolar environments reveal multiple congruent conformational states. J. Chem. Inf. Model. 56, 1547–1562 (2016).
Google Scholar
Kamenik, A. S., Lessel, U., Fuchs, J. E., Fox, T. & Liedl, K. R. Peptidic macrocycles – conformational sampling and thermodynamic characterization. J. Chem. Inf. Model. 58, 982–992 (2018).
Google Scholar
Shrestha, U. R., Smith, J. C. & Petridis, L. Full structural ensembles of intrinsically disordered proteins from unbiased molecular dynamics simulations. Commun. Biol. 4, 243 (2021).
Google Scholar
Potoyan, D. A. & Papoian, G. A. Energy landscape analyses of disordered histone tails reveal special organization of their conformational dynamics. J. Am. Chem. Soc. 133, 7405–7415 (2011).
Google Scholar
Appadurai, R., Nagesh, J. & Srivastava, A. High resolution ensemble description of metamorphic and intrinsically disordered proteins using an efficient hybrid parallel tempering scheme. Nat. Commun. 12, 958 (2021).
Google Scholar
Morrow, J. D., Gardner, J. L. A. & Deringer, V. L. How to validate machine-learned interatomic potentials. J. Chem. Phys. 158, 121501 (2023).
Google Scholar
Vassilev-Galindo, V., Fonseca, G., Poltavsky, I. & Tkatchenko, A. Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules. J. Chem. Phys. 154, 094119 (2021).
Google Scholar
Xin, H., Kitchin, J. R. & Kulik, H. J. Towards agentic science for advancing scientific discovery. Nat. Mach. Intell. 7, 1373–1375 (2025).
Google Scholar
Aspuru-Guzik, A. & Bernales, V. The rise of agents: computational chemistry is ready for (R)evolution. Polyhedron 281, 117707 (2025).
Google Scholar
